Technological development of key domains in electric vehicles: Improvement rates, technology trajectories and key assignees

Applied Energy 260 (2020) 114264

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Technological development of key domains in electric vehicles: Improvement rates, technology trajectories and key assignees

T

Sida Fenga,b, , Christopher L. Mageea,c ⁎

a

SUTD-MIT International Design Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA School of Economics and Management, China University of Geosciences, Beijing 100083, China c Institute for Data, Systems and Society, Massachusetts Institute of Technology, Cambridge, MA 02139, USA b

HIGHLIGHTS

the electric vehicle into four domains to study technology development. • Decompose technology improvement rates for electric motors and batteries are lower. • The hot and emerging topics of key subdomains are detected by technology trajectories. • The players in critical subdomains are found, such as Toyota, Honda and Panasonic. • Key • Key players found from the innovation view are also key players from the market view. ARTICLE INFO

ABSTRACT

Keywords: Electric vehicle Technology improvement rates Technology trajectories Patent analysis

Technology innovation in electric vehicles is of significant interest to researchers, companies and policy-makers of many countries. Electric vehicles integrate various kinds of distinct technologies and decomposing the overall electric vehicle field into several key domains allows determination of more detailed, valuable information. To provide both broader and more detailed information about technology development in the EV field, unlike most previous studies on electric vehicle innovation which analyzed this field as a whole, this research decomposed the electric vehicle field into domains, which are power electronics, battery, electric motor as well as charging and discharging subdomains and then further extracted the subdomains. Furthermore, In addition, the improvement rates, technology trajectories and major patent assignees in these domains and key subdomains are determined using patents extracted for each domain from the US patent system. The main findings are: (1) The estimated rates of performance improvement per year are 18.3% for power electronics, 7.7% for electric motors, 23.8% for charging and discharging and 11.7% for batteries. The relatively lower improvement rate for electric motors and batteries suggests their potential to hinder the popularization of electric vehicles. Besides, as for the subdomains, the relatively higher technology improvement rate of lithium-ion battery or permanent magnet motor in its domain supports the current trend of battery or motor type quantitively from a patent analysis view. A possible implication for the policy makers encouraging EV development is to issue more incentive plans for innovations in the battery and electric motor domains, especially for lithium-ion battery and permanent magnet motor. (2) The technology trajectories depict the development of four critical subdomains over time, which quantitively proves the focuses and emerging topics of the subdomains and thereby provide guidance to research topic selection. For example, the silicon negative electrode is a promising topic in the subdomain of lithium-ion battery. (3) The key players in the four critical subdomains appear to be Toyota and Honda in hybrid power electronics, E-One Moli Energy Corp in lithium-ion batteries, Panasonic in Permanent Magnet motors and Toyota in discharging. The key players found by the main path method from the view of innovation are also important players in EV from the market view. Other market participants should pay more attention to the adjustment of business strategy of these companies to monitor the market, and make effort to invent important EV related technologies.

⁎

Corresponding author at: SUTD-MIT International Design Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. E-mail address: [email protected] (S. Feng).

https://doi.org/10.1016/j.apenergy.2019.114264 Received 19 June 2019; Received in revised form 10 November 2019; Accepted 26 November 2019 0306-2619/ © 2019 Elsevier Ltd. All rights reserved.

Applied Energy 260 (2020) 114264

S. Feng and C.L. Magee

1. Introduction

comparing the patent data of internal combustion engine vehicles with different types of electric vehicles [42]. Others focus on the concept of technology convergence in the EV field. Wu et al. analyzed the technology convergence of Chinese EV patents [43]. Aaldering et al. investigated the patents related to alternative powertrains to establish technology interaction relations among patent classifications by network analysis as well as the potential relations by link prediction [44]. The studies mentioned above contribute to understanding the innovation in EV field, while there are three points where this study is different, which shows the originality of this study. First, prior researchers tend to consider the entire EV field instead of taking the distinct features of domains in the EV field into consideration. Second, none of the prior studies attempt to quantitatively estimate the rates of performance improvement in the key EV sub-domains which is arguably quite important in future success. Third, there is little research about the technology trajectories in the electric vehicle field and in the key domains. Therefore, to provide both broader and more detailed information about technology development in the EV field, in this study, the field is broken into several domains and subdomains, then the technology improvement rates of subdomains are compared and the technology trajectories of the critical subdomains are determined. Electric vehicles are like most products in that they integrate several technologies. Since such technologies are significantly different from one another, decomposing the overall EV field into several domains allows for more detailed and valuable information about the direction and status of technology innovation in this overall field. An electric vehicle can be regarded as a system with several subsystems including energy storage, electric propulsion, body, chassis and auxiliaries [45]. Each subsystem involves different components. Many of these subsystems and components are essentially the same in vehicles powered by non-electrical means and thus are not crucial to the relative capability of electric vehicles. However, the battery and charging subsystems are unique and important to EVs as well as the electric motor (EM) and power electronics (PE) in the electric propulsion subsystem. These four are the critical technologies for EVs because these components are experiencing major changes along the EV development process [46–50]. Batteries are the clear leader in energy storage system [46]. Charging supplies energy to the battery and it is often considered along with discharging of the battery, because high rate and safe charging and discharging are essential to EV batteries [51–53]. Hence, “Charging and Discharging” (Charging &Dis) is selected as one of the four domains. The electric motor converts the electrical energy from the battery into mechanical energy. Power electronics involves electronic control including power conversion and other control functions essential to the transportation function [54]. This paper explores each of these four domains to understand them individually, to look for interactions among them and to look more deeply within each of them. Overall, innovation in EVs will be assessed by analysis-particularly using the patent system of these four domains. This paper utilizes some recent developments in the assessment of the improvement of technology performance. After Gordon Moore proposed what came to be called Moore’s law for the semiconductor industry [55], the exponential improvement of technology performance over time, or more simply a constant % improvement per year, was also found in various other domains [56–64]. It is difficult and sometimes not possible to empirically estimate the improvement rate in a specific domain people wish to know about because collecting empirical data is challenging and some of the essential data has not been reported anywhere for confidentiality and other reasons. To address this problem, prior research has shown how to estimate the improvement rate of a technology domain from patent sets representative of the domain [65]. More recently, Triulzi et al. [66] demonstrated that the normalized centrality based on the patent citation network is the most reliable index to estimate technology improvement rate from patent sets. In this paper, this index is applied to estimate and compare the technology improvement rate of each domain.

Due to the advantages of low energy consumption and pollution, electric vehicles (EV) have the potential to contribute to de-carbonization of transportation and the emergence of low-carbon cities thus have become one of the development trends of interest in the automotive industry [1–3]. Nonetheless, the future success of the EV industry is highly dependent on technology innovation [4,5]. Policymakers in many countries such as Sweden, China, Malaysia and Korea have paid much attention to EV technology innovation and issued policies to encourage the technological innovation of EVs [6–12]. It may be an understatement that technological innovation in EV field is a topic of much interest. The environmental impact is a hotspot for EV. Researchers use distinct methods to evaluate the environmental effect of EV in various regions. Many researchers concluded that EV can contribute to the greenhouse gas emission reduction in many regions [13–17]. For example, Hawkins et al. found that in Europe, EVs offer a 10% to 24% decrease in global warming potential compared with conventional diesel vehicles [14]. Onat et al. stated that all-electric vehicle types could contribute to lower global warming in Qatar [17]. While some researchers hold the idea that EV cannot help to reduce greenhouse gas emission [18–20]. It is mostly because that the source of electricity in some regions produces a lot of greenhouse gas, which makes the popularity of EV seem to be environmental unfriendly [20–23]. While the electric vehicle itself still could contribute to the slowdown of global warming if the electricity generation system is adjusted to be renewable and environmental friendly [24–27]. Therefore, from the environmental viewpoint, EV is still a promising trend for de-carbonization of transportation and can contribute to the sustainable development. Many studies from the economic view are related to the purchasing willingness of the customers because the EV has a relatively high cost compared with the conventional vehicle [28,29]. Some researchers pointed that the economic feasibility of EVs can only be attained through government incentives or considering revenue from commercial activity. On the one hand, subsidies do help to promote consumers’ adoption towards EV [28,30]. While in the long run, the high monetary subsidies are not sustainable [31]. Researchers are trying to find ways of promoting the adoption without subsidies. Nian et al. proposed a novel business model improving the adoption of EV when there is no policy support [29]. Ma et al. found some effective alternative incentives to replace the subsidies [31].On the other hand, as for the commercial activity, some researchers are endeavoring to find ways of obtaining revenue by EV for customers, such as vehicle-to-grid technology [32]. Noel et al. found that the benefit of vehicle-to-grid is not well-understood by many customers and customers strongly value driving range and recharging time [33]. In conclusion, the high cost issue and other drawbacks such as driving range and recharging time are hindering the economic value of EV, while these can be solved by the improvement of related technologies [34]. Previous researchers have studied EV patents, a repository of invention of technology [35–37], to study EV innovation. Some investigated the macro evolution trend of electric vehicle innovation. Zhang et al. presented a technology road map for electric vehicles to show the general evolution of EV related technologies over time [38]. Sun analyzed the evolution of China’s electric vehicle patents from different aspects including the patent class distribution, geographical distribution and the cooperation among assignees [39]. Golembiewski et al. investigated battery technology trends for electric vehicles by analyzing the patent assignees in the battery value chain [40]. Some researchers have focused on comparing the technology development between traditional power source vehicles and alternative energy vehicles. Borgstedt et al. compared the patent holders of battery electric vehicles, hybrid electric vehicles and fuel cell electric vehicles with internal combustion engine vehicles [41]. Sick discussed whether there is a so-called “sailing ship effect” in the automotive industry by 2

