Towards a smarter hybrid energy storage system based on battery and ultracapacitor - A critical review on topology and energy management

Accepted Manuscript Towards a smarter hybrid energy storage system based on battery and ultracapacitor - a critical review on topology and energy management Rui Xiong, Huan Chen, Chun Wang, Fengchun Sun PII:

S0959-6526(18)32478-8

DOI:

10.1016/j.jclepro.2018.08.134

Reference:

JCLP 13918

To appear in:

Journal of Cleaner Production

Received Date: 23 June 2018 Revised Date:

19 July 2018

Accepted Date: 13 August 2018

Please cite this article as: Xiong R, Chen H, Wang C, Sun F, Towards a smarter hybrid energy storage system based on battery and ultracapacitor - a critical review on topology and energy management, Journal of Cleaner Production (2018), doi: 10.1016/j.jclepro.2018.08.134. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT

Towards a smarter hybrid energy storage system based on battery and ultracapacitor - a critical review on topology

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and energy management Rui Xiong*, Huan Chen, Chun Wang, Fengchun Sun

Department of Vehicle Engineering, School of Mechanical Engineering, Beijing Institute of Technology,

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Beijing 100081, China.

Corresponding Author: [email protected], [email protected] (R. Xiong)

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Address: Department of Vehicle Engineering, School of Mechanical Engineering, Beijing

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Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China.

Tel./Fax: +86 (10) 6891−4070

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Towards a smarter hybrid energy storage system based on battery and ultracapacitor - a critical review on topology and

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energy management Abstract - Hybrid Energy Storage System (HESS) can well solve the problems faced by alternative single energy storage system in terms of meeting the needs of high specific power and high specific

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energy simultaneously for plug-in hybrid electric vehicles (HEVs). A HESS containing battery and

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ultracapacitor (UC) has drawn much attention. However, there have been relatively few reviews on its structures and energy management strategies (EMSs). Based on the summary and analysis from the existing publications, this paper reviews and discusses the structures and the EMSs of HESSs comprised of battery and UC. Focusing on energy management research, a detailed discussion of

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rules-based control algorithms, optimization-based control algorithms and intelligent-based control algorithms is presented. Several typical implementations and applications are presented in detail,

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and a comparative evaluation of these methods can help researchers select the appropriate method to develop EMSs for HESSs. Finally, the paper also highlights a number of key factors and

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challenges, and presents the possible recommendations for the development of big data and machine learning-based algorithm for the energy management of the HESSs. Keywords: Hybrid Energy Storage System; Topologies; Energy Management Strategy; Battery; Ultracapacitor.

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1. Introduction In recent years, issues such as haze, environment pollution and energy shortages have appeared increasingly. Data from the U.S. Department of Energy show that only 15% of fuel energy is spent on the operation of vehicles and their associated components in conventional internal combustion

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engine vehicles while over 40% of the fuel energy is emitted as heat through exhaust emissions [1]. This not only causes great waste but also serious environment pollution [2]. In contrast, the energy conversion efficiency of electric vehicles (EVs) is as high as 75% or more. In other words, more

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than 75% of the energy is spent on the operation of vehicles and their related components. In addition, EVs also have the characteristics of low emission, low pollution, etc., so they have

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attracted growing interest over the last decade worldwide [3-4]. To vigorously popularize EVs, the United States, the European Union, China and other countries and regions have released a number of relevant policies [5]. Among them, the issue of how to improve the relevant characteristics of energy storage systems (ESSs) to meet the demand of high energy and high power of EVs has been

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given the closet attention [6]. However, given the current technologies, a single ESS is very difficult to simultaneously meet the energy and power requirements of a vehicle without affecting its lifetime. Therefore, combining two or more ESSs into a hybrid-ESS could be a good solution to

disadvantages [7].

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solve the above problem by making the best of each ESS’s advantages and avoiding their

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The ESS in an EV has a wide range of characteristics and performance. Its indicators mainly include the rated power, charge/discharge rate, power density, energy density, self-discharge rate, response time, energy storage efficiency and cycle life, etc. [8]. A suitable ESS can be chosen based on different performance requirements. The commonly used ESSs are divided into three categories: mechanical energy storage systems, electrical energy storage systems and chemical energy storage systems [9], as shown in Fig. 1. These ESSs have significantly different characteristics. The characteristic parameters of the typical ESSs are shown in Table 1. It can be concluded from Table 1 that the response times of compressed air energy storage system [10-12], fuel cell energy storage

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ACCEPTED MANUSCRIPT system [13-14] and battery energy storage system [15-16] are relatively slower. These types of ESSs can provide energy with considerably long discharge times and make good use of their energy advantages in a HESS. At the same time, the flywheel energy storage system [17-19], UC energy storage system [20-21] and superconducting magnetic energy storage system [22-23] have

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the characteristics of fast response time and high discharge rate in a short time. Although each storage technology has its own defects, it is possible to form a series of HESSs based on the analysis of power and energy characteristics of various ESSs, including batteries [24], UCs,

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flywheels [19], [25], superconducting magnets [10], compressed air, fuel cells, etc. [8-10], [26-27].

Fig. 1. Classification of commonly used energy storage systems.

