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

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%.

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Publication Details

Journal
Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering
Published
2025-08-10
DOI
https://doi.org/10.1177/09544070251347101
Primary Topic
Electric and Hybrid Vehicle Technologies
Type
article
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article

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

Xuanyao Wang, Hsiao-Ling Sun, Qun Wang
Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering
Electric and Hybrid Vehicle Technologies
article

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

Xuanyao Wang, Hsiao-Ling Sun, Qun Wang
article en

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%.

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering
Anhui University of Science and Technology (CN), Intelligent Energy (United Kingdom) (GB)
Openalex Percentile: Top 79%
Electric and Hybrid Vehicle Technologies
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