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Technology trajectories are a conceptual basis for understanding technology evolution [67–69]. Main path analysis based on patent citation networks is emerging as a commonly used method to empirically determine technology trajectories. It was first proposed to identify the knowledge trajectory in DNA field based on the publication citation network by Hummon and Doreian [70]. Then this method and its modifications based on patent citation network were applied to explore technology trajectories in various domains such as medicine [71], 3D printing [72] and waste management [73]. This study uses a recently developed main path analysis method, proposed by Park and Magee [74], to detect the technology trajectories in the EV field. This method can identify simpler and non-singular paths as well as more important patents and it also has been successfully applied to several technological domains [75–77]. Section 2 introduces the data and methods used in the paper detailing obtaining the key patent sets for all four domains of interest, which are power electronics, battery, electric motor as well as charging and discharging,. Section 3 gives performance improvement results for each domain as well as technological trajectories and key assignees of patents for critical subdomains. Section 4 discusses the results and makes the conclusions.

Fig. 1. Percentage of overlapping patents between the four domains in EV field.

the power electronics and charging& discharging are limited to the EV field. The reason is that the ranges of these two are too broad and the technologies in other fields are very different from those in EV. On the other hand, the battery and electric motor domains are not limited to EV field because in these cases, the technologies applied in different contexts are relatively highly related and can also contribute to the technology development in batteries and electric motors used in EV. Fig. 1 demonstrates the percentage of overlap patents between each pair of the four domains. For all the blocks off the diagonal, the color of each block indicates the overlap degree of the focal domain in the Y-line compared with the other domain in the X-line. The warmer the color is, the larger overlap degree is of the focal domain compared with the other one, and vice versa. The value in the block is the specific percentage value of overlap. As for the blocks on the diagonal, the values are the proportion of unique patents in the focal domain. All the four domains have over 85% unique patents, and over 99% of the patents in battery and electric motor domains are unique. Battery is highly related with “charging and discharging” domain and to avoid large cross-cutting information, it is excluded from the battery dataset by excluding the related patent classification during the data collection process. As a result, it can be shown that there is almost little overlap among the four datasets of the domains in this study, indicating that the datasets obtained in this study to represent the domains are relatively independent.

2. Data and methods 2.1. Data collection 2.1.1. Domain patent sets The U.S. granted patents are used in this study to explore the technological development in EV from a global view. The reasons are as follows: First, the U.S. patent system had a wide coverage of the countries [78]. Because of America’s large market size and powerhouse position in technologies development, most of the valuable patents in other countries also apply for patents in the U.S. to get protected in the U.S. market and protected by the law in the U.S [79,80]. Second, the U.S. patent system is the most representative among many patent systems considering the amount, variety and diverse sources of information [81].Therefore, U.S. patents can be regarded as the epitome of global technologies development [80,82–84].Establishing a relevant and complete patent data set for each domain is the basis of analyzing the four most important technology domains in EV field. The classification overlap method (COM) developed by Benson and Magee [85,86] is applied. This method can obtain a relatively relevant and complete patent dataset of one specific domain and has been successfully used in several studies [59,61,65,75,76,87,88].This method uses the combination of International Patent Classification(IPC) and United States Patent Classification (UPC) to collect the data. The details of the method can be found in Refs. [85] and [86]. In this research, the U.S. patent data related to the four domains ranging from 1976.1.1 to 2015.6.30 from the Patsnap database [89] are obtained. The UPC is replaced by Cooperative Patent Classification from mid-2015 and it is needed for application of COM, so the end time of the data is 2015.6.30. The overlap of classifications, relevancy and number of patents for each domain are shown in Table 1. It is noted that in the battery domain, patents which contain UPC 320 are deleted to distinguish from the domain of “Charging & Discharging”. Moreover,

2.1.2. Subdomain decomposition Based on the four domain datasets, the subdomains of each domain are extracted from the corresponding domain datasets by patent subclasses to get a further comparison and understanding of the technology development in the EV field. In details, there are two steps when extracting a subdomain from a domain. An example is shown as below: When extracting the subdomain of nickel battery from the domain of battery, there are two steps. First, the subclass patent codes which can represent the subdomain of nickel battery are found. They are IPC H01M4/32 which means “Nickel oxide or hydroxide electrodes” and UPC 429/223 of which the definition is “Nickel component is active

Table 1 The selection of domains. Domain name

Number of patents

Relevancy

Patent search terms

Power electronics [90,91] Charging&Discharging [92] Battery [46,93] Electric motor [94,95]

1932 1102 21,989 19,689

90% 97.3% 97.6% 91.3%

B60L&(318 or 361) B60L&320 H01M&429 NOT 320 H02K&310

Note: In battery domain, the patents just about fuel cell is deleted by removing the ones only contain “fuel cell” but no “battery” in the title and abstract. 3

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material”. Second, the patents which contain the IPC of H01M4/32 and the UPC of 429/223 are extracted from the domain of battery as the dataset of the subdomain of nickel battery. The results are double checked by reading the titles and abstracts of patents. Table 2 shows the details of the subdomains including the specific IPC and UPC sub-classes for each sub-domain extraction. The first column is the subdomains and the second column is the domain that this subdomain belongs to. The selection rules of subdomains are illustrated in the third column and the last column is the number of patents in the subdomain. It is necessary to note that Table 1 is about the domains and Table 2 offers information about subdomains. Each subdomain is extracted from the domain by IPC or UPC subclass codes which could represent the subdomain. Therefore, the whole domains might not be constituted just by the subdomains, and thereby the summation of the subdomains in Table 2 may not be equal to those in Table 1. The power electronic technologies of hybrid EV and full EV are divided into two subdomains: one is the technologies related to hybrid EV control; the other is the remaining patents that do not refer specifically to hybrid EV, which is referred to as “power electronics-other”. The two subdomains of charging and discharging are extracted from “charging and discharging” domain. Some of the patent classifications in the patents are about the “charging or discharging”. For example, UPC 320/124 represents “sequential charging or discharging of batteries or cells”. The patents might be about charging or discharging, so these patents are not included in the subdomain dataset. While the codes representing charging and discharging are included in both of the two subdomains. As for the battery, the subdomains of lead acid, nickel and lithium-ion batteries are obtained by selecting the related patent classifications. Moreover, the most common used motor types in EV are induction motors and permanent magnet (PM) motors [95], so the patents related to these two kinds of motors are selected from the electric motor domain by choice of the appropriate sub-classes shown in.

procedure to overcome the systemic biases in the patent system over time and patent classes. A z-score of the mean centrality can be obtained based on the randomization procedure and the rank percentile of the z-score by the patent filing year is the final normalized mean centrality of patent i. The formulas are as follows and further details are given in Ref. [66]:

z (averageSPNPcitedi, t 1) =

averageSPNPcitedi, t

1

=

1 LBWD, i

SPNPj, t j citedBWD, i

1

E (averageSPNPcitedi, t 1) (3)

MeanCentralityCitedZRPi = RankPercentile (z (averageSPNPcitedi, t 1)) (4) As a result, the improvement rate k of a domain can be estimated by Eq. (5) [66].