The HESSs consists of two or more onboard energy storage sources which need to be able to

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meet the complex driving conditions. Therefore, appropriate topologies and EMSs of HESSs are required to coordinate the power distribution among different power sources. Furthermore, it is an

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effective way to extend the lifetime of the HESSs [28], improve system efficiency and enhance the system economy by reasonably distributing the power output. The topologies and the EMSs are the most important aspects for the HESSs research, and the two research directions are closely related. As different topologies have different power sources, each EMS differs quite greatly to each other. So far, there have been many novel studies on the topology and EMS for the HESSs in recent years, but there are few reviews on HESS research.

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ACCEPTED MANUSCRIPT Table 1 Typical energy storage system characteristics [10], [13]. ESS

Rated power (MW)

Typical discharge time

Power density (W/kg)

Compressed air

100-300

1~days

—

30~60

0

minutes

40~70

20~40

—

Flywheel

0~0.25

s~h

400~1600

5~130

20~100

ms~s

80~90

15~20

10 ~10

UC

0~0.3

ms~1h

0.1~10

0.1~15

2~40

0.1~10

ms~8s

500~2000

0.5~5

10~15

0~0.1

minutes~h

200~340

130~250

0.1~0.3

0~50

s~days

>500(W/L)

500~3000

0.5~2

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Fuel cell

4

7

5

6

ms

85~98

5~12

10 ~10

ms

75~80

—

—

ms

65~95

5~8

600~1200

ms~minutes 20~66

5~30

10 ~10

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Superconducting magnetism Lithium-ion battery

Energy density Self-discharge Response Efficiency Lifetime Cycle lifetime (W·h/kg) rate /day (%) time (%) (years) (cycles)

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Based on the battery/UC hybrid energy storage systems (HESSs), this paper provides a comprehensive collection and discussion of the novel methods proposed in recent years. In contrast to previous papers, future developing trends of HESS EMSs are also discussed in this paper. Due to the defects of control algorithms and the complexity of the HESSs, multi-information infusion and integration of multi-control method is proposed. Furthermore, with the development of machine

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learning, the relationship has become even closer between energy management and vehicle intelligent network.

The remainder of this paper is organized as follows: Section 2 analyzes different topologies

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composed of battery and UC and their merits and faults. Section 3 summarizes and analyzes the current research status of energy management strategies. Section 4 discusses future developing

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trends of energy management strategy on HESSs. Conclusions are drawn in Section 5.

2. Topologies of HESS

The HESSs are composed of the battery, UC, bi-directional DC/DC and other components. Recently, researchers have proposed many topologies of HESSs. Generally, these topologies are divided into three types: Passive Parallel Topology, Fully Active Topology and Semi Active Topology [28-29], as shown in Fig. 2. Different topologies have different cost, control method, adaptability, performance and energy conversion efficiency [30-31].

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Fig. 2. Classification of HESS topologies.

2.1 Passive Parallel Topology

The schematic diagram of the passive parallel HESSs is shown in Fig. 3. The battery and UC

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are directly connected in parallel to the DC bus [32]. Not including the bi-directional DC/DC converter, it has the lowest cost and simplest form. The UC mainly plays a role of low-pass filter in this structure. However, the power distribution of the battery and UC is largely determined by their

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respective internal resistance rather than the control system compared to other topologies. Since the HESSs cannot be effectively controlled and managed, the battery will greatly bear the high current

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and high frequency current during vehicle acceleration and braking, which is harmful to efficient and healthy use of the battery [33]. Meanwhile, as UC voltage and battery voltage need to be kept consistent all the time, the variation range and frequency of the UC voltage are greatly limited, resulting in low efficiency of the UC and not fully exerting its advantages in high power.

Fig. 3. Passive Parallel Topology. 6

ACCEPTED MANUSCRIPT 2.2 Fully Active Topology Fully active topology mainly refers to using one or more bi-directional DC/DC converters connected to the DC bus to decouple the battery and UC, which can make system control more accurate. It is divided into Series Topology, Parallel Topology and Multiple Input Converter

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Topology. As shown in Fig. 4 (a), the series topology has two different bi-directional DC/DC converters. Due to large voltage variation of the UC packs, one bi-directional DC/DC converter is connected between the battery and UC packs, and the other is connected between the UC packs and

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the DC bus. This configuration makes it easier to stabilize the terminal voltage of the battery packs and the DC bus. Another form of series topology is shown in Fig. 4 (b), which differs from the

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previous topology in that the location of the battery and UC packs is exchanged. The biggest problem of series topology lies in how to achieve a balance between cells under constant current load [28-29]. The parallel topology is shown in Fig. 4 (c). The battery and the UC packs are each connected to the DC bus through a bi-directional DC/DC converter [34-35]. The multi-input

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converter topology is shown in Fig. 4 (d), which includes a multi-port input bi-directional DC/DC converter. In this topology, the battery and UC pack voltage may be lower than the DC bus voltage, which may lead to relatively fewer balance problems [28-29].

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In general, since this topology can completely achieve decoupling of two energy sources by bi-directional DC/DC converters, the battery and UC power can be separately controlled and the

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DC bus voltage can also be more stable. However, the control strategy for the whole topology would be more complex and bring significant increases in loss, size, weight and cost.

(a) Series Topology

(b) Series Topology

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(c) Parallel Topology

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(d) Multi-input converter Topology Fig. 4. Fully Active Topology.

