Investigation of integrated uninterrupted dual input transmission and hybrid energy storage system for electric vehicles

Applied Energy 262 (2020) 114446

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Applied Energy journal homepage: www.elsevier.com/locate/apenergy

Investigation of integrated uninterrupted dual input transmission and hybrid energy storage system for electric vehicles

T

⁎

Weiwei Yanga, Jiageng Ruanb, , Jue Yanga, Nong Zhangc a

School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, China Beijing University of Technology, Beijing, China c Faculty of Engineering and IT, University of Technology Sydney, NSW, Australia b

H I GH L IG H T S

G R A P H I C A L A B S T R A C T

an integrated system with • Propose dual input gearbox and hybrid energy storage system.

the power loss and battery • Investigate lifespan via real-time energy control strategy.

the performance of the in• Evaluate tegrated system in terms of Life-cycle cost.

multi-objective genetic • Mixed-integer algorithm is applied in parameter optimization.

solutions are achieved to bal• Pareto ance the cost, energy loss and battery lifespan.

A R T I C LE I N FO

A B S T R A C T

Keywords: Uninterrupted dual input transmission Hybrid energy storage system Battery capacity degradation Life-cycle cost Mixed-integer multi-objective genetic algorithm

Transportation sector is one of the major sources of pollutants as it contributes more than 80% of CO, while almost all HC and 90% of NOx and PM. Although battery electric vehicles are well-known for reducing environmental pollution, the driving range and battery lifespan put a significant barrier to its large-scale commercialization. In this study, an integrated system, which includes an uninterrupted dual input transmission and hybrid energy storage system, is proposed to improve energy efficiency and extend battery lifespan. Given the limitations of dynamic programming in practice, a real-time optimal control strategy is designed to evaluate the power loss and battery capacity degradation of the proposed integrated system based on detailed mathematical models of individual powertrain components. To achieve a desirable trade-off between battery degradation, energy consumption, and acquisition cost, a mixed-integer multi-objective genetic algorithm is implemented to optimize the parameters of the hybrid energy storage system, while Pareto principal is adopted to find the proper solution according to different purposes. The simulation results reveal that the proposed integrated system shows the potential of saving 15.85%–20.82% of the energy consumption in typical driving cycles and more than 22.61%–31.11% Life-cycle cost compared with the single-ratio transmission-based battery electric vehicles. The selected Pareto front can further enhance Life-cycle cost from 26.53% to 28.13% in the HWFET cycle. It can be concluded that the integrated uninterrupted dual input transmission and hybrid energy storage system not only

⁎

Correspondent author.

https://doi.org/10.1016/j.apenergy.2019.114446 Received 28 September 2019; Received in revised form 17 December 2019; Accepted 21 December 2019 0306-2619/ © 2020 Elsevier Ltd. All rights reserved.

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W. Yang, et al.

can improve motor efficiency and reduce energy consumption, it also can extend the battery lifespan to decrease Life-cycle cost compared to conventional single-ratio battery-only EV.

1. Introduction

has been widely regarded as a secondary power storage component to make up a hybrid energy storage system (HESS) with a traditional electrochemical battery in BEV [19]. Furthermore, the SC shows higher power capacity and longer lifespan compared with a battery [20]. Previous studies have proven that the negative impact of heavy current on a battery can be mitigated to some extent with the help from a SC [21]. Hence, the advantages of battery and SC can be well-utilized in virtue of a well-designed power management strategy [22]. To achieve higher overall energy efficiency and longer battery lifespan, the energy management strategy of HESS should be further improved, specifically for the uncertain driving conditions, parameter matching, and various powertrain structures. Energy management strategy, which is responsible for distributing the power between battery and SC pack, plays an important role in achieving a balance between dynamics performance, energy consumption, and cost [23]. The HESS power allocation strategy design should follow three basic goals at the system-level [24], which are: (a) avoid excessive battery charging/discharging to extend its lifespan; (b) make good use of the supercapacitor’s fast charging/discharging ability; (c) reduce the weight and cost of HESS without compromising performance. Currently, the rule-based control strategies and optimal methods-based strategies are widely applied to the EMS design. For instance, the rule-based switching and frequency strategy is brought out to control battery peak power by limiting SC voltage and certain intermediary frequency, respectively [25]. However, some researchers noticed that it is difficult to balance the energy distribution and battery lifespan for rule-based solutions when facing different driving patterns, which usually cause observable deviations and unpredictable flaws. To overcome the shortcomings, some optimal method-based solutions are applied. For example, compared to the rule-based controller, the root means square (RMS) value of battery current can be reduced 6% or 10% via using the model predictive controller (MPC) and DP, respectively [26]. For a hybrid powertrain, Pontryagin’s minimum principle (PMP) based EMS shows it has the potential of optimizing power distribution between engine and HESS by more than 15% energy consumption decrease compared with the rule-based strategy [27]. Reinforcement learning (RL) has also been applied to maximize the energy efficiency and feasibility in real-time practicing, which takes both the battery lifespan and the energy loss of HESS into consideration [28]. However, the above optimal strategy requires large computational resources and might interfere with the response time and the appropriate vehicle control commands. Therefore, an online energy strategy is developed here to achieve energy distribution by calculating the minimum power consumption and battery capacity degradation at each moment. As aforementioned, not only EMS but also HESS parameters have an