estimatedkj = e

5.01885 + 6.15987 MeanCentralityCitedZRPj

2 j

e2

(5)

where estimatedkj is the estimated improvement rate in domain j; j is the standard error of the log ofkj , which is obtained when estimating the log(k) by 30 domains that have the observed improvement rate(k)1. This is the j that is used as correction factor to move to a linear estimate of k..MeanCentralityCitedZRPj is the mean centrality of patents in domain j. The difference of improvement rates between two domains or subdomains are compared by the following steps: First, regarding the distribution of log(k) as a normal distribution, the estimated probability distribution functions of log(k) are obtained by the predicted mean and standard error. Second, a new variable w is calculated from the difference of the two probability distribution functions of two domains being compared. Third, the cumulative density function of w > 0 is obtained and it is the probability that one domain or subdomain is improving faster than the other [66,87]. 2.3. Main path analysis Main path analysis is used to detect the technological trajectory, which could extract the development trend along time of a domain. This study applied a main path analysis method named genetic backward-forward path analysis [75]. This method identifies simpler and non-singular paths as well as more important patents and has been successfully applied to several technological domains [75–77]. Genetic backward-forward path analysis identifies main paths by a backward and forward searching from the high persistence patents (HPPs). The main path analysis is computed in MATLAB. The process is listed as follows and more details can be found in Ref. [75].

The annual improvement rate of a domain is estimated from the separate patent sets. Triulzi et al. [66] have shown how a normalized centrality index in the patent citation network of the whole U.S. patent system can be used to estimate the improvement rate. The centrality is based on “search path node pairs” proposed by Hummon and Doreian ([70]. This method calculates the centrality of a focal patent by counting the paths going through the focal patent which connect two other patents in the citation direction. A high centrality patent acts as an intermediate to convey knowledge from the upstream patents to the downstream ones and thereby has a high contribution to the technology improvement. The “search path node pairs” method is calculated as follows [66]:

(outgoing pathsi, t + 1)

1

averageSPNPcitedi, t 1

2.2. Estimating the improvement rate(k)

SPNPi, t = (incoming pathsi + 1)

averageSPNPcitedi, t

1) Patent citation network construction The citation network of the patents in the domain are constructed disregarding citations to patents outside the domain. The nodes are the patents in the domain and the edges are the citation relations between the patents. The direction of the edge indicating the knowledge flow is from the cited patent to the citing one.

(1) (2)

whereSPNPi, t is the SPNP centrality of patent i in time t. The incoming paths is the number of all the patents that could reach patent i from a backward direction. It does not change over time because the backward citations of patent i is unchangeable along time. While as the number of patents which are reachable by patent i from a forward direction, the outgoing paths changes over time because a patent may be cited by new patents. The SPNPi, t is calculated by multiplying its number of incoming and outgoing paths both augmented by one to ensure the value is not zero. averageSPNPcitedi, t 1 is the average SPNP of the patents cited by patent i in time t-1. t is the filing year of patent i. LBWD, i is the number of backward citations made by patent i. This index is normalized by Triulzi et al. [66] using randomization

2) Knowledge persistence measurement Knowledge persistence of each patent is measured using the genetic knowledge persistence algorithm [96]. The main idea is that the patent inherits the knowledge from a cited patent, so the persistence of one patent is calculated by ordering the citation network into layers by citation sequence and tracing all its connected endpoints. The formula 1 More details could be found in Ref [66] Triulzi G AJ, Magee CL. Estimating Technology Performance Improvement Rates by Mining Patent Data. SSRNid2987588,2018.

4

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Table 2 Extraction of subdomain dataset from the domains. Subdomain

Belongs to Domain

Patent subclass selection rules

Number of patents

Power electronics-hybrid [90,91]

Power electronics

312

Power electronics-other [90,91] Charging [92] Discharging [92] Lead-acid battery [46,93]

Power electronics Charging &discharging Charging &discharging Battery

Nickel battery [46,93] Lithium-ion battery [46,93] Induction motor [94,95]

Battery Battery Electric motor

Permanent magnet motor [94,95]

Electric motor

IPC: B60K 6/42; B60K 6/44; B60K 6/442; B60K 6/445; B60K 6/448; B60K 6/46; B60K 6/48; B60K 6/485; B60W20; B62M23/02 The rest of the patents in this domain. UPC:320/100–320/111, 320/113–115, 320/119, 320/122, 320/123, 320/137–320/165 UPC:320/118; 320/127–320/131; 320/135; 320/136 IPC:H01M 2/28; H01M 4/14; H01M 4/68; H01M 10/06; H01M 4/73; H01M 4/82; UPC:429/ 225; 429/227; 429/228; 429/231 IPC:H01M 4/32; UPC: 429/223 IPC: H01M10/052; H01M10/0525 IPC: H02K44/06; H02K17/00; H02K17/02; H02K17/04; H02K17/12; H02K17/16; H02K17/ 22; H02K17/26; H02K17/28; H02K17/30; H02K 17/32; H02K 17/34; UPC: 310/90.5; 310/166; 310/172 IPC: H02K 1/17, H02K 1/27; UPC:310/152; 310/153; 310/154; 310/155; 310/156

1620 865 202 1187 391 4556 1296 3167

Note: In the “selection rules”, if the patent contains one of the mentioned subclasses, it is classified into this subdomain.

of persistence measurement is as follows [75]: n

mi

KPA = i=1 j =1

lj 1 k=1

1 BC (Pijk )

function of w > 0. The warmer the color is, the higher the probability is. Among the four domains, Charging&Discharging has the highest estimated improvement rate of 23.8%. It is 1.4 times (0.57/0.41) as likely to improve faster than Power Electronics (PE) than that PE improves faster than Charging&Discharging, and it is 4.4 (0.8/0.18) times more likely to have a faster improvement rate than Electric Motors (EM) than the opposite and it is 2.77 (0.72/0.26) times more likely to improve faster than the battery domain than the opposite. At the level of subdomains within each domain, the probability that the improvement rate of PE-hybrid is faster than PE-other is 3.1 times (0.74/0.24) than the opposite. PE-hybrid is also 1.33 times (0.72/0.54) more likely to have a higher improvement rate than the whole PE domain than the opposite. Moreover, PE-hybrid has the fastest estimated technology improvement of 38.5% compared with all the other domains and sub-domains. EM-PM has a faster improvement rate than the entire EM and the EM-induction motor patent sets. Discharging has the highest improvement rate in its domain. In the battery domain, the improvement rate of lithium-ion battery is the fastest and lead acid battery is the slowest. The probability that the improvement rate of lithium-ion battery is faster than lead acid battery is 3.6 times (0.76/ 0.21) than the probability that the improvement rate of lead acid battery is faster than the lithium-ion battery. To sum up, from the domain level, the improvement rates of battery and electric motor are relatively lower than those of power electronics and charging&discharging. As for the view of subdomain, power electronics of hybrid EV is the subdomain with the highest improvement rate. Besides, discharging, lithium-ion battery and permanent magnet motor have the highest estimated technology improvement rates in their own domains. The possible reasons are discussed in the discussion part Section 4.1

(6)

where KPA is the knowledge persistence of patent A; n is the number of patents in the last layer which have not been cited by other patents in this domain and have direct or indirect backward citation relation with patent A; mi is all backward citation paths from patent i to patent A; l j is the number of patents on backward path j from patent i to patent A; Pijk is the k-th patent on path j and BC (Pijk ) is the total number of backward citations of Pijk with no consideration of the layers before the layer that patent A belongs to. 3) Main path detection The persistence is normalized to a range from 0 to 1. The global persistence (GP) is normalized over the entire network and the local persistence (LP) over the layer. Then the cut-off value is set to get the higher persistence patents (HPP). The main paths are identified by searching the highest GP patents backward and forward from the HPP. The cut-off values of the other three subdomains are GP = 0.3 and LP = 0.8 following the recommendation in Ref. [74].The cut-off values of HPP in discharging are GP = 0.15 and LP = 0.15 due to the relatively low number of patents in this sub-domain. 3. Results 3.1. Improvement rate comparison among domains and sub-domains Fig. 2 shows the comparison of estimated improvement rates. Fig. 2(b) demonstrates the estimated improvement rates of the domains and subdomains. Fig. 2(a) is the probability density function of estimated technological improvement rate for each domain or subdomain in EV field. The improvement rate k is obtained from the estimation of log(k). Regarding the distribution of log(k) as a normal distribution, the estimated probability distribution functions of log(k) are obtained by the predicted mean and standard error, which is shown in Fig. 2(a). To compare the improvement rate of domains and subdomains, a new variable w is calculated by the difference of the two probability distribution functions of two domains being compared. The cumulative density function of w > 0 is obtained and it is the probability that one domain or subdomain is improving faster than the other [66,87].The results of the comparison of all pairs of domains and subdomains are summarized in Fig. 2(c). The domain or subdomain in the y-axis is the focal one. The other domain or subdomain is in the x-axis. The value in each block is the probability that the focal domain or subdomain improves faster than the other one, which is the cumulative density