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2.3 Semi-Active Topology

The DC bus and battery or UC are decoupled through bi-directional DC/DC converters in

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semi-active topology. This approach mainly includes three types: UC/battery topology, battery/UC topology and hybrid diode topology. The UC/battery topology is shown in Fig. 5 (a), where the bi-directional DC/DC converter is connected in series with the UC pack while the battery pack is directly connected to the DC bus. Given the connection to the battery pack, the DC bus cannot bear a wide range of voltage fluctuation. If the DC bus voltage changes too frequently, the battery

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lifetime will be severely affected. The UC has a wide voltage range; thus it places higher demands on the bi-directional DC/DC converter [21], [30-31], [33]. The battery/UC topology is shown in Fig. 5 (b), where the bi-directional DC/DC converter is connected in series with the battery, while

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the UC is connected directly to the DC bus. The DC bus voltage of this topology can fluctuate

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within a certain range. In this topology, the UC acts as a low-pass filter, where the high frequency and peak currents caused by power fluctuations are absorbed by it. This helps to improve the system efficiency. Furthermore, as the battery and the DC bus are decoupled, the battery can maintain a relatively stable charge and discharge current [28-30]. H.T. Min [36] analyzed the factors of UC/battery topology and battery/UC topology such as efficiency, volume, weight, power, energy, etc. The experimental results showed that the energy consumption of UC/battery topology can be reduced by 7%. To further improve the HESS structure, J. Cao [29] used a small bi-directional DC/DC converter and a diode to build a new hybrid diode topology, which is shown in Fig. 5 (c). The greatest feature 8

ACCEPTED MANUSCRIPT of this topology is that the UC voltage is always higher than the battery voltage by using DC/DC control. Therefore, the UC can fully exert its power characteristics without being connected in series with the DC/DC converter. Moreover, the battery load curve tends to be gentler, which helps to extend the battery lifetime. Z.Y. Song [28] changed the bi-directional DC/DC converter into a

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directional DC/DC converter to further reduce the size and weight of the DC/DC converter and improve its conversion efficiency. The improved structure is shown in Fig. 5 (d). At the same time, the researchers compared the performance of four typical semi-active topologies mainly in terms of

(b) Battery/UC Topology

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(a) UC/Battery Topology

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costs based on the algorithm of dynamic programing [37].

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cost factors. The results show that the structure of the UC/battery topology has lower operating

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(c) Hybrid diode Topology

(d) Hybrid diode Topology

Fig. 5. Semi Active Topology.

Although each topology has its advantages, the number of DC/DC converter should not be too much considering the energy efficiency. It will significantly increase the energy loss else, especially for the DC/DC converter series connected with the UC. Conversely, if a DC/DC converter is not used and the passive structure may bring minimal energy loss, it is difficult to make full use of the best performance of each onboard energy component, and it is not conducive for increasing the reliability and safety of the HESSs. In summary, the semi-active mode using one bi-directional DC/DC converter is the most ideal structure. Based on the characteristics of voltage 9

ACCEPTED MANUSCRIPT and power requirements, the connected position of the DC/DC converter and power level can be flexibly designed to enable the system to have the best performance.

3. Energy Management Strategy

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To achieve improved system efficiency and extend the lifetime of the system under the premise of meeting the power requirement, the core of the HESS control strategy is how to distribute the power of the battery packs and the UC packs in different system states.

Under an excellent control strategy, the battery/UC hybrid energy storage system can make good

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use of the complementary features of the battery and UC. The purpose of the control strategy is to allocate the output/input power according to the characteristics of the two power sources as well as

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to improve the power efficiency, dynamic performance and extend the battery lifetime through controlling the output/input current and voltage. The control strategy requires that the battery not only provide average demand power and low-frequency power but also reduces the battery charge-discharge rate and current surge in order to extend its lifetime [38]. At the same time, the

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control strategy requires the UC to provide short-time peak power and high frequency power. It can be considered that the UC in the HESSs actually acts as a power buffer device when using UC to improve the high-power charge-discharge capacity in HESSs. Fig. 6 shows a semi-active HESS

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power distribution status diagram. Parameters of Battery and UC

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Preq

Pbat

PUC

Fig. 6. HESS power distribution diagram.

Currently, the control strategies of HESS EMS research in the literature can be divided into two 10

ACCEPTED MANUSCRIPT main categories: rule-based control strategy and optimization-based control strategy. The rule-based control strategies (RBC) mainly include deterministic rule control strategy, fuzzy logical control (FLC) strategy and wavelet transform (WT). The optimization-based control strategies mainly include dynamic programming (DP) [39], genetic algorithm (GA) [40], model predictive

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control (MPC) [27], [41], particle swarm optimization (PSO) [42] and linear programming [6]. Furthermore, it is worth mentioning that with recent studies on intelligent algorithms such as machine learning. Researchers also have started to apply these intelligent algorithms in HESS

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energy management. Therefore, HESS control strategies are divided into three categories for analysis in this paper, as shown in Fig. 7. The figure shows a list of commonly used control

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strategies.

Fig. 7. Classification of HESS control strategy.