Due to environmental issues systematically deteriorating, such as rising air pollution and fossil fuel shortage, new energy vehicles, such as battery electric vehicles (BEVs), plug-in hybrid electric vehicles (PHEVs), HEVs [1], and fuel cell vehicles (FCVs) [2] are being introduced to the market. Meanwhile, the use of electric vehicles batteries based on the concept of Smart Grid can reduce contamination and energy distribution loss via storing and distributing energy at homes, and distributing energy resources, among others [3]. Due to the merits of the electric powertrain systems, the conventional vehicles are gradually being replaced. Among them, BEV is regarded as the ideal solution to achieve zero-emissions [4]. However, considering the popularization of BEVs, it is significantly hindered by the limited driving range [5] and battery lifespan [6]; meanwhile, multi-speed transmissions and hybrid energy storage systems are widely accepted as promising options to overcome the above limitations through providing higher electric machine operating efficiency and a high-energy-high-power energy source with acceptable balance between cost, energy density and power density. Various electrified powertrains were proposed to relax drivers’ range anxiety by upgrading the single-ratio transmission (SRT) in BEVs, which is dominant in the current BEV market [7], to a multi-speed one. The reason is that the single-ratio and single-motor cannot adapt to complex and uncertain driving conditions, which usually bring out low efficiency. To reduce energy consumption, multi-speed automatic manual transmission (AMT) [8], dual-clutch transmission (DCT) [9], automatic transmission (AT) [10], continuously variable transmission (CVT) [11], and power-split transmission (PST) [12] have been investigated. However, all the above-mentioned gearboxes come with power interruption, complicated mechanical structures and increased manufacturing cost that put barriers to their wider application. To improve the system efficiency and dynamic performance, many researchers have concentrated on dual-motor and multi-speed systems. A dual-motor and two-speed system is proposed to improve fuel economy, extend driving range compared to the traditional single-motor and single-ratio system [13]. Zhu et al. have proven that the operating efficiency of the above powertrain can be higher than a single-motor and two-speed system [14]. However, one motor is connected with the fixed gear ratio in the above system, so it may not be optimal for any driving conditions. Then a dual-motor and four-speed transmission is designed to further optimize the overall efficiency of the system [15]. Unfortunately, the power interruption is inevitable in the shifting process because of the synchronizer. Therefore, a novel uninterrupted dual input transmission (UDIT) has been designed in our previous research [16], as shown in Fig. 1, to balance the energy efficiency, dynamic performance and financial cost. However, according to the results [17], although the shifting control strategies have been carefully designed, key parameters optimization and energy management strategy (EMS) have to be further improved including battery lifespan and financial cost and so on. It is noticed that the cycle life of the battery and the dynamic output power of the energy storage system play important roles in determining the performance of BEVs [18]. For a battery-only electric vehicle, with a large battery capacity, both the cost and weight of the battery take a great amount of the total cost and weight. However, when it comes to a smaller battery pack, the driving range per charge will be shortened noticeably while the sudden peak discharging or charging will significantly harm the battery lifespan. Given a supercapacitor (SC) can provide high charging/ discharging efficiency and wide power range, it

Fig. 1. The configuration of the proposed uninterrupted dual input transmission. 2

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influence on the performance indexes of EV. HESS parameters optimization is based on certain performance indexes to select the series and parallel numbers for battery cell and SC module, which includes finance cost and battery lifespan [29]. Dynamic programming (DP) has the potential to minimize the degradation of the battery for certain driving cycles, it is widely adopted to optimize the SC size and taken as the evaluation criteria for other optimizing methods, in terms of seriesparallel module numbers. Meanwhile, rule-based EMS is the most widely implemented optimizing method in practice in HESS control to improve the battery health. According to the reference [30], the battery life can be enhanced by 47% and 60% in a specific bus driving cycle with the help of SC. However, most of the rule-based EMS only focus on extending the battery lifetime, while the installation costs, components weight, and space were not investigated. To address the optimal design of multi-objective, direct weighting algorithm [21], Lagrange multiplier algorithm [31], particle swarm optimization (PSO) [32], and multiobjective genetic algorithm [33] are proposed. For instance, Shen et al. [21] propose an integrated optimization for HESS size and battery lifespan, which uses a direct weighting algorithm and reduces 76% of battery loss with the help of 72 SC modules in the urban driving cycle, while the penalty functions are related to the researcher’s behaviors which is hard to be optimized. Koltsaklis et al. applied a mixed-integer linear programming to optimize the operational planning of energy networks [34] and minimize the total cost of the energy [35]. Kabatepe et al. proposed a bi-criteria solution algorithm to generate the set of efficient solution for minimizing the costs and emissions [36]. Amini et al. applied the Lagrange multiplier algorithm to solve the optimal EV routing problem for the trade-off between charging stations’ electricity price and transportation network traffic conditions [37]. However, Lagrange multiplier algorithm only obtains the local optimal solution, and it is also related to the initial value; PSO algorithm is unstable, and the convergence results are affected by the population and parameter size. Meanwhile, genetic algorithm is robust and widely applied to solve the complex problem because it can obtain a global optimal solution without the initial condition. Therefore, multi-objective optimization can be applied to calculate the Pareto solutions for the trade-off of the contradicted objectives. It has been proven that the Pareto front based results indicate the fluctuation of battery current can be well controlled if a small part of SC cost is sacrificed [33]. However, few studies have been conducted on mixed-integer multi-objective optimization with respect to the uninterrupted dual input transmission and the hybrid energy storage system. To this end, non-dominated sorting genetic algorithm II (NSGA-II) is implemented in this study to comprehensively investigate the performance of the proposed UDIT&HESS. The optimal Pareto front can further reduce finance cost and battery capacity loss. It is clear from the above literatures review that most of the researches mainly focus on one or two of the performance indexes, i.e. powertrain system, energy storage system, and energy management strategy. However, the three parameters are inseparable. In this study, the authors aim at providing a comprehensive investigation of proposed integrated uninterrupted dual input transmission and hybrid energy storage system by covering all the above performance indexes of EV, which is illustrated in the graphical abstract. Specifically, the focus of this paper can be summarized as:

Battery & Supercapacitor 3rd

SY1

2nd SY2

1st

M2

M1

Out

Electric connection

Mechanical connection

Fig. 2. The configuration of the proposed integrated system.

