Improved Mode Switching and Gear Shift Control Strategy of Parallel Hybrid Electric Vehicle Based on Dynamic Programming

<div>Parallel hybrid commercial vehicles equipped with automated manual transmissions are extensively utilized in the commercial vehicle sector due to their minimal configuration changes, high energy efficiency, and multi-mode driving capabilities. The key to enhancing the fuel economy of these vehicles lies in the mode switching and gear shift control strategy. To meet the driving requirements of these vehicles and optimize their fuel efficiency, this study introduces a mode switching and gear shift control strategy based on dynamic programming for a parallel hybrid commercial vehicle. First, dynamic programming is applied to the energy management strategy of the hybrid electric vehicle to determine the optimal fuel-efficient power output. Subsequently, the results from dynamic programming simulations are utilized to establish the mode switching boundaries and gear shift patterns. An improved mode switching and gear shift control strategy is then proposed and compared with the control strategy based on optimal efficiency. The findings indicate that the fuel consumption per 100 km with the dynamic programming improvement strategy was 5.64% lower than that of the control strategy based on optimal efficiency, under the conditions of the Chinese heavy-duty commercial vehicle test cycle for heavy trucks. This underscores the superior fuel efficiency of the improved control strategy compared with the control strategy based on optimal efficiency.</div>

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Publication Details

Journal
SAE International journal of commercial vehicles
Published
2025-02-06
DOI
https://doi.org/10.4271/02-18-02-0008
Citations
1
Primary Topic
Electric and Hybrid Vehicle Technologies
Type
article
Field-Weighted Citation Impact
0.68
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article

Improved Mode Switching and Gear Shift Control Strategy of Parallel Hybrid Electric Vehicle Based on Dynamic Programming

Ya‐Yen Sun, Mingyao Yao, Zhengfeng Yan, Xinyi Zhai et al.
1 citations
SAE International journal of commercial vehicles
Electric and Hybrid Vehicle Technologies
0.68
article

Improved Mode Switching and Gear Shift Control Strategy of Parallel Hybrid Electric Vehicle Based on Dynamic Programming

Ya‐Yen Sun, Mingyao Yao, Zhengfeng Yan, Xinyi Zhai, Yujuan Li, G S Jiang
article en
1 citations

Abstract

<div>Parallel hybrid commercial vehicles equipped with automated manual transmissions are extensively utilized in the commercial vehicle sector due to their minimal configuration changes, high energy efficiency, and multi-mode driving capabilities. The key to enhancing the fuel economy of these vehicles lies in the mode switching and gear shift control strategy. To meet the driving requirements of these vehicles and optimize their fuel efficiency, this study introduces a mode switching and gear shift control strategy based on dynamic programming for a parallel hybrid commercial vehicle. First, dynamic programming is applied to the energy management strategy of the hybrid electric vehicle to determine the optimal fuel-efficient power output. Subsequently, the results from dynamic programming simulations are utilized to establish the mode switching boundaries and gear shift patterns. An improved mode switching and gear shift control strategy is then proposed and compared with the control strategy based on optimal efficiency. The findings indicate that the fuel consumption per 100 km with the dynamic programming improvement strategy was 5.64% lower than that of the control strategy based on optimal efficiency, under the conditions of the Chinese heavy-duty commercial vehicle test cycle for heavy trucks. This underscores the superior fuel efficiency of the improved control strategy compared with the control strategy based on optimal efficiency.</div>

SAE International journal of commercial vehiclesVol. 18(2)
Hefei University of Technology (CN), Weichai Power (China) (CN), State Key Laboratory of Engine and Powertrain System
Openalex Percentile: Top 34%
Electric and Hybrid Vehicle Technologies
0.68
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