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3.1 Rule-based control strategy

At present, the rule-based control strategies based on heuristics or empiric experience are the most widely used in practical applications. This type of strategies has the advantages of low computational complexity, simple control, strong robustness and high reliability. Because the pre-established rule-based control strategy cannot be adjusted in real application, it causes poor performance under some system conditions. The UC has limited capacity, so the next system control is directly affected by the previous system power distribution and state of charge (SOC) of the UC [43]. The rule-based control strategy is seldom used alone in theoretical research on HESSs. 11

ACCEPTED MANUSCRIPT It is mainly used as a comparison to optimal control strategies [44-47] or for multi-control strategy joint applications [34], [48]. 3.1.1 Deterministic rule control

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The core of the control strategy based on deterministic rules is to distribute the power according to the time constant of the battery and UC in a HESS and the peak power duration of the UC. The logic threshold strategy is the most widely used in many deterministic rules. Table 2 presents a min denote the SOC and its minimum general strategy of logic threshold, where SOC bat and SOCbat

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min max limit of battery, respectively; SOCUC , SOCUC and SOCUC denote the SOC and its minimum

and maximum limits of UC, respectively; Preq and Paverage denote the demand power and its

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average power of the load equipment, respectively; Pbat and PUC denote the output power of battery and UC, respectively; Pbat _ MaxCh arg e and Pbat _ MaxDisch arg e denote the maximum input and output power of battery, respectively;

Table 2 A general strategy of logic threshold.

ELSE

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min min IF SOCbat ≤ SOCbat THEN Pbat = 0; PUC = 0; ∧ SOCUC ≤ SOCUC

max THEN Pbat = 0; PUC = Preq ; IF Preq ≤ 0 ∧ SOCUC ≤ SOCUC

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max IF Preq ≤ 0 ∧ SOCUC > SOCUC THEN Pbat = max( Preq , Pbat _ MaxCh arg e ); PUC = 0; max IF 0 < Preq ≤ Paverage ∧ SOCUC ≥ SOCUC THEN Pbat = Preq ; PUC = 0;

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max IF 0 < Preq ≤ Paverage ∧ SOCUC < SOCUC THEN Pbat = Paverage ; PUC = Preq − Paverage ; min IF Preq > Paverage ∧ SOCUC ≥ SOCUC THEN Pbat = Paverage ; PUC = Preq − Paverage ;

min IF Preq > Paverage ∧ SOCUC < SOCUC THEN Pbat = max( Preq , PMaxDisch arg e ); PUC = 0;

Furthermore, due to the complex dynamics of HESS power requirements, researchers have introduced the frequency-based filtering algorithm based on the energy and power characteristics of both the battery and the UC. By using the filter, the high-frequency current of the required system power is provided by the UC packs while the low-frequency current is provided by the battery packs. However, it also requires constant debugging to achieve the ideal filter control for 12

ACCEPTED MANUSCRIPT deterministic operating conditions [49]. Taking system efficiency and battery lifetime as the optimization targets, A. Castaings [44] compared the results of the optimization-based control algorithm and the filtering algorithm. Under different operating conditions, observing the system state values can be used for the feedback regulator of the filter cutoff frequency [50].

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3.1.2 Fuzzy logical control

Fuzzy logical control (FLC) is a type of control algorithm based on fuzzy set theory, fuzzy language variable and fuzzy logical inference. The key of the control method is setting the rules for

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the membership function and fuzzy rule. The main features of FLC are that neither the fuzzy controller design needs to establish a precise mathematical model of the controlled objects, nor

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does it need to know the explicit mathematical relationship between the input and the output of the controller. FLC can be designed depending on the researcher’s experience in the controlled object, and it also has strong adaptability and robustness. In addition, it can be divided into traditional fuzzy strategy, fuzzy adaptive strategy and fuzzy forecasting strategy.

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Compared with deterministic rule control strategy, FLC can better control HESSs to adapt to different operating conditions. As FLC is based on experience, researchers usually adopt it in conjunction with other control strategies to improve its performance. Y.Z. Wang [51] proposed

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using a Markov chain to predict the system required power. The algorithm improved the fuzzy control effect according to the prediction and the real demand power value. The simulation and

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experiment results proved that this method is feasible and effective. C. Gao [52] modified the fuzzy controller by optimizing the control result, while S. Dusmez [48] achieved this by combining wavelet transform. The SOC of a UC has a significant impact on the operating performance of the HESSs, so the UC output power in the FLC power distribution can be corrected by the real vehicle speed [43]. To continuously update the fuzzy control function, W.H. Zhou [53] proposed the adaptive membership function based on historical information. Y.L. Murphey [54] used FLC combined with machine learning to optimize the control system. Moreover, researchers have also done a comparative analysis on HESS efficiency with different topologies [55]. 13

ACCEPTED MANUSCRIPT 3.1.3 Wavelet Transform Wavelet transform (WT) is widely used in signal processing since it is able to perform local signal analysis both in the time domain and frequency domain. In recent years, the WT algorithm has been widely applied in the energy management field due to its unique advantages [56]. The

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base functions in wavelet transform are not limited to one. The commonly used base functions are Haar wavelet function, Morlet wavelet function, Daubechies wavelet function and Meyer wavelet function, and among them, the Harr wavelet function is widely used for its practicality and

Ws x(t ) = x (t ) ⋅ ϕ s (t ) =

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convenience. The WT of the signal or target object x(t ) can be defined as follows:

1 +∞ t −u S (t )ϕ ( )du ∫ s −∞ s

(1)

s = 2j , j

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where, s denotes the scale factor, ϕ (t ) denotes the scaled signal through the scale factor s . If Z, then the WT at this time is also called second-order WT.