and battery capacity degradation. 5. Mixed-integer multi-objective genetic algorithm is applied to obtain the Pareto optimal solution. The rest of this paper is organized as follows: Section 2 describes the mathematical models of the critical components in the proposed BEV powertrain; Section 3 demonstrates the performance of the proposed integrated uninterrupted dual input transmission and hybrid energy storage system in terms of the energy consumption and battery capacity degradation; Section 4 investigates the economic benefits of the proposed integrated system with Life-cycle cost; Section 5 presents the parameter optimization of the hybrid energy storage system to balance the energy efficiency, acquisition cost, and battery lifespan using mixed-integer multi-objective genetic algorithm. Finally, conclusions are summarized in Section 6. 2. Mathematical model The proposed integrated uninterrupted dual input transmission and hybrid energy storage system is demonstrated in Fig. 2, which is composed of two motors and a 3-speed automatic manual transmission. Given the system overall efficiency, the integrated system has 3 modes: only motor M1 drives, only motor M2 drives, motor M1 and motor M2 drive together. Implementation of the uninterrupted power shifting can be achieved by adjusting M1 speed and torque. The battery and supercapacitor power distribution follow the principle that minimize the battery capacity degradation. The specifications of the uninterrupted dual input transmission have been given in our previous research [16]. 2.1. Battery model The resistance model is widely used to reflect the battery charging and discharging characteristics because it not only guarantees accuracy but also reduces the complexity of model [38], as shown in Fig. 3. The battery state of charge (SOC) could be derived from the simplified battery model as

1. An integrated uninterrupted dual input transmission and hybrid energy storage system is proposed to boost energy efficiency without increasing system complexity. 2. Real-time energy control strategy is proposed to investigate the motor power loss and battery capacity degradation rate in typical driving cycles. 3. The Life-cycle cost is applied to evaluate the performance and mutual effect of the proposed integrated system compared to the singleratio transmission in a battery electric vehicle. 4. The parameter optimization of hybrid energy storage system is carried out to minimize the energy consumption, acquisition cost,

∙

SOC (t ) = −

1 Voc − Qb

Voc2 − 4Rb ∗ Pb (t ) 2Rb

(1)

where, Qb stands for the full battery capacity, Rb represents the internal resistance, VL is the external voltage, VOC is the open circuit voltage, and Pb (t ) is the battery power. Assuming the environment temperature is 27 °C, the relationship of 3

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W. Yang, et al.

I

CSC =

MSC·CSC mod ule NSC

RSC =

NSC·RSC mod ule MSC

VSC = NSC ·VSC

Rb

VL

Voc

SOC, and voltage and charging/discharging resistance of the battery cell is demonstrated in Fig. 4. Ignoring the difference of all the battery cells, the battery pack consists of a certain number of series and parallel cells, which can be illustrated as the following equations.

Qb = Mb·Qbcell Rb = Nb·Rbcell / Mb Voc = Nb·Vbcell

2.3. DC-DC model

(2)

where, Nb is the series numbers of battery cells, Mb is the parallel numbers of battery cells, Qbcell , Rbcell , Vbcell is the capacity, resistance, and voltage of battery cell, respectively. According to the previous research, the battery state of health (SOH) deteriorates gradually with time [39]. In this paper, an empirical formula-based Arrhenius degradation model, which was achieved on a LiFePO4 battery, is adopted to evaluate the battery capacity degradation.

To reduce charging/discharging current fluctuation and enhance efficiency, a DC-DC converter is usually installed with the supercapacitor to match up the high voltage. Ignoring the transient process of DC-DC, power, and energy efficiency are mainly studied, and a constant value is used in this paper. 2.4. Motor model Fig. 6 reveals the efficiency maps of the traction motor 1 (M1) and motor 2 (M2), respectively. As the motor can not only drive the truck but also act as a generator to recycle the brake energy, the calculated motor power should be different according to the discharging or charging process of the battery. The output power is:

Ea + B·CRate ) R·Ten (Ah ) z

15162 − 1516·CRate ) R·Ten (Ah )0.824

(3)

where, Qloss is the capacity loss of battery, A is a pre-exponential factor, Ea is the activation energy, B is the correction factor of discharge rate, CRate is the discharge rate, R is the Molar gas constant and the value is 8.314, Ten is the environment temperature, Ah is the Ah throughput, Z is the time factor.

Pmot =

Tmot ωmot Tmot ≥ 0 ⎧ ηm ⎨Tmot ωmot η Tmot < 0 r ⎩

(5)

where, Pmot , Tmot , ωmot is motor power, torque, and angular velocity, respectively, ηm is the discharging efficiency and ηr is for charging efficiency.

2.2. Supercapacitor model

3. Benefits of the integrated UDIT and HESS

RC model is widely applied to reflect the SC pack performance in energy storage system as its principal function is charging/discharging quickly in this study. The basic performance of the adopted SC pack relies on the characteristics of a single SC module and the series and parallel SC module numbers in the pack:

3.1. The real-time energy management strategy A real-time control strategy (RTCS) is proposed in this section,

3.9

0.08 Voltage

3.8

0.07

discharging rint

3.7

0.06

Voltage/V

charging rint 3.6

0.05

3.5

0.04

3.4

0.03

3.3

0.02

3.2

0.01

3.1

0

0.1

0.2

0.3

0.4

0.5

SOC

0.6

0.7

0.8

Fig. 4. The resistance and voltage index of the battery cell. 4

0.9

1

0

5LQt/ȍ

≈ 0.0032·e−(

(4)

where, NSC is the series numbers of SC modules, MSC is the parallel numbers of SC modules, CSCmodule , RSCmodule , VSCmodule is the capacity, resistance, voltage, energy of SC modules, respectively. CSC , RSC , VSC , ESC is the capacity, resistance, voltage, energy of SC pack, respectively. Fig. 5 illustrates the variation of SC capacity and resistance with the charging/discharging current at 27 °C. It can be seen that the SC capacity is almost constant and around 2900F while current fluctuates violently. The fluctuant range of SC resistance is small as well, which indicates the energy loss of SC is low even when the instantaneous charging/discharging current is large.

Fig. 3. The simplified battery model.

Qloss = Ae−(

mod ule

2 2 ESC = 0.5·CSC ·(VSC _ max − VSC _ min )

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10-4 10

2940 Capacity Rint

9.5

2900

9

5int/ȍ

Capacity/F

2920

2880 -300

-200

-100

0

100

Current/A

8.5 300

200

Fig. 5. The resistance and capacity index of the SC module.

0

2000

.8.8 47 1 .8 000. 078 0.75 0.72 0.69 0.66 0.63 4000

0.9

6000

8000

40

0.94

0.9

20 0

10000

0.

0.92

0.9 0.9

3 0.9

0.8 8

torque(N.m)

9 0.

