Energy Management Strategy Based on Constructing a Fitting Driving Cycle for Pure Electric Vehicles

Nowadays, the trend of powertrain electrification in the public transportation sector is clear. To fit the periodical high-load variation and relatively high handling stability requirements for battery electric buses, the dual-motor four-wheel powertrain attracts great attention in recent years. Although the bus routes are fixed, the passenger capacity and driving speed vary significantly with time, season, and traffic conditions, which presents a serious challenge for efficient power coupling in the dual-motor system to reduce energy consumption. To mitigate the negative effect of periodical power demand variation on energy coupling efficiency for dual-motor powertrain, specific driving cycle fitting is provided based on massive amounts of collected bus driving data. Then, Deep Deterministic Policy Gradient (DDPG) algorithm is introduced in Energy Management Strategy (EMS) design to improve the vehicle energy performance in fixed driving routes with uncertain demand. The results show that DDPG-EMS achieve 93.79%-97.67% of the benchmark Dynamic Programming (DP) - based EMS under different validation cycles. The comparison of DDPG-EMS agent trained by fitting cycle and typical cycle reached 97.1%-97.67% and 93.79%-96.99% of DP, respectively, which proved the effectiveness of the specifically designed cycle in reinforcement learning-based EMS for dual-motor electrified bus.