3.2. Technology trajectories of subdomains with high improvement rates PE-hybrid, lithium-ion battery, PM motor and discharging have the highest improvement rates in their domains. This section reports and analyzes their technology trajectories to have a deeper understanding of their technology development as important examples in each domain. Figs. 3 through 7 give the patents arrayed over time that make up the main path for each of the four example sub-domains. The year indicates the publication year of the corresponding patent which is placed in position. There are two types of nodes regarding to the size. The larger nodes in each figure are the High Persistence Patents (HPP), and the smaller ones are the patents with highest GP in the layer found by forward and backward searching from HPP. The color shows the patent cluster determined by reading the titles and abstracts of the patents. The meaning of the cluster is listed above the main paths. The patent 5

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Fig. 2. The comparison of improvement rates.

numbers are shown on the left of the main paths which allows anyone to access these patents. Fig. 3 shows the main path determined for power electronics control in the hybrid EV sub-domain. The patents are classified into three groups: A (drive system), B (energy source control) and C (power control). Power electronics is the technology relevant to the power switching devices which is related to control system to drive electric motors [97]. In hybrid EV, power electronics technology plays a major role to improve the efficiency of drive system in driving range and fuel economy [98]. The task of PE technologies includes converters/inverters, control, power switching and integrated to any electronics devices [99]. The three clusters are consistence with the definition and tasks of PE technology mentioned above. Patents classified into the drive system are about the invention of a general drive system. Energy source control focuses on the power electronics related to switching or cooperation of multiple energy sources. For example, patent 49 is about switching between a motor-driven mode and an engine-driven mode. Power control focuses on the further details about adjusting power. For

instance, patent 21 is about generator output control to make the motor output adapt to the operating condition of the hybrid EV. Fig. 3 shows that B (energy source control) and C (power control) are the two main clusters from 1978 to 2015. The main path results indicate that the later developments did not involve new overall configurations but instead important improvements in control details. This is in accordance with the illustration in previous study that the detailed switching technologies are the technical challenges of power devices, such as switching loses during turn-on and turn-off, switching frequency of operating mode [98]. Therefore, the focus of PE technology in hybrid EV in the recent years is detailed technologies. The lithium-ion battery in the early days are focused on the application to computer, communication and consumer electronic market [100]. Later, the technology become more complicated to extend the application to additional markets of small devices such as medical devices, toys and lighting. In recent years, the market pull of companies and governments is acting on lithium-ion battery industry to achieve the goal of reducing the greenhouse gas emission by electric vehicles. 6

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Fig. 3. The main path of PE-hybrid.

While this requires significant improvements of this industry such as safety, higher capacity and lower price, which invokes the technology innovation of this industry [101]. Fig. 4 is the main path for lithium-ion batteries. There are two components in this path. The HPPs in the first path are mainly about non-liquid electrolyte, adhesives and separators. The main topic of the second component is the electrode. The result of these two components covers the main components of lithium-ion battery: electrolyte, separator and electrodes. In addition, the second component is much larger and more complex. This comforts for the reality that the hotspot of lithium-ion battery innovation is electrode [102]. The key to the success of novel and advanced rechargeable

batteries are the materials of electrodes [100].The technology improvement of electrodes can make a great contribution to overcoming the current challenges for lithium-ion battery in EV by improving the energy and power densities and lowering the cost [102,103]. To probe more deeply, the electrode patents were further analyzed as shown in Fig. 5. Cluster b, c and d are about the positive electrode consisting of lithium manganese oxide or lithium nickel oxide. They form the majority of the main path and are distributed through the entire time range. This is in accord with the current research and manufacturing activities that positive electrode materials draw more attention than the negative electrode [104]. Cluster e is related to the

Fig. 4. The main path of lithium-ion battery. 7

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Fig. 5. The main path of lithium-ion battery-electrode.

usage of carbon in the battery system. On the one hand, patent 60 and 77 are related to carbon coating. Carbon coating is an emerging topic in lithium-ion battery field because of its advantage of electrode protection and conductivity increase as well as improvement of the surface chemistry of the active material, resulting in the rate capability and cycle stability improvement of the electrodes [100,105]. On the other hand, patents 4, 5, 9, 10, 18, 21, 26 published from 1986 to 1999 are about carbon negative electrodes. Carbon is a traditionally and

commonly used material for the negative electrode. It is noted that cluster f is silicon usage in negative electrodes and these patents mainly occur from 2009 to 2015 in the technology trajectories. Compared to the traditional carbon negative electrode, which has about 372 mA h/g theoretical gravimetric capacity, silicon has been shown to have a much higher theoretical gravimetric capacity of about 4200 mA h/g and is regarded as a potentially effective replacement for carbon negative electrodes [100,106]. In recent years, researchers are making great

Fig. 6. The main path of PM motor. 8

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Fig. 7. The main path of discharging.

coulomb counting, monitoring of battery voltage, and more recently predictive algorithms [107], which includes both charging and discharging control on-board [108]. This indicates that the technologies related to discharging are commonly studied with charging. Moreover, this kind of technology belongs to the battery management system. From 1980 to 2000, D (Battery monitor) and G (Solar/fuel cell battery control system) are the main topics in discharging. From 2000 to 2015, A (Charging and discharging control), E (Battery module balancing) and H (Plural batteries controlling) are the dominant clusters in the main path. Cluster D (Battery monitor) and A (Charging and discharging control) are the main body of the main path and they have citation relations with each other by Patent 4 and Patent 11. It implies they are the main topics in this subdomain and they are relatively related. This is in consistence with the fact that battery parameters are usually monitored by the battery management system and the battery management system includes both charging and discharging process. Besides, cluster E (Battery module balancing) and H (Plural batteries controlling) are about the control of several batteries. This can reveal the characteristic of batteries in EV, which are individual modules and cells organized in series and parallel to constitute a battery pack [108].

efforts to invent negative electrodes consisting of silicon and this topic will apparently continue to be promising [101]. Characterized by their constant rotor magnetization, the PM motors have the highest efficiency [50,95]. PMs in the rotor induce high magnetic fields in the air gap, with no excitation currents, leading to high power density [95].The traction of EV requires high energy efficiency, so PM are considered to be the mostly preferred type of motor in EV [46]. The main path result of PM motors is shown in Fig. 6. The proportion of HPP is the highest among the four subdomains. Furthermore, the HPPs belonging to cluster A (PM rotor) occupy a large majority (83%) of the HPPs. This indicates that in the PM motor subdomain, inventions related to the PM rotor is the core topic from 1978 to 2015. This can be explained by the fact that PM motors are characterized by their constant rotor magnetization, which contributes to the high efficiency of PM motor by inducing high magnetic fields in the air gap without excitation currents [95]. In addition, Patent 51(US7385328) in 2008, originally assigned to Reliance Electric Technologies, LLC, appears to be a convergence of knowledge flows from many of the previous patents in the main path and it is also an HPP. This patent is about a structural design of PM motors with cogging reduction. In detail, “when the leading edge of each pole of the rotor aligns with a stator tooth the trailing edge of that pole is generally not aligned with a stator tooth”. This implies the high knowledge convergence between cluster A and B. In fact, there are many kinds of PM arrangements and thereby PM motors allow great flexibility in design [95]. This can explain why the overall PM motor design is the second large cluster and is closely associated with Cluster A(PM rotor). Cluster F(Axially asymmetric permanent magnet motor), initiated by Patent 51, is about the invention of a new kind of PM motor with axially- asymmetric rotors, which reduces the torque ripple. This is also a kind of new PM motor design based on the PM rotor. To sum up, the technology innovation of PM motor is concentrating on the invention of rotor. The safety, durability, and performance of batteries are highly dependent on how they are charged or discharged. Fig. 7 shows the main path in discharging. There are eight clusters and they are rather isolated from each other. They are all related to the battery management system. Battery management systems are electronic systems use multiple methods to estimate the electrical charge of the battery, including

3.3. Key players in subdomains with high improvement rates The assignees of HPPs are analyzed in this section to explore the key players in the four subdomains. Some of the assignees are subsidiaries of the parent company and the subsidiary corporation patents are treated as belonging to the parent companies. In each subdomain, the numbers of HPPs assigned to the companies including the subsidiaries of the companies are calculated and the parent companies which own more than one HPPs are listed in Table 3 in descending order. In the subdomain of hybrid power electronics, both Toyota and Honda have six patents. They are the companies with the most HPPs in this subdomain. It is shown in Fig. 3 that assigned to Honda, Patent 16, 26, 28, 32, 33 and 48 belong to the power control technology cluster, which emphasizes the detailed adjusting of power output. While the patents with high persistence assigned to Toyota are 12, 14, 18, 20, 22 and 36. Expect for 36, which is about power control, the rest are all about energy source control. As for the lithium-ion battery, E-One Moli 9

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in EV field. The detailed findings and further discussion are as follows.