Researchers have used the WT algorithm to decompose the demand power into two parts, i.e. high frequency and low frequency. Combining with the power prediction algorithm, M. Ibrahim

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[57] used discrete WT as a time-frequency filter to control HESS. Q. Zhang [58] used a multi-layer Haar WT for fully active HESS power distribution, optimizing the UC voltage by constantly adjusting the frequency threshold. To optimize the output power distribution, in literature [48], the

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FLC based on the SOC of a UC and the demand power was used to correct the power distribution value of the three-layer WT algorithm.

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3.2 Optimization-based control strategy Optimization-based control strategy has high algorithmic complexity [41], [59], poor robustness and even can hardly be used in real-time control applications. Nevertheless, it can not only guide the design of rules or other optimized control strategies but also be applied online in the near future with the optimization of control algorithms. In recent years, researchers have done a great deal of work on the optimization control of HESSs. Among the various applications of the optimization control algorithm, the power loss of a HESS is usually seen as an optimization objective function:

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(2)

where PBat,loss denotes the power loss of battery; PUC,loss denotes the power loss of UC; PDC/DC,loss denotes the power loss of DC/DC.

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This section mainly compares and analyzes the research algorithms of optimal HESS energy management strategies. The algorithms less applied to HESSs are not analyzed in this paper, such as particle swarm optimization which is often applied to Plug-in HEVs [42] or fuel cell HEVs.

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3.2.1 Dynamic Programming

Dynamic programming (DP) was developed by Richard Bellman in the 1950s [39] including

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stochastic dynamic programming (SDP) and deterministic dynamic programming (DDP). For a deterministic system, DP can determine the optimal control input via the designed optimization objective function. Compared with other optimization control theories, DP has the advantage of being able to handle complex linear and nonlinear systems with multi-state and multi-input

as follows [60]:

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variables with global optimization. The optimization objective function for DDP can be described

(1) The cost function of the Nth step:

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J N ( x (i )) = g N ( x (i ))

(3)

(2) The cumulative cost function in step k (1 ≤ k ≤ N −1 ):

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J k ( x(i ) ) =

min

u ( j )∈U k ( x ( i ))

{g

k

( x(i), u ( j )) + J k +1 ( f k +1 )}

(4)

where g N ( x(i) ) denotes the cost of the end step, g k ( x (i ), u ( j )) denotes the cost of step k, f k +1 denotes the state variable at the step k+1, J k +1 ( f k +1 ) denotes the optimization objective function with the best energy consumption at step k+1, U k denotes the set of control variables, i and j denote the discrete points of the state variables and control variables, respectively. Due to the global optimality of DP, it is widely used in HESS control. By introducing a battery lifetime degradation model, A. Santucci [61] used DP to optimize system control, and the battery

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ACCEPTED MANUSCRIPT lifetime and system costs were optimized compared to a rule-based control strategy. Aiming at improving the battery lifetime of a HESS applied to Series Plug-in HEV, M. Masih-Tehrani [62] proposed a battery lifetime model to obtain the optimal battery and UC pack parameter values and the optimal system control strategy based on DP. Because of the decrease of battery performance

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and lifetime attenuation at low temperature, Z.Y. Song [63] proposed a battery lifetime degradation model under different temperatures and discharge depths, and optimized the HESS power distribution under different states through the DP algorithm.

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In addition, the HESS optimal results of DP can be used in other rule control strategies and optimization strategies for learning and training [64]. Based on the DP approach, R. Xiong [65]

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extracted a rules-based control strategy in order to achieve real-time application of the optimal control strategy in different operating conditions. Z.Y. Song [66] extracted suboptimal rules from the optimal results obtained by DP under multi-conditions, and the optimized rule control strategy has obvious advantages. J.Y. Shen [45] used results optimized by DP to train a neural networks

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model that could be applied online and achieved excellent results. This optimization training method provided good references for future HESS EMSs. At the same time, DP can also be used to validate HESS control strategies based on energy consumption [49] and make comparisons of

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different HESS structures [30].

Since DP is widely used in optimal system control, O. Sundstrom [67] designed and published a

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generic DP toolbox that can be run in Matlab. The future system information is needed when using DP in a HESS control application, so it is not suited for direct use for real-time applications and can only be used in optimal design and comparison of other rules. Furthermore, DP needs to traverse a large amount of data which leads to a heavy calculation burden, especially in cases of multiple states and multiple inputs. When the variables are too large and the computational grid is too thin, the calculation will dramatically increase, and "dimensional disaster" may even occur [59].

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ACCEPTED MANUSCRIPT 3.2.2 Genetic Algorithm Genetic algorithm (GA) is a computational model that simulates the biological evolution process of natural selection theory and genetic mechanism in Darwin's biological evolution theory. It is a method of searching the optimal solution by simulating the natural evolution process [40]. GA can

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deal with nonlinear, multi-model and multi-objective function optimization problems, and demonstrates strong versatility and robustness. It has the following characteristics: (1) GA starts searching from a string set, and it has wide coverage and can choose the global optimality. (2) GA

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can simultaneously process multiple individuals in parallel, which can effectively avoid local optimization. (3) GA uses the fitness function as the optimization goal without having to meet the

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conditions of a continuous differentiable. The domain can be set freely, and the application scope is very wide. (4) GA calculates individual survival probability through individual fitness, and the search direction does not need to be determined. (5) GA has the characteristics of self-organizing, self-adaptive and self-learning.