50

9

60

0.92

3 0.9

torque(N.m)

0.87

100

0

(b)

80

0.88

150

0.9

(a)

0

0.9

8084 6 0.80. 2.8 .8.8 0 078 0. 0.76 0.74 0.72 0.7

2000

4000

speed(r/min)

6000

0.88

8000

10000

speed(r/min)

Fig. 6. The efficiency map of the motor (a) M1, and (b) M2. Table 1 Vehicle specifications of a typical B-class vehicle.

Driving cycles

Demand power /torque/speed

M1 power

M1+M2 power

M2 power

Minimum power at each instant

Parameter

Value

Unit

Vehicle mass Aero drag coefficient Rolling resistance coefficient Rolling radius Vehicle front areas Single-ratio Motor power Motor torque Motor speed Battery voltage Battery capacity

1400 0.28 0.013 0.302 2.47 5.738 36/72 120/240 3000/8000 360 70

kg – – m m2 kW Nm rpm V Ah

HESS

BEV

Table 2 Specification of the proposed integrated system. Battery power

Minimize battery degradation at each instant

SC power

Battery power

Component

Parameter

Value

Unit

Motor (M2)

Rate/peak power Rate/peak torque Max speed Rate/peak power Rate/peak torque Max speed Final ratio M1 ratio M2 ratio Voltage Capacity

15/30 40/80 10,000 23/45 82/160 10,000 3.93 2.15 2.15, 1.46, 1.03 350 20.7

kW Nm rpm kW Nm rpm – – – V F

Motor (M1)

Ratio

Battery lifespan

Fig. 7. The proposed real-time control strategy.

Supercapacitor

5

Applied Energy 262 (2020) 114446

speed/(km/h)

(a) 100 50 0

0

200

400

speed/(km/h)

(c) 100 50 0

0

0

0

500

1000 time/s

0

(f)

1500

1000

2000

600

800

1500

2000

50

(e)

500

1500

(d)

100

400 time/s

1000

50

0

50

time/s

500

100

0

(b)

100

600 800 1000 time/s

speed/(km/h)

speed/(km/h)

speed/(km/h)

speed/(km/h)

W. Yang, et al.

0

200

100 50 0

1500

0

500

time/s

1000 time/s

Fig. 8. The driving cycles, (a) NEDC, (b) FTP75, (c) LA92, (d) HWFET, (e) UDDS, (f) WLTP. 14000

50

SR total consumption power UDIT total consumption power SR motor loss power UDIT motor loss power

12000

UDIT&BAT

30

power/kW

10000

Power/kW

SR&BAT

40

8000 6000

20 10 0 -10

4000

-20

2000

-30

0

200

400

600

800

1000

1200

time/s

0

NEDC

FTP75

LA92

HWFET

UDDS

WLTP

Fig. 10. The comparison of battery demand power in NEDC.

Driving cycle Fig. 9. The comparison of energy loss between SR&BEV and UDIT&BEV.

4

Initial SOC

Final SOC of SRT& BEV

Final SOC of UDIT& BEV

Improvement

NEDC FTP75 LA92 HWFET UDDS WLTP

0.9 0.9 0.9 0.9 0.9 0.9

0.8389 0.8067 0.8045 0.7992 0.8390 0.7589

0.8525 0.8272 0.8213 0.821 0.8527 0.7818

1.59% 2.54% 2.09% 2.73% 1.64% 3.01%

Eletricity consumption/kWh

Table 3 The SOC comparison between single-ratio and dual input transmission in BEV. Cycle

SR&BAT UDIT&HESS

3.5

15.85%

3 19.76%

2.5 2

16.32%

19.18%

20.82%

19.98%

1.5 1 0.5 0

which is illustrated in Fig. 7. Due to the existence of two motors in the proposed UDIT, the RTCS is implemented to allocate demand power by calculating the minimum energy consumption at each moment. Based on the different demand power, the proposed UDIT can achieve the following operating modes: M2 drives, M1drives, and both M1 and M2 drive. Therefore, the output power of proposed UDIT system relies on the gear ratio and efficiency of motors, as shown in Eq. (6).

NEDC

FTP75

LA92 HWFET Driving cycle

UDDS

WLTP

Fig. 11. The comparison of electricity consumption in the typical driving cycle.

6

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W. Yang, et al.

10-3 4 Battery capacity loss/%

Table 4 The relationship between battery replacement cost and replacement time.

SR&BAT SR&HESS UDIT&BAT UDIT&HESS

3.5

nr _b

1

2

3

4

5

6

RC

2454.926

4897.759

7328.557

9747.38

12154.29

14549.34

3 2.5

Table 5 The manufacturing cost of transmission.

2 1.5 1

Powertrain

Single-ratio

Uninterrupted dual input transmission

MC

0

60.84

0.5 0

NEDC

FTP75

LA92 HWFET Driving cycle

UDDS

Table 6 The segmental parameters for Life-cycle cost.

WLTP

Parameter

Value

unit

Cb CSC CDC i RT Cele Top

890 7120 270 0.025 10 0.1 12

$/kWh $/kWh $/kW – years $/kWh h

ψ MCben

0.25 12.5

– $/kW(input motor)

Fig. 12. The comparison of battery loss of the integrated system.

Pd = PM1 + PM 2 Td = TM1·iM1 + TM 2·iM 2 ωM1 = ωd ·iM1 ωM 2 = ωd ·iM 2

(6)

where, Pd , Td , ωd stand for demand power, demand torque, and demand angular velocity, respectively; similarly, PM1, TM1, ωM1 represent M1 output power, torque, angular velocity; PM2 , TM2 , ωM2 stand for M2 output power, torque, angular velocity; iM1 represent M1 ratio; iM2 is M2 ratio. To minimize power consumption at each moment, the M1 torque is split from −160 Nm to 160 Nm and then M2 torque can be calculated via equation (6). Compared to DP, the calculated load of the proposed method can be accepted for real-time control. For the BEVs, battery is the unique energy source, so the battery output power can be got through M1 power, M2 power, and DC-DC efficiency, and then battery capacity loss is calculated by Eq. (3). For HESS, both battery and SC can supply energy to motors, therefore power allocation is optimized by minimizing battery degradation. The output voltage of SC varies from 0.5VSC _max to VSC _max , then SC output power can be obtained through Eq. (4). The other power is supplied by the battery.

