| Research on Adaptive Energy Management Strategy Based on Road Segment Electricity Allocation for the PHEV |
| Shaohua Wang1,2, Yunxiang Zheng1, Dehua Shi1,2, Chun Li3, Kaimei Zhang1 |
1Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China
2Zhenjiang City Jiangsu University Engineering Technology Research Institute, Zhenjiang 212013, Jiangsu, China
3Higer Bus Company Limited, Suzhou 215026, China |
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Received: January 18, 2024; Revised: October 2, 2024 Accepted: November 9, 2024. Published online: December 30, 2024. |
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| ABSTRACT |
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Fuel efficiency enhancement in plug-in hybrid electric vehicles (PHEVs) is intricately linked to battery usage efficiency and distinct driving conditions. Aiming at a multi-gear and multi-mode parallel PHEV, this study proposes an adaptive energy management strategy based on the battery electricity allocation and equivalent fuel consumption minimization strategy (ECMS) considering the characteristics of different road segments. First, different kinds of standard driving cycles are segmented and clustered into four types, on which basis a combination cycle of different types is constructed. Using dynamic programming, the effects of different average available battery state of charge (SOC) and different driving condition ratios on the optimal battery electricity allocation are studied. Second, a neural network is used to establish a prediction model for the road segment electricity allocation and the battery SOC is planned. Finally, the ECMS strategy is adopted for optimal torque allocation, whose equivalent factor is optimized by the genetic algorithm. Research results show that, when the initial SOC of the battery is 0.9 and 0.8, the proposed energy management strategy improves the vehicle fuel economy by 5.35% and 5.72%, respectively, compared with the ECMS strategy that plans the SOC trajectory based on the traveling distance. |
| Key Words:
Hybrid electric vehicle · Adaptive energy management strategy · Electricity planning · Equivalent fuel consumption minimization strategy · Driving condition characteristics |
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Corresponding Author.
Dehua Shi , Email. 
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