Table 3 Rank of parent companies in each subdomain. PE-hybrid (34 HPPs) Parent company of the assignees Toyota Honda General Electric Company Renault Suzuki Motor Corporation

Number of patents 6 6 3 3 3

Lithium-ion battery (28HPPs) Parent company of the assignees E-One Moli Energy Panasonic Chinese Wanxiang Group Umicore Massachusetts Institute of Technology

Number of patents 5 3 3 2 2

PM motor (35 HPPs) Parent company of the assignees Panasonic Seiko Group General Electric Company Toyota Siemens Honeywell International Inc. United Technologies Corporation Kabushiki Kaisha Toshiba

Number of patents 5 4 3 2 2 2 2 2

Discharging (15HPPs) Parent company of the assignees Toyota

Number of patents 4

4.1. Improvement rate comparison The difference of improvement rates can be explained from different aspects by distinct theories [87]. From the comparison of improvement rates found in the present study, there are some interesting findings. In this sub-section, the implications of these findings and the explanations of the findings are discussed: At the domain level, the improvement rates of battery and electric motor are relatively lower than those of power electronics and charging &discharging. Considering the rather big differences of technologies among domains, this might be explained by the theory which attributes the differences of improvement rates to the scaling and complexity of interactions among components of the artifacts that make up the technology domain [109]. The technology components in batteries and electric motors more intensely interact than those in power electronics or charging&discharging and thus these systems have higher complexity or lower modularity, which results in a slower pace of improvement. At the subdomain level, power electronics of hybrid EV is the subdomain with the highest improvement rate of 38.5% among all the tested subdomains. The improvement rate of the other PE subdomain is much lower, which is 15.8%and the explanation for this is uncertain. There is not a clear reason for the hybrid EV domain to have lower complexity or different scaling. The hybrid electric vehicle has multiple energy sources and has high requirements on the switch or cooperation of the energy sources. In hybrid EV, power electronics technology plays a major role to improve the efficiency of drive system in driving range and fuel economy and the reliability and affordability of power electronic technology may lead hybrid EV to the market [98]. One might speculate that this allows for more modularity but this and other explanations remain speculative. In the battery domain, the improvement rate of lithium-ion battery is the fastest and the lead acid battery is the slowest. The implications of this finding are in full concordance with the widely-held view that the lithium-ion battery is the trend of battery in EV in the near future. A non-mechanistic explanation for why these batteries is improving more rapidly is that they emerged later with higher costs and more paths to improvement than earlier battery systems but a fundamental explanation in terms of complexity and scaling remains ambiguous because of lack of deep investigation of these aspects of the battery system. The improvement rate of PM motor is the fastest in the EM domain. This might be explained by the fact that the PM motor is assumed to share a larger market than induction motor in EV, though they now share equal market, because of its high efficiency as the increasing demand of protecting the environment and high energy efficiency [93,94,110].

Energy is the most important company with the most HPPs- patents 10, 11, 12, 14 and 16. As shown in Fig. 4, these five high persistence patents are about electrodes including carbonaceous negative electrodes, lithium nickel oxide positive electrode and lithium manganese oxide positive electrode. In PM motor, as the parent company of Sanyo Electric Co., LTD., Panasonic has the most HPPs, which are patent 8, 22, 36, 42 and 43. The invention topic of these patents is about PM rotors. Toyota is the only company that has more than one HPP in the discharging domain. There are only 15 HPPs in this domain and Toyota invented 4. The topic of patent 14, 15, 17 is about charging and discharging control. Patent 12 is about battery module balancing and it initiates this technology cluster in the main path. From an overall view, Toyota is the only company that appears in three subdomains, indicating that it has the most coverage of the subdomains. 4. Discussion The results are summarized in Table 4 to offer a general information of the four critical subdomains. These four subdomains belong to distinct domains, so each of them has its own features. Power electronics in hybrid EV has the highest improvement rate. It can be inferred that power electronics in hybrid EV have a relatively high speed of developing than the other three. The topics of patents in the main path of this subdomain is three, which implies that the focus of this subdomain is concentrated. In the column of key players, the most significant players are listed. It is interesting to find that except the subdomain of lithiumion battery, the most important key players in the other three subdomains all belong to Japan, indicating the significant role Japan plays

4.2. Technology trajectories in critical subdomains The technology trajectories of the four subdomains with the highest improvement rate in the domain were explored by main path analysis. In the subdomain of power electronics in hybrid EV, cluster B (energy source control) and C (power control) are the two main clusters in the main path. The patents in B (energy source control) and most of the

Table 4 Summary of the improvement rate, technology trajectories and key assignees. Subdomain

Improvement rate

Main path

Key players

PE-hybrid Lithium-ion battery PM motor Discharging

38.5% 13.2% 10.1% 28.6%

34HPPs (3 clusters) 28HPPs (7 clusters) 35 HPPs (8 clusters) 15 HPPs (6 clusters)

Toyota, Honda E-One Moli Energy Panasonic Toyota

Note: The key players listed here are the most significant players witch rank first in each subdomain. 10

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patents in C (power control) reveal the multi energy sources feature of power electronics in hybrid EV compared with pure battery EV. This implies the patents related to hybrid PE innovation has high concentration on multi energy sources control giving weak support to the speculation in the previous sub-section about possible emergence of additional modularity as an explanation for the higher improvement rate. Meanwhile, instead of new overall configurations, the later development of PE technology pays more attention to control details. This is in accordance with the technical challenges of power devices, which are detailed switching technologies, such as switching loses during turn-on and turn-off, switching frequency of operating mode [98]. Therefore, the focus of PE technology in hybrid EV in the recent years is detailed technologies and it might still be the trend in the future. In lithium-ion battery field, the electrode, especially positive electrode, is the essential innovation topic of lithium-ion battery. As for the positive electrode, the electrode consisting of lithium manganese oxide or lithium nickel oxide is consistently important in lithium-ion battery innovation from 1980 to 2015. As for the negative electrode, carbon is a commonly used material from the early dates, while the innovation of electrodes containing silicon is emerging in recent years owing to its advantage in facilitating high energy capacity. In addition, Carbon coating of electrodes is an emerging topic because it can contribute to improving the rate capability and cycle stability of the electrodes. In PM motor subdomain, the invention related to PM rotor is the core topic from 1978 to 2015. In the main path results of discharging shows that this subdomain belongs to battery management system and it is often considered with charging. Besides, there are eight isolated clusters. A (Charging and discharging control), E (Battery module balancing) and H (Plural batteries controlling) are the dominant technologies in the main path from 2000 to 2015. This indicates that main path analysis further decomposes the subdomain into technology clusters, yielding more details about the subdivisions and their evolution in this subdomain, even when there is not a great number of patents in the cluster. Stating from domains to sub-domains and continuing to small clusters gives a valuable cascade of topics of interest.

