Regenerative braking control strategy for battery electric heavy-duty trucks considering vehicle load states and braking intentions

Abstract

Battery electric heavy-duty trucks (BETs) are increasingly used in intercity logistics, ports, and mining, but their long-haul deployment is constrained by limited driving range and relatively low energy replenishment efficiency. Regenerative braking can alleviate this issue by improving energy utilization efficiency. To this end, a regenerative braking control strategy for BETs considering vehicle load states and braking intentions is proposed. First, a vehicle load state identification method based on mass estimation is developed. Then, an improved inter-axle braking torque distribution strategy is designed to comply with multi-axle braking regulations while maximizing regenerative braking potential. Next, a braking intention-based drive-axle torque distribution strategy is introduced to balance braking safety, energy economy, and comfort. Finally, the proposed strategy is validated under the CHTC-D and CHCV driving cycles. Results show that the proposed load-state identification method rapidly and accurately determines the corresponding vehicle load state. Compared with the widely adopted parallel strategy, the proposed braking strategy achieves higher energy-saving performance across load states, with pronounced advantages under loaded and overloaded states. Specifically, the energy recovery rates increase by 47.7% and 33.8% in the CHTC-D cycle, while energy-saving contribution and driving range contribution increase by 41.5% and 25.4%, respectively, in the CHCV cycle. These findings confirm the effectiveness of the proposed strategy in enhancing the energy efficiency of multi-axle BETs.