Compared to the SRT&BEV, a real-time optimal control strategy is utilized to evaluate the potential of the proposed system (UDIT&HESS) because of the high computational cost of DP. A typical B-class vehicle is taken as the application of the proposed UDIT&HESS, whose specifications are given in Table 1 [11]. In terms of the proposed UDIT, the combination of M1 power and M2 power should be approximately equal to the motor power of SR& BEV. According to the previous research [40], the transmission ratio has been calculated. Therefore, the corresponding parameters of the various components for UDIT are presented in Table 2. Typical driving cycles are adopted to evaluate the performance of the proposed UDIT and HESS including New European Driving Cycle

(a)

200

SR&BAT

Current/A

100 0 -100

UDIT&HESS 0

200

400

600

800

40

Power/kW

1000

1200

1400

1000

1200

1400

time/s (b)

20 0 -20 -40

0

200

400

600

800

time/s Fig. 13. The variation of battery current of SRT&BEV and UDIT&HESS in LA92, (a) battery current, (b) SC power. 7

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Table 7 The Life-cycle cost for the typical driving cycles.

Table 8 The energy loss during HWFET driving cycles.

System

AC

OC

nr _b

LCC

NEDC

SRT&BEV UDIT&HESS SRT&BEV UDIT&HESS SRT&BEV UDIT&HESS SRT&BEV UDIT&HESS SRT&BEV UDIT&HESS SRT&BEV UDIT&HESS

4669.83 4876.83 4669.83 4876.83 4669.83 4876.83 4669.83 4876.83 4669.83 4876.83 4669.83 4876.83

503.71 398.86 486.75 390.54 652.27 545.84 1272.97 1028.83 436.01 348.88 792.38 666.74

2 1 2 1 4 2 5 3 2 1 6 4

10071.3 7791.45 10054.34 7783.13 15069.48 10381.27 18097.09 13295.06 10003.6 7741.47 20011.55 15351.8

FTP75 LA92 HWFET UDDS WLTP

Enhance

Performance

SRT&BEV

UDIT&HESS

Improve

22.64%

Energy consumption/ kWh Battery capacity loss/%

2.4732 0.0022

1.9985 0.00153

19.19% 30.45%

22.59% 30

31.11% 20

26.53%

10

22.61%

Power/kW

Cycle

23.28%

0 -10 battery power of SRT&BEV

Start

battery power of UDIT&HESS

-20

SC power of UDIT&HESS -30

Select candidate optimized vector

0

100

200

300

400

500

600

700

800

time/s

Fig. 16. The power allocation of the SRT&BEV and UDIT&HESS. Real-time control strategy

Energy consumption

Table 9 The Life-cycle cost of the Pareto solution for HWFET driving cycles.

Battery capacity loss

Acquisition cost

System

AC

OC

nr _b

LCC

Improve

SRT&BEV UDIT&HESS

4714.86 4913.01

1272.76 1028.47

5 3

18141.91 13037.72

– 28.13%

No

Calculate multiobjective function

3.2. The improvement of energy efficiency As the efficiency range of the traction motor varies from 60% to 95%, it provides an opportunity to save energy by improving operating tracks in cycles. Compared to the SRT&BEV, Fig. 9 demonstrates the power loss of the proposed uninterrupted dual input transmission with BEV (UDIT&BEV) in the driving cycles. It can be seen that both the total consumption power and the motor power loss of UDIT&BEV are lower than for the SR counterpart. This is because the two motors can work independently or together to accommodate the various speed fluctuation with higher overall efficiency. To comprehensively evaluate the overall efficiency of the proposed UDIT, the final SOC values calculated in each single driving cycle are depicted in Table 3. The battery energy consumption can be respectively reduced by 1.59%, 2.54%, 2.09%, 2.73%, 1.64%, and 3.01%, respectively through the adoption of UDIT in each cycle as shown in Fig. 10 from left to right. Take the NEDC as an example shown in Fig. 10, the battery can output less power and recover more power with UDIT in NEDC. Therefore, the proposed high-efficiency UDIT can extend the driving range of an EV. The above simulation results have shown that the proposed UDIT can improve the energy efficiency to some extent. However, HESS has an influence on power allocation between battery and SC, which also causes the difference of energy consumption. Therefore, the total electricity consumption of the two powertrains is shown in Fig. 11. It can be seen that the proposed UDIT&HESS can decrease electricity consumption by 20.82%, 19.76%, 16.32%, 19.18%, 19.98%, and 15.85% in the given driving cycles, respectively.

Update Pareto solution

Converge?

Yes

End

Fig. 14. The flowchart of the mixed-integer multi-objective genetic optimization.

Fig. 15. The scatter gram of the first front.

(NEDC), EPA Federal Test Procedure (FTP-75), California Unified Cycle (UC, LA92), Highway Fuel Economy Test (HWFET), Urban Dynamometer Driving Schedule (UDDS), and Worldwide harmonized Light vehicles Test Procedure (WLTP). The driving cycles are shown in Fig. 8.

3.3. The improvement of battery capacity degradation As previously mentioned, battery technology is the limiting factor and seriously restricts the development of EV because of the battery cost and degradation. Therefore, the battery capacity loss must be 8

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MCSR = 0 MCUDIT = RSP·MCben·CRF RSP = 0.0183·(irmax ·Tmot )0.512 ·z 0.256

another performance index to evaluate the system merits. According to Eq. (3), the calculation of the battery degradation is shown in Fig. 12. Compared to the SRT&BEV, the proposed UDIT&BEV can reduce power consumption to improve battery lifespan by enhancing motor efficiency. It can be seen that HESS can effectively reduce the battery capacity loss as shown in Fig. 12 by comparing SRT&BEV and SRT&HESS or UDIT&BEV and UDIT&HESS. Due to the improvement of energy efficiency and application of the auxiliary SC, the battery degradation of UDIT&HESS is clearly lower than the other system as shown in Fig. 12. Take the LA92 driving cycle as an example, the variations of the battery current demonstrate why the proposed UDIT&HESS can decrease the battery capacity loss via comparing the SRT&BEV and UDIT& HESS. Compared to SRT&BEV, the battery current fluctuation of the proposed UDIT&HESS is less frequent, and the amplitude is smaller, which leads to a longer battery lifespan. The auxiliary SC replace the battery to supply or absorb some impulse power, which can be seen in Fig. 13(b).

