Sage Journals: Discover world-class research

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

Get full access to this article

View all access options for this article.

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.

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

Abstract

Keywords

Get full access to this article

References