5. Conclusions In this study, we explored the improvement rate, technology trajectories and key assignees for EV domains and subdomains to have a better understanding of the technology innovation in electric vehicles. The conclusions and implications found by the results in this study are as follows: (1) The estimation of technology improvement rate in this study indicates that the higher complexity of batteries and electric motors result in relatively slower improving technology innovation than charging and discharging or power electronics in EV field and thereby makes them candidates for hindering the popularization of EV. A possible implication for the policy makers encouraging EV development is to issue more incentive plans for innovations in the battery and electric motor domains. Moreover, power electronics for hybrid electric vehicle has the highest estimated improvement rate than other subdomains. It is a commonly adopted statement that power electronics plays a significant role in hybrid electric vehicle. The result in this paper proves this point from the quantitative view by patent analysis. The implication is that the market participants in hybrid EV need to pay close attention to the power electronics for the hybrid electric vehicle. (2) The technology trajectories of critical subdomains quantitively prove the focuses of subdomains, such as positive electrode in lithium-ion battery and rotor in PM motor. In addition, the results also show the trend or emerging topics of subdomains: the detailed switching technologies are the focus in recent years in PE for hybrid EV and it might still be the trend in the future; As for lithium-ion battery, the silicon negative electrode is found to be an emerging research topic in recent years. These findings could offer additional means for choosing hot or emerging research topics for researchers and scientists in EV field. (3) The key players found by the main path method from the view of innovation are also important players in EV from the market view. This is objective evidence for the widely-believed premise that technology innovation is essential in the EV field to market success. The implication is that other market participants should not only pay more attention to the adjustment of business strategy of these companies to monitor the market, but also make effort to invent important EV related technologies or seek cooperation opportunities with these players to occupy the market.

4.3. Key assignees in critical subdomains The apparently strongest key players in the four subdomains are Toyota and Honda in hybrid power electronics, E-One Moli Energy Corp in lithium-ion battery, Panasonic in PM motors and Toyota in discharging. The HPPs’ research topics of these key players are also the main technology topics in the main path of the subdomain. Toyota and Honda are the pioneers in hybrid EV. The hybrid electric vehicle was first widely commercialized by Toyota when it released the Toyota Prius in 1997, followed by the Honda Insight in 1999 [111]. Furthermore, both companies continue to be the leading companies with large global market share in hybrid EV [112,113]. Toyota has sold more than 10 million units of hybrid EV as of January 2017 [112]. As the key player in the lithium-ion battery subdomain with the most HPPs, E-One Moli Energy Corp. is a lithium-ion battery company in Taiwan which aims to produce high capacity energy cells. It has offered batteries to Ford for electric cars, and in 2008 became the first qualified battery supplier of lithium-ion battery for BMW MINI E [114]. It owns the first North American high-volume manufacturer of rechargeable lithium-ion batteries in Canada, resulting in being listed by the Critical Foreign Dependencies Initiative [115]. Panasonic is the key player in PM motor and it also ranks second in lithium-ion battery subdomain, so it is unignorably a player in EV field. From an overall view, Toyota is the only company that appears in three subdomains except lithium-ion battery, indicating that it has the most coverage of the HPPs. Moreover, it has announced establishment of a joint venture with Panasonic [116], the second key player in lithium-ion battery, to develop batteries in EV. This means Toyota will act in important innovation roles covering all critical subdomains in EV field.

Although this work provides insight and novel results and discussions about the technological development of EV, there are still some limitations. For example, there are two types of EV according to the application. One is the car and the other is “the bus, truck and lorry”. Exploring and comparing the technology development of the domains for these two types is of practical significance from the aspect of application. While at present, it seems not to be possible to achieve the division of vehicle types by patent classification system, which are commonly used to class the patents and we also use the classification system to decompose the EV filed in the work. Neither the IPC or UPC patent classification systems make distinctions about vehicle type. The division of vehicle types could be achieved by proposing a method using natural language processing. It is worth studying and we will try to find a good method to divide the types in the future to explore more information form the view of application in the future work CRediT authorship contribution statement Sida Feng: Conceptualization, Data curation, Visualization, Investigation, Writing - original draft, Formal analysis. Christopher L. Magee: Methodology, Supervision, Writing - review & editing. 11

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Declaration of Competing Interest

Acknowledgement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

The authors acknowledge the funding support for this work received from the SUTD-MIT International Design Center and the China Scholarship Council (CSC). We also are grateful for input from Professor Giorgio Triulzi of University des Andes (Colombia), Colombia and Dr. Subarna Basnet of the MIT International Design Center.

Appendix (see Table A1).

Table A1 Summary of abbreviations. Full name

Abbreviation

electric vehicle electric motor power electronics charging and discharging classification overlap method International Patent Classification United States Patent Classification permanent magnet high persistence patents local persistence global persistence

EV EM PE Charging &Dis COM IPC UPC PM HPP LP GP

[17] Onat NC, Kucukvar M, Aboushaqrah NNM, Jabbar R. How sustainable is electric mobility? a comprehensive sustainability assessment approach for the case of Qatar. Appl Energy 2019;250:461–77. [18] Qiao QY, Zhao FQ, Liu ZW, He X, Hao H. Life cycle greenhouse gas emissions of electric vehicles in China: combining the vehicle cycle and fuel cycle. Energy 2019;177:222–33. [19] Lombardi L, Tribioli L, Cozzolino R, Bella G. Comparative environmental assessment of conventional, electric, hybrid, and fuel cell powertrains based on LCA. Int J Life Cycle Assess 2017;22:1989–2006. [20] Li FY, Ou R, Xiao XL, Zhou KL, Xie W, Ma DW, et al. Regional comparison of electric vehicle adoption and emission reduction effects in China. Resour Conserv Recycling 2019;149:714–26. [21] Rupp M, Handschuh N, Rieke C, Kuperjans I. Contribution of country-specific electricity mix and charging time to environmental impact of battery electric vehicles: a case study of electric buses in Germany. Appl Energy 2019;237:618–34. [22] Onat NC, Kucukvar M, Afshar S. Eco-efficiency of electric vehicles in the United States: a life cycle assessment based principal component analysis. J Cleaner Prod 2019;212:515–26. [23] Yu A, Wei YQ, Chen WW, Peng NJ, Peng LH. Life cycle environmental impacts and carbon emissions: a case study of electric and gasoline vehicles in China. Transp Res Part D-Transp Environ 2018;65:409–20. [24] Nienhueser IA, Qiu YM. Economic and environmental impacts of providing renewable energy for electric vehicle charging - a choice experiment study. Appl Energy 2016;180:256–68. [25] Li PL, Zhao PJ, Brand C. Future energy use and CO2 emissions of urban passenger transport in China: a travel behavior and urban form based approach. Appl Energy 2018;211:820–42. [26] Poullikkas A. Sustainable options for electric vehicle technologies. Renew Sustain Energy Rev 2015;41:1277–87. [27] Nanaki EA, Koroneos CJ. Comparative economic and environmental analysis of conventional, hybrid and electric vehicles - the case study of Greece. J Cleaner Prod 2013;53:261–6. [28] Sierzchula W, Bakker S, Maat K, van Wee B. The influence of financial incentives and other socio-economic factors on electric vehicle adoption. Energy Policy 2014;68:183–94. [29] Nian V, Hari MP, Yuan J. A new business model for encouraging the adoption of electric vehicles in the absence of policy support. Appl Energy 2019;235:1106–17. [30] Du JY, Ouyang DH. Progress of Chinese electric vehicles industrialization in 2015: a review. Appl Energy 2017;188:529–46. [31] Ma SC, Xu JH, Fan Y. Willingness to pay and preferences for alternative incentives to EV purchase subsidies: an empirical study in China. Energy Econ 2019;81:197–215. [32] Gough R, Dickerson C, Rowley P, Walsh C. Vehicle-to-grid feasibility: a technoeconomic analysis of EV-based energy storage. Appl Energy 2017;192:12–23. [33] Noel L, Carrone AP, Jensen AF, de Rubens GZ, Kester J, Sovacool BK. Willingness to pay for electric vehicles and vehicle-to-grid applications: a Nordic choice experiment. Energy Econ 2019;78:525–34. [34] Chen F, Taylor N, Kringos N. Electrification of roads: opportunities and challenges.