(10)

where, MC is manufacturing cost, $/year, MCben is the bench price of transmission, irmax is the maximum ratio of the transmission, z is the gear ratio number. In conclusion, Life-cycle cost (LCC) can be expressed as Eq. (11), and the segmental parameters are given in Table 6 [42].

LCC = AC + OC + RC + MC

(11)

Based on the above calculation, the results of LCC for the five driving cycles are shown in Table 7. Due to the presence of SC, the initial acquisition cost of UDIT&HESS is higher, while the operating cost and battery replacement time are significantly lower, over 20% in this study, which offers a better ownership cost. 5. Parameter optimization of hybrid energy storage system

4. Life-cycle cost

Parameter optimization of HESS means the selection of the series and parallel numbers between the battery cell and SC module, which depends on dynamic performance, energy consumption, and the cruising range of electric vehicles. In this paper, to balance the performance between energy efficiency, acquisition cost and battery capacity degradation, mixed-integer multi-objective genetic algorithm is applied to optimize HESS parameters. The optimization is demonstrated in the Fig. 14. The objective function can be explained as follows.

Although the proposed UDIT and HESS can improve energy efficiency and battery lifespan, the initial cost is another challenge for large scale commercialization of EV. To further investigate the benefits of the proposed UDIT&HESS, Life-cycle cost (LCC) is proposed to evaluate the financial cost [41]. Generally, LCC mainly consists of the acquisition cost, operating cost, and replacement costs. However, considering the difference of transmission in this paper, the manufacturing cost of UDIT and SRT is also considered in LCC. Given that the lifespan of various components is different, capital recovery factor (CRF) is applied to alleviate the difference in time. Battery cost, SC cost, and DC-DC convert are a very large part of the acquisition cost; therefore, the following equations are adopted to express their values:

min J = [JOC , JAC , JB] → x

= [JAOC , JB] JAOC = JOC + JAC →

x = (ns _b, np _b, ns _SC , np _SC )

where, J is the multi-objective optimization index, JAOC is initial cost, JB is battery capacity loss, → x is optimized vector, ns _b is the battery series numbers, np _b is the battery parallel numbers, ns _sc is the SC series numbers, np _sc is the SC parallel numbers. Given the voltage of the motor controller varies from 250 V to 400 V, the range of battery series numbers can be set between 80 and 110. To satisfy the cruising range while considering the limitation of motor peak power and cost, the battery capacity should be larger than 65Ah, and less than 95Ah. Therefore, the battery parallel numbers can be selected from 33 to 48. Due to the efficiency of DC-DC convertor being related to the voltage ratio across it, SC voltage should be close to the battery voltage to achieve max DC-DC efficiency. Therefore, SC voltage should be less than 400 V, i.e. SC series number is less than 160. To meet the vehicle dynamic performance, SC demanded power is higher than the gap of motor peak power and battery power. SC energy can be calculated by Eq. (4), and then SC series number is limited to be more than 100. In other words, SC series number varies from 100 to 160, while the parallel number is set at 1 in this paper. Due to HWFET requiring the most replacement time except WLTP, as shown in Table 7, HWFET is assigned to implement in parameter optimization of HESS. By setting population size (60), Pareto-fraction (0.3), and generations (1 0 0), a mixed-integer multi-objective genetic algorithm is used to calculate the Pareto optimal solutions. Fig. 15 reveals the noninferiority solutions of the mixed-integer multi-objective genetic algorithm and the Pareto solutions distribution uniformity. It can be seen that the Pareto solutions cannot minimize both of the initial cost and battery capacity degradation at the same time. Comprehensively considering the battery replacement cost, and initial cost, the authors select the battery series numbers, battery parallel numbers, and SC series numbers as 99, 36, and 134, respectively, which is pointed out by the arrow in Fig. 15. According to the selected Pareto optimal solution in the HWFET

AC = (ACb + ACSC + ACDC )·CRF ACb = Cb·ns _b·np _b·Eb ACSC = CSC ·ns _SC ·np _SC ·ESC ACDC = CDC ·PDC CRF =

i·(1 + i) RT (1 + i) RT − 1

(7)

where, AC is acquisition cost, $/year, ACb is battery price, ACSC is SC price, ACDC is DC convert price, i is the interest rate, RT is useful life. The operating cost means the electricity bills of the vehicle working process, and it can be calculated as:

OC =

(ΔEb + ΔESC )·Cele ·Top·ψ T

(8)

where, OC is operating cost, $/year, Cele is the electricity price, T is a cycle time, Top is vehicle operating time, ψ is the mean utilization. Compared to SC and DC-DC convert, the battery lifespan is shorter, and so the paper focuses on the battery replacement costs, which has been given as Eq. (9), and the values are calculated as shown in Table 4. nr _b

RC = ∑ (1 + i)−j·0.2 ·ACb·CRF j=1

nr _b = ceil (Qloss _b/20% − 1) Qloss _b =

Qloss ·Top·ψ·RT T

(12)

(9)

where, RC is the replacement cost, $/year, nr _b is the battery replacement time.According to previous research [4], the relative selling price (RSP) of the transmission is applied to evaluate the UDIT manufacturing cost. Assume SR transmission cost is 0, and the UDIT’s price is approximately equal to the price of 3-AMT. Therefore, the manufacturing cost can be obtained by Eq. (9), and the values are demonstrated in Table 5. 9

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(No.201706460069).

driving cycle, the merits of the proposed UDIT&HESS are reappraised compared to SRT&BEV. The energy consumption and battery capacity degradation are given in Table 8. The simulation results demonstrate that the proposed UDIT&HESS can improve energy efficiency by 19.19% and reduce battery capacity degradation by 30.45%. The power distribution of the two powertrains is shown in Fig. 16. Compared to SRT&BEV, the proposed UDIT&HESS can absorb the instantaneous high power, especially in the braking stage. Due to the benefits of auxiliary SC, the battery power of the proposed hybrid system is clearly smaller than SRT&BEV. That is why the battery lifespan can be improved greatly. Compared to the previous HESS parameter, battery capacity of selected Pareto optimal solution increases from 25.2kwh to 25.66kwh, and SC capacity reduces to 0.253kwh accordingly. However, the battery capacity loss remains almost unchanged. Based on Pareto solution, Lifecycle cost of the two powertrains are calculated and presented in Table 9. We can see that the Life-cycle cost of the proposed UDIT&HESS can be reduced by 28.13% compared with SRT&BEV, and the Pareto solution can reduce financial cost by from 26.53% to 28.13%.