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As a type of global optimization algorithm, GA is also usually used in the EMS of HESSs. M. Wieczorek [47] proposed a real-time control algorithm that can reduce the charge-discharge rate of the battery in a HESS based on GA. Because the FLC relies too much on expert' experience and

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has poor performance in system control, S Khoobi [68] designed an optimal fuzzy control strategy based on GA and obtained good results. By introducing battery lifetime costs, the researchers used

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GA to optimize daily energy consumption and battery degradation costs of light-rail vehicles. The results showed that it can reduce operating costs by 13.9% [69]. In addition, GA has also been applied to parameter matching and optimization of multi-energy sources because of its ability to solve the multi-objective and nonlinear problems of the system [28], [70-71]. However, GA is difficult to deal with multi-object and nonlinear optimization problems in some cases [72]. At the same time, it belongs to a stochastic algorithm, which is prone to obtaining non-optimal solutions in practical applications.

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ACCEPTED MANUSCRIPT 3.2.3 Model Predictive Control Model predictive control (MPC), also known as receding horizon control (RHC), includes the establishment of predictive models, online optimization and feedback correction, as shown in Fig. 8. To bridge the gap of global optimization and real-time control, this algorithm was introduced

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into HESS control applications. The performance of MPC depends on two aspects: the prediction accuracy and optimization of the control strategy. To improve the control efficiency of a HESS and the optimize real-time and robustness of the system control strategy, researchers have established a

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HESS control strategy based on the prediction of vehicle driving conditions and system demand power. In the process of HESS control, MPC is used to predict the vehicle information of limited

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time-domain together with other methods, such as the Markov process [52], [73] and then optimizes the power distribution of the control system through an optimization algorithm such as

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−

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quadratic programming [49], [73], DP algorithm [74-75], etc.

Fig. 8. The logical structure of MPC.

Based on the MPC toolbox in Matlab, B. Hredzak [27] performed a power prediction and allocation of HESSs. Usually, in order to improve system control accuracy, it is necessary to establish a complex nonlinear system model [76] and refine the DCDC model [27]. Due to the computational complexity of complex models, it can only be validated by a high performance test rig [77]. Considering the complexity and applicability of MPC, many researchers have adopted a linear system model to reduce the MPC algorithm computation [41]. B. Hredzak [78] proposed

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ACCEPTED MANUSCRIPT using multiple low-order MPC models to control different component of a HESS separately, but such a method will reduce prediction and control accuracy. Aiming to reduce MPC algorithm computation, O. Gomozov [41] proposed the method of using non-uniform sampling times for different prediction conditions. Some papers also employed multi-control strategies for

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multi-power systems. For example, A. Santucci [61] proposed a method of hierarchical control of a HESS with the objective of reducing battery lifetime degradation and increasing system efficiency.

3.3 Artificial Intelligence-based Control Strategy

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S. Zhang [74] combined the rule-based strategy with MPC to distribute the power of a HESS.

Artificial intelligence (AI) algorithms are an important developing direction of system control in

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the years to come. Cross-modal subspace learning methods have been used to perform pattern recognition of complex photographs [79], and have reference value for vehicle condition prediction and energy management. Researchers have applied AI algorithms to HESSs. Neural networks (NNs) belong to basic AI algorithms, so NNs and reinforcement learning algorithms are classified into one

HESSs. 3.3.1 Neural Networks

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category. These algorithms represent the developing direction of intelligent algorithms applied to

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NNs resemble the processes of computing and thinking of the human brains, which are obtained by simulating the characteristics of neuronal activities in the human brain. The application of NNs

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requires extensive training data sets for optimal training. In essence, the learning of NNs is a means of inductive learning. Based on the repeated learning of a large number of instances, the internal adaptive algorithm continuously modifies the weight between neurons, making weight distributions gradually converge to a stable range [80]. The optimality of NNs depends on how much data are used for training [81]. NNs have the following basic characteristics: 1) high degree of parallelism; 2) high degree of non-linear global role; 3) excellent fault tolerance and associative memory; and 4) strong adaptive and learning ability [82]. A typical multi-layer feed-forward NN is presented in Fig. 9, where X 1~ n denotes the net input data, O denotes the net output data, Y denotes the training 19

ACCEPTED MANUSCRIPT output data during the training process, e denotes the error during the training process.

X1 ⋅⋅⋅ ⋅⋅⋅ ⋅⋅⋅

⋅⋅⋅

⋅⋅⋅

X3

O

⋅⋅⋅

⋅⋅⋅

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X2

Xn

− +

Y

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e

Fig. 9. NN schematic diagram.