References [1] Zhao X, Doering OC, Tyner WE. The economic competitiveness and emissions of battery electric vehicles in China. Appl Energy 2015;156:666–75. [2] Amini MH, Moghaddam MP, Karabasoglu O. Simultaneous allocation of electric vehicles' parking lots and distributed renewable resources in smart power distribution networks. Sustain Cities Soc 2017;28:332–42. [3] Veneri O. Technologies and Applications for Smart Charging of Electric and Plug-in Hybrid Vehicles 2016. [4] Hong W, Huang Y, He H, Chen L, Wei L, Khajepour A. Chapter 5 – Energy Management of Hybrid Electric Vehicles. Modeling Dynamics & Control of Electrified Vehicles. 2018. [5] Correa G, Munoz P, Falaguerra T, Rodriguez CR. Performance comparison of conventional, hybrid, hydrogen and electric urban buses using well to wheel analysis. Energy 2017;141:537–49. [6] Brady J, O'Mahony M. Travel to work in Dublin. The potential impacts of electric vehicles on climate change and urban air quality. Transp Res Part D-Transp Environ 2011;16:188–93. [7] Sierzchula W, Nemet G. Using patents and prototypes for preliminary evaluation of technology-forcing policies: lessons from California's Zero emission vehicle regulations. Technol Forecast Soc Chang 2015;100:213–24. [8] Arias MB, Bae S. Electric vehicle charging demand forecasting model based on big data technologies. Appl Energy 2016;183:327–39. [9] Wikstrom M, Hansson L, Alvfors P. Socio-technical experiences from electric vehicle utilisation in commercial fleets. Appl Energy 2014;123:82–93. [10] Adnan N, Nordin SM, Amini MH, Langove N. What make consumer sign up to PHEVs? predicting Malaysian consumer behavior in adoption of PHEVs. Transp Res Part a-Policy Pract 2018;113:259–78. [11] Fan Y, Peng BB, Xu JH. The effect of technology adoption on CO2 abatement costs under uncertainty in China's passenger car sector. J Cleaner Prod 2017;154:578–92. [12] Ma SC, Fan Y, Feng LY. An evaluation of government incentives for new energy vehicles in China focusing on vehicle purchasing restrictions. Energy Policy 2017;110:609–18. [13] de Souza LL, Lora EES, Palacio JCE, Rocha MH, Reno MLG, Venturini OJ. Comparative environmental life cycle assessment of conventional vehicles with different fuel options, plug-in hybrid and electric vehicles for a sustainable transportation system in Brazil. J Cleaner Prod 2018;203:444–68. [14] Hawkins TR, Singh B, Majeau-Bettez G, Stromman AH. Comparative environmental life cycle assessment of conventional and electric vehicles. J Ind Ecol 2013;17:53–64. [15] Gai YJ, Wang A, Pereira L, Hatzopoulou M, Posen ID. Marginal greenhouse gas emissions of Ontario's electricity system and the implications of electric vehicle charging. Environ Sci Technol 2019;53:7903–12. [16] Yagcitekin B, Uzunoglu M, Karakas A, Erdinc O. Assessment of electrically-driven vehicles in terms of emission impacts and energy requirements: a case study for Istanbul, Turkey. J Clean Prod 2015;96:486–92.

12

Applied Energy 260 (2020) 114264

S. Feng and C.L. Magee Appl Energy 2015;150:109–19. [35] Wei YM, Kang JN, Yu BY, Liao H, Du YF. A dynamic forward-citation full path model for technology monitoring: an empirical study from shale gas industry. Appl Energy 2017;205:769–80. [36] Lindman A, Soderholm P. Wind energy and green economy in Europe: measuring policy-induced innovation using patent data. Appl Energy 2016;179:1351–9. [37] Miguez JL, Porteiro J, Perez-Orozco R, Patino D, Rodriguez S. Evolution of CO2 capture technology between 2007 and 2017 through the study of patent activity. Appl Energy 2018;211:1282–96. [38] Zhang Y, Guo Y, Wang XF, Zhu DH, Porter AL. A hybrid visualisation model for technology roadmapping: bibliometrics, qualitative methodology and empirical study. Technol Anal Strategic Manage 2013;25:707–24. [39] Sun HP, Geng Y, Hu LX, Shi LY, Xu T. Measuring China's new energy vehicle patents: A social network analysis approach. Energy 2018;153:685–93. [40] Golembiewski B, Stein NV, Sick N, Wiemhofer HD. Identifying trends in battery technologies with regard to electric mobility: evidence from patenting activities along and across the battery value chain. J Cleaner Prod 2015;87:800–10. [41] Borgstedt P, Neyer B, Schewe G. Paving the road to electric vehicles - A patent analysis of the automotive supply industry. J Cleaner Prod 2017;167:75–87. [42] Sick N, Nienaber AM, Liesenkotter B, vom Stein N, Schewe G, Leker J. The legend about sailing ship effects - Is it true or false? The example of cleaner propulsion technologies diffusion in the automotive industry. J Cleaner Prod 2016;137:405–13. [43] Wu YQ, Li CJ, Zhang QQ. The Analysis of Transdisciplinary Integration Characteristic for China's Pure Electric Vehicles Technology from Patent Perspective. In: Yan J, Sun F, Chou SK, Desideri U, Li H, Campana P, et al., editors. 8th International Conference on Applied Energy2017. p. 2478-83. [44] Aaldering LJ, Leker J, Song CH. Competition or collaboration? - Analysis of technological knowledge ecosystem within the field of alternative powertrain systems: a patent-based approach. J Cleaner Prod 2019;212:362–71. [45] Chan CC. The state of the art of electric and hybrid vehicles. Proc IEEE 2002;90:247–75. [46] Rajashekara K. Present status and future trends in electric vehicle propulsion technologies. IEEE J Emerg Selected Topics Power Electr 2013;1:3–10. [47] Yong JY, Ramachandaramurthy VK, Tan KM, Mithulananthan N. A review on the state-of-the-art technologies of electric vehicle, its impacts and prospects. Renew Sustain Energy Rev 2015;49:365–85. [48] Un-Noor F, Padmanaban S, Mihet-Popa L, Mollah MN, Hossain E. A Comprehensive Study of Key Electric Vehicle (EV) Components, Technologies, Challenges, Impacts, and Future Direction of Development. Energies. 2017; 10. [49] Rubino L, Capasso C, Veneri O. Review on plug-in electric vehicle charging architectures integrated with distributed energy sources for sustainable mobility. Appl Energy 2017;207:438–64. [50] Lopez I, Ibarra E, Matallana A, Andreu J, Kortabarria I. Next generation electric drives for HEV/EV propulsion systems: technology, trends and challenges. Renew Sustain Energy Rev 2019;114. [51] Kang B, Ceder G. Battery materials for ultrafast charging and discharging. Nature 2009;458:190–3. [52] Kang KS, Meng YS, Breger J, Grey CP, Ceder G. Electrodes with high power and high capacity for rechargeable lithium batteries. Science 2006;311:977–80. [53] Fotouhi A, Auger DJ, Propp K, Longo S, Wild M. A review on electric vehicle battery modelling: from Lithium-ion toward Lithium-Sulphur. Renew Sustain Energy Rev 2016;56:1008–21. [54] Chan CC, Chau KT. An overview of power electronics in electric vehicles. IEEE Trans Ind Electron 1997;44:3–13. [55] Moore G. Cramming more components onto integrated circuits. Electron Magaz 1965;38:114–7. [56] Koh H, Magee CL. A functional approach for studying technological progress: application to information technology. Technol Forecast Social Change 2006;73:1061–83. [57] Koh H, Magee CL. A functional approach for studying technological progress: extension to energy technology. Technol Forecast Social Change 2008;75:735–58. [58] Vendruscolo M, Dobson CM. Protein dynamics: Moore's law in molecular biology. Curr Biol 2011;21:R68–70. [59] Benson CL, Magee CL. On improvement rates for renewable energy technologies: solar PV, wind turbines, capacitors, and batteries. Renew Energy 2014;68:745–51. [60] Farmer JD, Lafond F. How predictable is technological progress? Res Policy 2016;45:647–65. [61] Magee CL, Basnet S, Funk JL, Benson CL. Quantitative empirical trends in technical performance. Technol Forecast Social Change 2016;104:237–46. [62] Martino J. Examples of technological trend forecasting for research and development planning. Technol Forecast Social Change 1971;2:247–60. [63] Nordhaus WD. Do real-output and real-wage measures capture reality? The history of lighting suggests not1997. [64] Nordhaus WD. Two centuries of productivity growth in computing. J Economic History 2007;67:128–59. [65] Benson CL, Magee CL. Quantitative determination of technological improvement from patent data. PLoS ONE 2015;10. [66] Triulzi G AJ, Magee CL. Estimating Technology Performance Improvement Rates by Mining Patent Data. SSRN-id2987588, 2018. [67] Dosi G. Technological paradigms and technological trajectories: A suggested interpretation of the determinants and directions of technical change. Res Policy 1982;11:147–62. [68] Huenteler J, Ossenbrink J, Schmidt TS, Hoffmann VH. How a product's design hierarchy shapes the evolution of technological knowledge-Evidence from patentcitation networks in wind power. Res Policy 2016;45:1195–217.