References [1] Cao Jian, Emadi A. A new battery/ultracapacitor hybrid energy storage system for electric, hybrid, and plug-in hybrid electric vehicles. IEEE Trans Power Electron 2012;27:122–32. https://doi.org/10.1109/TPEL.2011.2151206. [2] Sorrentino M, Capaldo D. Simultaneous optimization of real-time control strategies and powertrain design for fuel cell hybrid vehicles. SAE Tech. Pap. Ser., vol. 1, SAE International; 2019. doi:10.4271/2019-24-0199. [3] Cardenas JA, Gemoets L, Rosas JHA, Sarfi R. A literature survey on smart grid distribution: an analytical approach. J Clean Prod 2014;65:202–16. [4] Ruan J, Walker P, Zhang N. A comparative study energy consumption and costs of battery electric vehicle transmissions. Appl Energy 2016;165:119–34. https://doi. org/10.1016/j.apenergy.2015.12.081. [5] Schäuble J, Kaschub T, Ensslen A, Jochem P, Fichtner W. Generating electric vehicle load profiles from empirical data of three EV fleets in Southwest Germany. J Clean Prod 2017;150:253–66. [6] Wu J, Liang J, Ruan J, Zhang N, Walker PD. A robust energy management strategy for EVs with dual input power-split transmission. Mech Syst Signal Process 2018;111:442–55. [7] Wu G, Zhang X, Dong Z. Powertrain architectures of electrified vehicles: Review, classification and comparison. J Franklin Inst 2015;352:425–48. https://doi.org/10. 1016/J.JFRANKLIN.2014.04.018. [8] Tseng C-Y, Yu C-H. Advanced shifting control of synchronizer mechanisms for clutchless automatic manual transmission in an electric vehicle. Mech Mach Theory 2015;84:37–56. https://doi.org/10.1016/j.mechmachtheory.2014.10.007. [9] Ruan J, Walker PD, Zhang N, Wu J. An investigation of hybrid energy storage system in multi-speed electric vehicle. Energy 2017;140:291–306. https://doi.org/ 10.1016/J.ENERGY.2017.08.119. [10] Roozegar M, Setiawan YD, Angeles J. Design, modelling and estimation of a novel modular multi-speed transmission system for electric vehicles. Mechatronics 2017;45:119–29. [11] Ruan J, Son Q, Yang W. The application of hybrid energy storage system with electrified continuously variable transmission in battery electric vehicle. Energy 2019;183:315–30. [12] Wu J, Liang J, Ruan J, Zhang N, Walker PD. Efficiency comparison of electric vehicles powertrains with dual motor and single motor input. Mech Mach Theory 2018;128:569–85. https://doi.org/10.1016/j.mechmachtheory.2018.07.003. [13] Ruan J, Song Q. A novel dual-motor two-speed direct drive battery electric vehicle drivetrain. IEEE Access 2019;7:54330–42. [14] Zhu B, Zhang N, Walker P, Zhan W, Yueyuan W, Ke N, et al. Two motor two speed power-train system research of pure electric vehicle; 2013. [15] Holdstock T, Sorniotti A, Everitt M, Fracchia M, Bologna S, Bertolotto S. Energy consumption analysis of a novel four-speed dual motor drivetrain for electric vehicles. In: 2012 IEEE Veh. Power Propuls. Conf.; 2012. p. 295–300. [16] Liang J, Yang H, Wu J, Zhang N, Walker PD. Shifting and power sharing control of a novel dual input clutchless transmission for electric vehicles. Mech Syst Signal Process 2018;104:725–43. [17] Liang J, Yang H, Wu J, Zhang N, Walker PD. Power-on shifting in dual input clutchless power-shifting transmission for electric vehicles. Mech Mach Theory 2018;121:487–501. [18] Chong LW, Wong YW, Rajkumar RK, Rajkumar RK, Isa D. Hybrid energy storage systems and control strategies for stand-alone renewable energy power systems. Renew Sustain Energy Rev 2016;66:174–89. https://doi.org/10.1016/J.RSER. 2016.07.059. [19] Kollimalla SK, Mishra MK, Narasamma NL. Design and analysis of novel control strategy for battery and supercapacitor storage system. IEEE Trans Sustain Energy 2014;5:1137–44. https://doi.org/10.1109/TSTE.2014.2336896. [20] Song Z, Li J, Hou J, Hofmann H, Ouyang M, Du J. The battery-supercapacitor hybrid energy storage system in electric vehicle applications: A case study. Energy 2018;154:433–41. https://doi.org/10.1016/J.ENERGY.2018.04.148. [21] Shen J, Dusmez S, Khaligh A. Optimization of sizing and battery cycle life in battery/ultracapacitor hybrid energy storage systems for electric vehicle applications. IEEE Trans Ind Informatics 2014;10:2112–21. https://doi.org/10.1109/TII.2014. 2334233. [22] Song Z, Hofmann H, Li J, Hou J, Han X, Ouyang M. Energy management strategies comparison for electric vehicles with hybrid energy storage system. Appl Energy 2014;134:321–31. https://doi.org/10.1016/j.apenergy.2014.08.035. [23] Wieczorek M, Lewandowski M. A mathematical representation of an energy management strategy for hybrid energy storage system in electric vehicle and real time optimization using a genetic algorithm. Appl Energy 2017;192:222–33. https://doi. org/10.1016/J.APENERGY.2017.02.022. [24] Li J, Xiong R, Mu H, Cornélusse B, Vanderbemden P, Ernst D, et al. Design and realtime test of a hybrid energy storage system in the microgrid with the benefit of improving the battery lifetime. Appl Energy 2018;218:470–8. https://doi.org/10. 1016/j.apenergy.2018.01.096. [25] Allègre A-L, Bouscayrol A, Trigui R. Flexible real-time control of a hybrid energy storage system for electric vehicles. IET Electr Syst Transp 2013;3:79–85. [26] Santucci A, Sorniotti A, Lekakou C. Power split strategies for hybrid energy storage systems for vehicular applications. J Power Sources 2014;258:395–407. https://doi. org/10.1016/j.jpowsour.2014.01.118. [27] Vinot E, Trigui R. Optimal energy management of HEVs with hybrid storage system. Energy Convers Manag 2013;76:437–52. https://doi.org/10.1016/j.enconman. 2013.07.065.