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NNs can handle nonlinear problems well and have the ability to process system control at high speed. They are widely used in many aspects such as system control, pattern recognition, prediction and optimization [81], [83]. Researchers need to obtain plenty of optimal control data sets in advance when using a NN for a HESS. After training, the other parts of the data sets are used to

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verify the correctness of the NN. Based on this idea, researchers have done a great deal of work using NNs for HESS optimization. Using HESS power distribution data based on a DP algorithm, J. Shen [45] trained a NN controller to realize the real-time application of optimized data and

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real-time verification [84]. J Moreno [83] replaced the rules of the PI feedback system with data-trained NNs, resulting in the increase of efficiency by 3.3%. To improve the transient

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performance of HESS power distribution, R. Zgheib [85] adopted a NN for system control with an obvious effect. In addition, NNs also play an important role in speed prediction [58], [86]. However, the amount and quality of training as well as the suitability of the established NN models have a direct influence on the performance [80]. There is a need to measure the impact of multiple factors in establishing and training NNs. 3.3.2 Reinforcement Learning With the advancement of technology, new intelligent algorithms are widely applied in the field of computers, operations research and robotics. The intelligent algorithm is one of the important 20

ACCEPTED MANUSCRIPT development directions of future system control. Among them, reinforcement learning (RL) has been applied in HESS EMSs and performed well.

St

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rt

rt +1

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St +1

at

Fig. 10. Schematic diagram of RL.

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RL algorithm was proposed by M. L. Minsky in the 1950s [87]. With the breakthrough in mathematical basic research on RL, it has become one of the hot topics in the field of machine learning [88]. At present, the RL algorithm is widely used in the fields of system automatic control. RL refers to the method of controlling the system by observing and analyzing the current behavior of the control system and making the optimal decision by gradual learning or trial-and-error under

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the unknown system structures and parameters [89]. The framework is shown in Fig. 10, where St denotes the set of all states of the controlled object; at denotes the set of all actions in the Agent. The RL algorithm consists of the following physical quantities: 1) strategy: represents the mapping

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from state to action, which is the core of this algorithm; 2) reward r: evaluation on each state change and the selected action; and 3) value function: evaluation on strategy, which is the objective

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function in the optimal control problem. T. Liu [90] applied RL to the real-time energy management of hybrid systems. By comparing the simulation results of RL and DP algorithm, the effectiveness of the proposed real-time EMS is proved. Based on the RL algorithm which takes forgetting factors into consideration, R. Xiong [91] considers both battery lifetime and temperature changes in HESS EMSs. The results show that the RL-based algorithm can reduce energy loss by 16.8% compared with a rule-based control strategy. In addition, RL is also used in a HESS composed of fuel cells and batteries [92]. The main merits and demerits of the energy management strategies mentioned above are 21

ACCEPTED MANUSCRIPT summarized in Table 3. Table 3 A summary of the main control approaches applied to HESS. EMS

Main merits Online

Main demerits

application,

Application mode in EMS

low Parametric on-line calibration and

computational complexity, simple

Online application [43], [52], poor adaptive correction of the

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RBC control, strong robustness and

[57]

algorithm high reliability Global

optimality,

optimized

control benchmark, suitable for

Offline

Heavy calculation burden, cannot

multi-object

and

nonlinear

be used for online application

optimization problems

comparison for evaluation of the

and

effectiveness

problems, cannot be used for

the

EMS

algorithm

benchmark [30], [48]; extract

nonlinear

optimization

Offline

global

optimization

[46], [68]

online application

Accurate online application application,

low

Heavy calculation burden, require

Online application [27], [61],

accurate information prediction

[75-77]

Large amount of demand training

Online application [44], [81],

data, poor control stability

[84]

Larger calculation burden

Online application [89], [90]

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Online NN

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Difficult to handle multi-object

GA

MPC

[60], [62]; optimized control

control strategy [44], [63-65]

Global optimality, provides a

of

optimization

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DP

global

computational complexity

Real-time and robust features, RL

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excellent control performance

Researchers have made great achievements in the research of EMSs for HESSs in recent years.

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The current novel algorithms lack more verification and application in the actual situation. At the same time, the information of the future route was not considered in many EMSs designs, and it only assumed to have been given. Given the working conditions, the optimization of EMSs will lead to a significant increase in performance of HESSs. However, the working conditions are unknown, which makes many novel algorithms cannot be applied in practice.

4. Future development trends and challenges With the continuous improvement of related technologies [93], algorithms and the urgent need of further improving the accuracy and adaptability of EMSs, future research on HESS EMSs need to 22

ACCEPTED MANUSCRIPT focus on taking multi-factor infusion and multi-control strategy integration into consideration. As the control of a HESS is greatly affected by working conditions, with the development of intelligent vehicles, the realization of vehicle driving cycle prediction can greatly improve the EMS performance of a HESS [94].

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4.1 Multi-information infusion of EMS

Batteries and UCs are complex electrochemical systems, thus their performance degradation can be greatly influenced by different factors such as operating temperature, cut-off voltage,

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charge/discharge rate, cycle times, etc. [38], [95]. Since battery characteristics are greatly affected by temperature [96], control strategies can be designed either by introducing a battery temperature

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model or by comparing battery characteristics at different temperatures [28], [61]. M. Shams-Zahraei [97] took battery temperature into consideration in the control strategy. Using battery experimental results, R. Xiong [64], [91] considered both battery durability and temperature in an EMS. The battery state-of-health (SOH) changes with different charge/discharge cycles, rates

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and temperatures, which are shown in Fig. 11. As the control effect of HESS EMSs will degrade with the aging of batteries and UCs, the consideration of battery degradation is of great importance [28], [38], [61], especially the control strategy design under different aging characteristics of

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batteries.