[69] Verspagen B. Mapping technological trajectories as patent citation networks: A study on the history of fuel cell research. Adv Complex Syst 2007;10:93–115. [70] Hummon NP, Doreian P. connectivity in a citation network-the development of DNA theory. Social Netw 1989;11:39–63. [71] Mina A, Ramlogan R, Tampubolon G, Metcalfe JS. Mapping evolutionary trajectories: applications to the growth and transformation of medical knowledge. Res Policy 2007;36:789–806. [72] Huang Y, Zhu DH, Qian Y, Zhang Y, Porter AL, Liu YQ, et al. A hybrid method to trace technology evolution pathways: a case study of 3D printing. Scientometrics 2017;111:185–204. [73] Gwak JH, Sohn SY. A novel approach to explore patent development paths for subfield technologies. J Assoc Inform Sci Technol 2018;69:410–9. [74] Park H, Magee CL. Tracing Technological Development Trajectories: A Genetic Knowledge Persistence-Based Main Path Approach. Plos One. 2017;12. [75] Park H, Magee CL. Quantitative identification of technological discontinuities. IEEE Access 2019;7:8135–50. [76] Magee CL, Kleyn PW, Monks BM, Betz U, Basnet S. Pre-existing technological core and roots for the CRISPR breakthrough. PLoS ONE 2018:13. [77] You D, Park H. Developmental trajectories in electrical steel technology using patent information. Sustainability. 2018;10. [78] Ding CG, Hung WC, Lee MC, Wang HJ. Exploring paper characteristics that facilitate the knowledge flow from science to technology. J Informetr 2017;11:244–56. [79] Huang MH, Chang HW, Chen DZ. The trend of concentration in scientific research and technological innovation: A reduction of the predominant role of the U.S. in world research & technology. J Informetr 2012;6:457–68. [80] Huang MH, Yang HW, Chen DZ. Increasing science and technology linkage in fuel cells: a cross citation analysis of papers and patents. J Informetr 2015;9:237–49. [81] Kim J, Lee S. Patent databases for innovation studies: a comparative analysis of USPTO, EPO, JPO and KIPO. Technol Forecast Social Change 2015;92:332–45. [82] Ribeiro LC, Kruss G, Britto G, Bernardes AT, Albuquerque EDE. A methodology for unveiling global innovation networks: patent citations as clues to cross border knowledge flows. Scientometrics 2014;101:61–83. [83] Jiang JM, Goel RK, Zhang XY. Knowledge flows from business method software patents: influence of firms' global social networks. J Technol Transf 2019;44:1070–96. [84] Xiang XY, Cai H, Lam S, Pei YL. International knowledge spillover through coinventors: an empirical study using Chinese assignees' patent data. Technol Forecast Social Change 2013;80:161–74. [85] Benson CL, Magee CL. A hybrid keyword and patent class methodology for selecting relevant sets of patents for a technological field. Scientometrics 2013;96:69–82. [86] Benson CL, Magee CL. Technology structural implications from the extension of a patent search method. Scientometrics 2015;102:1965–85. [87] Benson CL, Triulzi G, Magee CL. Is there a Moore's Law for 3D printing? 3d Print Addit Manuf 2018;5:53–62. [88] Yoon B, Magee CL. Exploring technology opportunities by visualizing patent information based on generative topographic mapping and link prediction. Technol Forecast Social Change 2018;132:105–17. [89] Patsnap. Patsnap patent search and analysis. Retrieved November 29, 2018, from http://www.patsnap.com. 2018. [90] Emadi A, Lee YJ, Rajashekara K. Power electronics and motor drives in electric, hybrid electric, and plug-in hybrid electric vehicles. IEEE Trans Ind Electron 2008;55:2237–45. [91] Sarlioglu B, Morris CT, Di H, Li SL. Driving toward accessibility: a review of technological improvements for electric machines, power electronics, and batteries for electric and hybrid vehicles. IEEE Ind Appl Mag 2017;23:14–25. [92] Tang ZC, Guo CL, Jia DM. Analysis of Electric Vehicle Battery Charging and Discharging. In: Xu XD, Li B, Lu QM, Yan XY, Li JL, editors. Mechatronics Engineering, Computing And Information Technology2014. p. 1879-83. [93] Kumar MS, Revankar ST. Development scheme and key technology of an electric vehicle: an overview. Renew Sustain Energy Rev 2017;70:1266–85. [94] Chau KT, Li WL. Overview of electric machines for electric and hybrid vehicles. Int J Veh Des 2014;64:46–71. [95] de Santiago J, Bernhoff H, Ekergard B, Eriksson S, Ferhatovic S, Waters R, et al. Electrical motor drivelines in commercial all-electric vehicles: a review. IEEE Trans Veh Technol. 2012;61:475–84. [96] Martinelli A, Nomaler O. Measuring knowledge persistence: a genetic approach to patent citation networks. J Evolut Econ 2014;24:623–52. [97] Subiyanto Mohamed A, Hannan MA. Photovoltaic maximum power point tracking controller using a new high performance boost converter. Int Rev Electr Eng-IREE 2010;5:2535–45. [98] Hannan MA, Azidin FA, Mohamed A. Hybrid electric vehicles and their challenges: a review. Renew Sustain Energy Rev 2014;29:135–50. [99] Hannan MA, Abd Ghani Z, An Mohamed A. Enhanced inverter controller for PV applications using the dSPACE Platform. Int J Photoenergy 2010. [100] Chen JJ. Recent progress in advanced materials for lithium ion batteries. Materials 2013;6:156–83. [101] Blomgren GE. The development and future of lithium ion batteries. J Electrochem Soc 2017;164:A5019–25. [102] Nitta N, Wu FX, Lee JT, Yushin G. Li-ion battery materials: present and future. Mater Today 2015;18:252–64. [103] Aaldering LJ, Song CH. Tracing the technological development trajectory in postlithium-ion battery technologies: a patent-based approach. J Clean Prod 2019:241. [104] P.A. Nelson KGG, I. Bloom, and D.W. Dees. Modeling the Performance and Cost of Lithium-Ion Batteries for Electric-Drive Vehicles. Argonne National Laboratory;

13

Applied Energy 260 (2020) 114264

S. Feng and C.L. Magee 2011. [105] Li HQ, Zhou HS. Enhancing the performances of Li-ion batteries by carboncoating: present and future. Chem Commun 2012;48:1201–17. [106] Casimir A, Zhang HG, Ogoke O, Amine JC, Lu J, Wu G. Silicon-based anodes for lithium-ion batteries: effectiveness of materials synthesis and electrode preparation. Nano Energy 2016;27:359–76. [107] Rahimi-Eichi H, Chow MY. Adaptive Parameter Identification and State-of-Charge Estimation of Lithium-Ion Batteries. 38th Annual Conference on Ieee Industrial Electronics Society2012. p. 4012-7. [108] Muneer TK, M.L.; Doyle, A. Electric Vehicles: Prospects and Challenges: Elsevier; 2017. [109] Basnet S, Magee CL. Modeling of technological performance trends using design theory. Des Sci 2016:2. [110] Bilgin B, Liang JB, Terzic MV, Dong JN, Rodriguez R, Trickett E, et al. Modeling

[111] [112] [113] [114] [115] [116]

14

and analysis of electric motors: state-of-the-art review. IEEE Trans Transp Electrif 2019;5:602–17. Lake M. How it works; A Tale of 2 Engines: How Hybrid Cars Tame Emissions. The New York Times; 2001-11-08. Worldwide Sales of Toyota Hybrids Surpass 10 Million Units. Toyota Europe Newroom; 2017-01-14. Schreffler R. Toyota Remains Unchallenged Global Hybrid Leader. Ward's AutoWorld; 2014-08-20. website M-E-OMO. Molicel-E-One Moli Energy Corp-Company Profile. Viewing cable 09STATE15113, request for information:critical foreign dependencies (critical infrastructure and key resources located abroad). Wayback Machine; 2010. Morgan T. Toyota and Panasonic confirm EV battery joint venture. Autocar; 201901-22.