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Article

Two-Speed AMT Shift Control Strategy Based on Vehicle Speed Prediction and Driving Style Recognition for Heavy-Duty Electric Vehicles

by
Wei Jiang
1,2,
Xuan Wang
2,3,
Shenggen Zhang
2,
Xiansheng Huang
2,
Jingang Liu
4,
Shuai Cao
2,
Hao Zhou
2 and
Yunhan Song
2,*
1
State Key Laboratory of Intelligent Vehicle Safety Technology, Chongqing 400023, China
2
Intelligent Connected Vehicle Inspection Center (Hunan) of CAERI Co., Ltd., Xueshi Road, Yuelu District, Changsha 410205, China
3
Jiangsu CAERI Automotive Engineering Research Institute Co., Ltd., Zhenbei Road, Suzhou New District, Suzhou 215151, China
4
School of Mechanical Engineering, Xiangtan University, North Second Ring Road, Yuhu District, Xiangtan 411105, China
*
Author to whom correspondence should be addressed.
Vehicles 2026, 8(7), 157; https://doi.org/10.3390/vehicles8070157
Submission received: 23 March 2026 / Revised: 20 May 2026 / Accepted: 29 May 2026 / Published: 7 July 2026
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Abstract

The two-speed transmission system significantly enhances the powertrain matching performance of heavy-duty electric military armored vehicles by optimizing high-torque output at low speed and energy efficiency at high speed. However, most existing electric vehicles do not incorporate driving styles or real-time driving condition prediction into their shift control strategies, resulting in suboptimal gear shift timing and smoothness that fail to align with driver expectations and operational requirements. To address these limitations, this study focuses on the two-speed automated manual transmission (AMT) system in heavy-duty electric military armored vehicles. Firstly, a comprehensive shift control model is established, integrating key components such as the drive motor and power battery. Furthermore, a shift control strategy based on vehicle speed prediction and driving style recognition is proposed. The operational logic of this strategy is systematically analyzed under various driving cycles. Simulation and hardware-in-the-loop (HIL) results confirm the performance gains. Simulation and hardware-in-the-loop (HIL) results indicate that the proposed approach improves vehicle power performance by 21.36%, increases energy efficiency by 3.94%, and reduces powertrain shock by 31.81% compared to the conventional vehicle-speed-based gear shifting method. Compared to the adaptive shift schedule design method, the proposed approach reduces shifting frequency by 21.43% and improves ride comfort by at least 19.17% while maintaining comparable dynamic performance and energy efficiency.
Keywords: transmission shift control; vehicle speed prediction; driving style; fuzzy control transmission shift control; vehicle speed prediction; driving style; fuzzy control

Share and Cite

MDPI and ACS Style

Jiang, W.; Wang, X.; Zhang, S.; Huang, X.; Liu, J.; Cao, S.; Zhou, H.; Song, Y. Two-Speed AMT Shift Control Strategy Based on Vehicle Speed Prediction and Driving Style Recognition for Heavy-Duty Electric Vehicles. Vehicles 2026, 8, 157. https://doi.org/10.3390/vehicles8070157

AMA Style

Jiang W, Wang X, Zhang S, Huang X, Liu J, Cao S, Zhou H, Song Y. Two-Speed AMT Shift Control Strategy Based on Vehicle Speed Prediction and Driving Style Recognition for Heavy-Duty Electric Vehicles. Vehicles. 2026; 8(7):157. https://doi.org/10.3390/vehicles8070157

Chicago/Turabian Style

Jiang, Wei, Xuan Wang, Shenggen Zhang, Xiansheng Huang, Jingang Liu, Shuai Cao, Hao Zhou, and Yunhan Song. 2026. "Two-Speed AMT Shift Control Strategy Based on Vehicle Speed Prediction and Driving Style Recognition for Heavy-Duty Electric Vehicles" Vehicles 8, no. 7: 157. https://doi.org/10.3390/vehicles8070157

APA Style

Jiang, W., Wang, X., Zhang, S., Huang, X., Liu, J., Cao, S., Zhou, H., & Song, Y. (2026). Two-Speed AMT Shift Control Strategy Based on Vehicle Speed Prediction and Driving Style Recognition for Heavy-Duty Electric Vehicles. Vehicles, 8(7), 157. https://doi.org/10.3390/vehicles8070157

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