Fig. 11. Battery SOH changes with different factors.

In addition, DC/DC converter, working conditions identification and HESS volume optimization also have a significant impact on the performance of EMSs. DC/DC converter mainly use the off-line look-up table of model parameters to represent its work efficiency in the literature, so

23

ACCEPTED MANUSCRIPT designing a model that reflects its characteristics to improve its simulation accuracy is also an important research project in HESS control [82]. In terms of working conditions identification, F. Soriano [98] completed this based on an intelligent algorithm and analytic algorithm. J. Wang [99] designed a working condition identification method based on learning vector quantization (LVQ)

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neural network. For HESS volume optimization, Z.Y. Song [100] comprehensively optimized the volume of UC packs and an EMS. 4.2 Integration of multi-control method

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Many experts and scholars have proposed various energy management control methods for HESSs. However, each method has its own unique merits and faults. Combining the advantages of

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each algorithm, EMSs integrating with a multi-control method have attracted more researchers' attention [45], [51], [54], [57]. For example, the biggest advantage of DP is that the global optimization an EMS can be obtained offline, but it is difficult to apply online in real time. The rule-based EMS has good real-time performance, but the design of rules mainly relies on

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experience and has poor adaptability. Therefore, the global optimal energy distribution of a HESS can be realized based on a DP algorithm initially, and then the relevant rules can be extracted based on the previous step. Thus the advantages of the two control methods can be utilized to achieve the

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optimal energy allocation of a HESS [101]. In addition, WT can extract fluctuation or abrupt signals from the signals from the perspective of frequency domain; thus optimizing the EMS of a

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HESS can be achieved by combining this advantage of WT with other control algorithm. Many joint control methods have been successfully applied to a HESS, e.g., WT and FLC [56], [102], WT and NN [103]. Artificial intelligence methods have also been applied to multi-control algorithms. Y.L. Murphey [54] trained the fuzzy controller through a machine learning algorithm to realize the optimal distribution of a multi-energy source system. Combining with multiple control algorithms can better control a HESS. However, it also increases the computational complexity of the system and puts forward higher requirements on the hardware system.

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ACCEPTED MANUSCRIPT 4.3 Combining with vehicle intelligent network Condition prediction and information identification have a great influence on vehicle powertrain control and HESS power optimization distribution [65]. With the application of Global Position System (GPS) and Geographic Information System (GIS) in vehicle systems, both vehicle

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condition prediction and speed prediction have become possible [104]. As shown in Fig. 12, to bridge the gap of actual conditions and virtual conditions, the road information is obtained and transmitted to the vehicle by GPS, and then the predicting condition data obtained through

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information processing can provide an important reference for the HESS power distribution. Researchers have made great progress in improving the efficiency of HESSs based on road

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prediction and terrain information [105-106]. The combination of road factors and control strategies can better reflect the real system work state, to achieve the purpose of coordinating the charge/discharge rate of the batteries [107]. The conclusion can be drawn that the prediction precision has a great impact on battery lifetime, system efficiency and performance of HESSs by

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studying the prediction information [108].

Fig. 12. Intelligent vehicle road information.

5. Conclusions This review systematically analyzes several aspects of HESSs, including topology classification and its merits and faults, EMSs and its strengths and weaknesses. Due to the energy loss of the 25

ACCEPTED MANUSCRIPT DC/DC converter, the system efficiency cannot be higher than the battery only powered system and has a potential to be improved by choosing a more appropriate control strategy of a HESS. More importantly, a superior energy management strategies can help to extend the battery service lifetime and reduce the system lifetime-cycle cost by controlling the battery charge/discharge rate

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appropriately. The efficiency, performance and lifetime of a HESS are affected by many factors, and it is difficult to meet the needs of practical application by using a single control strategy or simplifying system complexity. Therefore, the way of combining more than one control strategy to

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compensate for their weaknesses get more supported. Complex EMS considering a multi-factor and multi-control strategy is the future developing trend. In particular, the information of working

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condition is critical to system control. With the development of AI, the system control of a HESS has become inseparable from big data, information prediction and other intelligent technologies.

Abbreviations HESS

hybrid energy storage system

GA

genetic algorithm

energy storage system

MPC

model predictive control

HEV

hybrid electric vehicle

PSO

particle swarm optimization

ultracapacitor

SDP

stochastic dynamic programming

DDP

deterministic dynamic programming

EMS

energy management strategy

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UC

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ESS

electric vehicle

AI

artificial intelligence

DC

direct current

NN

neural network

state of charge

RL

reinforcement learning

SOC RBC

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EV

rule-based control strategy

SOH

state-of-health

fuzzy logical control

LVQ

learning vector quantization

WT

wavelet transform

GPS

Global Position System

DP

dynamic programming

GIS

Geographic Information System

FLC

26

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Acknowledgements This work was supported in part by the National Natural Science Foundation of China (Grant No. 51507012), Beijing Municipal Natural Science Foundation of China (Grant No. 3182035) and

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National Key Research and Development Program of China (Grant 2018YFB0104104). The systemic experiments of the lithium-ion batteries were performed at the Advanced Energy Storage and Application (AESA) Group, Beijing Institute of Technology.

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