Real-time energy management strategy for plug-in 4WD hybrid vehicles with 3-DHT transmission

Abstract

The plug-in hybrid electric vehicle (PHEV) is considered the optimal transition solution from gasoline-powered cars to pure electric vehicles. Given that the core of hybrid vehicles lies in the Energy Management System (EMS), it becomes crucial to explore a robust and adaptable EMS. This study focuses on the existing plug-in 3-DHT hybrid four-wheel-drive vehicle (4WD-PHEV). Initially, addressing the front/rear axle torque distribution issue, an optimal working curve is derived based on the efficiency characteristics of the rear-drive motor, integrating driver intent to achieve coordinated torque distribution between the front and rear axles. Subsequently, targeting the multiple gear shift issue in the front axle 3-DHT, a shift logic is formulated using vehicle speed and driver throttle pedal signals as decision variables, validated through simulation in the Charge Depletion (CD) mode. Moreover, for the front axle’s multi-source energy allocation problem, a Fuzzy Logic Control (FLC) strategy is devised. To further enhance optimization, a FLC-PMP dual-layer optimization control strategy is proposed by introducing the Pontryagin’s Minimum Principle (PMP) algorithm based on the FLC strategy. Finally, employing AVL-CRUISE and Matlab/Simulink software, an integrated vehicle model and control strategy model are developed, and their simulations are compared. Comparative analysis against rule-based (RB) strategies reveals that, in the CD mode, the formulated shift logic is validated. Moreover, under similar driving cycles (6×WLTC), the fuel economy of FLC-PMP and FLC strategies is respectively improved by 3.73% and 6.36%.

Keywords

Plug-in hybrid electric vehicles energy management strategy front/rear axle torque distribution fuzzy logic control Pontryagin’s minimum principle

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References

Jochem

Doll

Fichtner

External costs of electric vehicles. Transp Res D Transp Environ 2016; 42: 60–76.

Ouyang

Progress of Chinese electric vehicles industrialization in 2015: a review. Appl Energy 2017; 188: 529–546.

Vergis

Chen

Comparison of plug-in electric vehicle adoption in the United States: a state by state approach. Res Transp Econ 2015; 52: 56–64.

Brooke

Positioning for hybrid growth. SAE Int Automot Eng 2017; 9: 32–34.

Kwak

Kim

, et al. Real-time torque-split strategy for P0+ P4 mild hybrid vehicles with eAWD capability. IEEE Trans Transp Electrif 2021; 8(1): 1401–1413.

Liu

Wang

, et al. Adaptive equivalent consumption minimisation strategy and dynamic control allocation-based optimal power management strategy for four-wheel drive hybrid electric vehicles. Proc IMechE, Part D: J Automobile Engineering 2019; 233(12): 3125–3146.

Guo

Chu

, et al. A hierarchical energy management strategy for 4WD plug-in hybrid electric vehicles. Machines 2022; 10(10): 947.

Qiu

Wang

Dynamic powertrain design for electric vehicle based on consumption and cost. J Jiangsu Univ (Nat Sci Ed) 2019; 40: 16–21.

Xie

Lang

, et al. Coordinated management of connected plug-in hybrid electric buses for energy saving, inter-vehicle safety, and battery health. Appl Energy 2020; 268: 115028.

10.

Wang

Xia

Cai

, et al. Novel energy management strategy for a dual-motor hybrid electric vehicle considering frequency of mode transitions. Energy Convers Manag 2022; 269: 116106.

11.

Wei

Sun

Chen

, et al. Comparison of architecture and adaptive energy management strategy for plug-in hybrid electric logistics vehicle. Energy 2021; 230: 120858.

12.

Liu

Chen

, et al. Research on a multi-objective hierarchical prediction energy management strategy for range extended fuel cell vehicles. J Power Sources 2019; 429: 55–66.

13.

Guo

Inoa

Choi

, et al. Study on global optimization and control strategy development for a PHEV charging facility. IEEE Trans Veh Technol 2012; 61(6): 2431–2441.

14.

Luo

Jiang

, et al. Research on double fuzzy control strategy for parallel hybrid electric vehicle based on GA and DP optimisation. IET Electr Syst Transp 2018; 8(2): 144–151.

15.

Khayyer

Wollaeger

Onori

, et al. Analysis of impact factors for plug-in hybrid electric vehicles energy management. In: Proceedings of the 15th international IEEE conference on intelligent transportation systems, Anchorage, AK, USA, 16–19 September 2012, pp.1061–1066. New York: IEEE.

16.

Choi

Byun

Lee

, et al. Adaptive equivalent consumption minimization strategy (A-ECMS) for the HEVs with a near-optimal equivalent factor considering driving conditions. IEEE Trans Veh Technol 2021; 71(3): 2538–2549.

17.

Rezaei

Burl

Zhou

, et al. A new real-time optimal energy management strategy for parallel hybrid electric vehicles. IEEE Trans Control Syst Technol 2017; 27(2): 830–837.

18.

Zhang

Chu

, et al. Optimal energy management strategy for parallel plug-in hybrid electric vehicle based on driving behavior analysis and real-time traffic information prediction. Mechatronics 2017; 46: 177–192.

19.

Liu

Qin

, et al. Research on equivalent factor boundary of equivalent consumption minimization strategy for PHEVs. IEEE Trans Veh Technol 2020; 69(6): 6011–6024.

20.

Wang

Sun

, et al. Model predictive control strategy for energy optimization of series-parallel hybrid electric vehicle. J Clean Prod 2018; 199: 348–358.

21.

Dou

Xia

Yuan

, et al. Energy management strategy for microgrid based on improved model predictive control. Power Syst Autom 2017; 41(22): 56–65.

22.

Xie

Xin

, et al. Pontryagin’s minimum principle-based model predictive control of energy management for a plug-in hybrid electric bus. Appl Energy 2019; 236: 893–905.

23.

Ganguly

Sahoo

Das

Multi-objective planning of electrical distribution systems using dynamic programming. Int J Electr Power Energy Syst 2013; 46: 65–78.