Flexible predictive power-split control for battery-supercapacitor systems of electric vehicles using IVHS

doi:10.23919/JSEE.2023.000013

Journal of Systems Engineering and Electronics ›› 2023, Vol. 34 ›› Issue (1): 224-235.doi: 10.23919/JSEE.2023.000013

• CONTROL THEORY AND APPLICATION • Previous Articles Next Articles

Flexible predictive power-split control for battery-supercapacitor systems of electric vehicles using IVHS

Defeng HE¹^,*(), Jie LUO¹(), Di LIN²(), Shiming YU¹()

¹ College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
² BYD Company Limited, Huizhou 516083, China

Received:2022-01-27 Online:2023-02-18 Published:2023-03-03
Contact: Defeng HE E-mail:hdfzj@zjut.edu.cn;2111903059@zjut.edu.cn;2111803060@zjut.edu.cn;ysm@zjut.edu.cn
About author:
HE Defeng was born in 1979. He received his bachelor ’s degree in thermal energy and power engineering from Central South University, Changsha, China, in 2001 and doctorate degree in control science and engineering from University of Science and Technology of China, Hefei, China, in 2008. Since 2015, he has been a full professor at Zhejiang University of Technology, Hangzhou, China. He has been the author of more than 100 research publications and has authorized more than 30 Chinese invention patents. His research interests include intelligent prediction and optimal control of autonomous systems. E-mail: hdfzj@zjut.edu.cn

LUO Jie was born in 1997. He received his B.S. degree in automation from Zhejiang University of Technology, Hangzhou, China, in 2019. He is currently pursuing his Ph.D. degree in control science and engineering from Zhejiang University of Technology, Hangzhou, China. His research interests include model predictive control and its applications to connected and automated vehicles. E-mail: 2111903059@zjut.edu.cn

LIN Di was born in 1993. He received his M.S. degree in control science and engineering from Zhejiang University of Technology, Hangzhou, China, in 2021. He is currently a full engineer at the BYD Company Limited, Huizhou, China. His research interests include model predictive control and its applications to connected and automated vehicles. E-mail: 2111803060@zjut.edu.cn

YU Shiming was born in 1962. He received his Ph.D. degree in control theory and control engineering from Zhejiang University, Hangzhou, China, in 2001. He is currently a full professor at Zhejiang University of Technology, Hangzhou, China. His research interests include model predictive control and system identification. E-mail: ysm@zjut.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (62173303), and the Fundamental Research for the Zhejiang Provincial Universities (RF-C2020003)

Abstract

Abstract:

The utilization of traffic information received from intelligent vehicle highway systems (IVHS) to plan velocity and split output power for multi-source vehicles is currently a research hotspot. However, it is an open issue to plan vehicle velocity and distribute output power between different supply units simultaneously due to the strongly coupling characteristic of the velocity planning and the power distribution. To address this issue, a flexible predictive power-split control strategy based on IVHS is proposed for electric vehicles (EVs) equipped with battery-supercapacitor system (BSS). Unlike hierarchical strategies to plan vehicle velocity and distribute output power separately, a monolayer model predictive control (MPC) method is employed to optimize them online at the same time. Firstly, a flexible velocity planning strategy is designed based on the signal phase and time (SPAT) information received from IVHS and then the Pontryagin’s minimum principle (PMP) is adopted to formulate the optimal control problem of the BSS. Then, the flexible velocity planning strategy and the optimal control problem of BSS are embedded into an MPC framework, which is online solved using the shooting method in a fashion of receding horizon. Simulation results verify that the proposed strategy achieves a superior performance compared with the hierarchical strategy in terms of transportation efficiency, battery capacity loss, energy consumption and computation time.

Key words: electric vehicle (EV), model predictive control (MPC), Pontryagin’s minimum principle (PMP), power-split

Defeng HE, Jie LUO, Di LIN, Shiming YU. Flexible predictive power-split control for battery-supercapacitor systems of electric vehicles using IVHS[J]. Journal of Systems Engineering and Electronics, 2023, 34(1): 224-235.

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Parameter	Value
m/kg	1550
ρ/(kg·m⁻³)	1.23
A/m²	2.68
C_r	0.014
C_d	0.275

Component	Parameter	Value
Battery pack	V_b/V	312
	Q/Ah	90
	R_b/Ω	0.3
Supercapacitor pack	V_sc,max/V	310
	C_sc/F	2500
	R_sc/Ω	0.01

Parameter	Value	Parameter	Value
(u_min/u_max)/N	3100	(v_min/v_max)/(m·s⁻¹)	0/22
(SOC_b,min/SOC_b,max)/%	20/100	SOC_sc,ref/%	75
(P_b,min/P_b,max)/kW	−20/35	λ₀	0.1
(SOC_sc,min/SOC_sc,max)/%	50/100	N_p	20
(P_sc,min/P_sc,max)/kW	−105/105	N_s	10
ζ	0.001	Δt	1

Signal light	Initial state/s	Interval (t_r/t_g)/s	Distance/m
1st	r-26	45/20	210
2nd	g-2	40/25	428
3rd	g-18	45/20	460
4th	g-3	20/45	490
5th	g-6	40/25	357
6th	r-24	45/20	255

Signal light	Initial state/s	Interval (t_r/t_g)/s	Distance/m
1st	r-16	42/13	451
2nd	r-12	38/15	376
3rd	r-31	43/14	414
4th	g-14	39/14	315
5th	r-21	38/16	311

Flexible predictive power-split control for battery-supercapacitor systems of electric vehicles using IVHS

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Scenario	Travel time/s		Improvement/%
Scenario	Proposed	Baseline	Improvement/%
L05	138	193	29
L10	388	482	20
L15	639	791	19
L20	887	1050	16

Scenario	Accumulative current throughput/mA		Improvement/%
Scenario	Proposed	Baseline	Improvement/%
L05	2.1545	3.1636	68
L10	3.4135	9.9033	34
L15	5.9290	21.234	28
L20	7.5566	25.553	30

Scenario	Final SOC		Improvement
Scenario	Proposed	Baseline	Improvement
L05	89.39	89.40	−0.01
L10	89.09	89.07	0.02
L15	88.40	88.28	0.12
L20	87.90	87.55	0.35

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Scenario	Proposed	Baseline
Scenario	Proposed	High level	Low level
L05	188.0	456.4	87.0
L10	163.8	450.8	89.2
L15	144.5	450.2	117.1
L20	145.5	473.1	121.2