6. Conclusion An integrated uninterrupted dual input transmission and hybrid energy storage system is studied in this paper. A real-time energy management strategy is proposed not only for the power distribution optimization of the two motors but also for the hybrid energy storage system, through minimizing the total energy consumption and battery capacity degradation at each instant, respectively. Compared to our previous research, the proposed uninterrupted dual input transmission with the optimal control strategy can improve drivability via eliminating the torque hole, and, at the same time, enhance energy efficiency through reducing motor power loss. Specifically, compared to the single-ratio transmission-based battery-only electric vehicles, the simulation results reveal that the proposed integrated system can reduce electricity consumption up to 20.82% in the six typical driving cycles. In terms of battery lifespan improvement, investigation results demonstrate that the integrated system can be extended up to 31.11% when compared to the battery-only electric vehicles. Due to the battery replacement cost making up a large proportion in Life-cycle cost, a mixed-integer multi-objective genetic algorithm is adopted to optimize hybrid energy storage system parameters. According to the selected Pareto solution, the optimal integrated system can further reduce the Life-cycle cost from 26.53% to 28.13% in the HWFET cycle compared to the non-optimized integrated system. In conclusion, compared to the popular single-ratio battery electric vehicle, the integrated uninterrupted dual input transmission and hybrid energy storage system will improve energy efficiency, and decrease the Life-cycle cost by extending the battery lifespan. CRediT authorship contribution statement Weiwei Yang: Conceptualization, Methodology, Software, Investigation, Resources, Data curation, Writing - original draft. Jiageng Ruan: Conceptualization, Methodology, Software, Investigation, Resources, Data curation, Writing - review & editing, Supervision. Jue Yang: Writing - review & editing, Supervision, Project administration, Funding acquisition. Nong Zhang: Project administration, Funding acquisition. Declaration of Competing Interest None. Acknowledgments This work has been supported by the China Scholarship Council 10

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scheduling of microgrids. Chem Eng Res Des 2018;131:318–32. [36] Kabatepe B, Türkay M. A bi-criteria optimization model to analyze the impacts of electric vehicles on costs and emissions. Comput Chem Eng 2017;102:156–68. [37] Amini MH, Karabasoglu O. Optimal operation of interdependent power systems and electrified transportation networks. Energies 2018;11:196. [38] Yang W, Liang J, Yang J, Zhang N. Optimal control of a novel uninterrupted multispeed transmission for hybrid electricmining trucks. Proc Inst Mech Eng Part D J Automob Eng 2019;233:3235–45. https://doi.org/10.1177/0954407018821524. [39] Oruganti PS, Jung D, Arasu M, Ahmed Q, Rizzoni G. Optimal energy management in a range extender PHEV using a cascaded dynamic programming approach. In: ASME 2018 Dyn. Syst. Control Conf.; 2018. [40] Wu H, Walker P, Wu J, Liang J, Ruan J, Zhang N. Energy management and shifting stability control for a novel dual input clutchless transmission system. Mech Mach Theory 2019;135:298–321. https://doi.org/10.1016/J.MECHMACHTHEORY.2019. 01.032. [41] Fuller S, Petersen S. Life-cycle costing manual for the federal energy management program, NIST Handbook 135, 1995 Edition | NIST. Handb (NIST HB) - 135 1996. [42] Herrera V, Milo A, Gaztañaga H, Etxeberria-Otadui I, Villarreal I, Camblong H. Adaptive energy management strategy and optimal sizing applied on a battery-supercapacitor based tramway. Appl Energy 2016;169:831–45. https://doi.org/10. 1016/J.APENERGY.2016.02.079.

[28] Xiong R, Cao J, Yu Q. Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle. Appl Energy 2018;211:538–48. https://doi.org/10.1016/j.apenergy.2017.11.072. [29] Song Z, Hofmann H, Li J, Hou J, Zhang X, Ouyang M. The optimization of a hybrid energy storage system at subzero temperatures: Energy management strategy design and battery heating requirement analysis. Appl Energy 2015;159:576–88. https://doi.org/10.1016/j.apenergy.2015.08.120. [30] Song Z, Hofmann H, Li J, Han X, Ouyang M. Optimization for a hybrid energy storage system in electric vehicles using dynamic programing approach. Appl Energy 2015;139:151–62. https://doi.org/10.1016/J.APENERGY.2014.11.020. [31] Boyd S, Parikh N, Chu E, Peleato B, Eckstein J. others. Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends®in Mach Learn 2011;3:1–122. [32] Lu X, Zhou K, Yang S. Multi-objective optimal dispatch of microgrid containing electric vehicles. J Clean Prod 2017;165:1572–81. [33] Song Z, Li J, Han X, Xu L, Lu L, Ouyang M, et al. Multi-objective optimization of a semi-active battery/supercapacitor energy storage system for electric vehicles. Appl Energy 2014;135:212–24. https://doi.org/10.1016/j.apenergy.2014.06.087. [34] Koltsaklis NE, Kopanos GM, Georgiadis MC. Design and operational planning of energy networks based on combined heat and power units. Ind Eng Chem Res 2014;53:16905–23. [35] Koltsaklis NE, Giannakakis M, Georgiadis MC. Optimal energy planning and

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