DATA-DRIVEN OPTIMIZATION OF HYBRID POWERTRAIN ENERGY MANAGEMENT USING REINFORCEMENT LEARNING AND ECMS

Hybrid electric vehicles (HEVs) require an optimal energy management strategy (EMS) to allocate power between the internal combustion engine (ICE) and the electric motor. Although they are straightforward, traditional rule-based approaches do not account for future electricity needs. Equivalent consumption minimization strategy (ECMS) improves fuel economy by minimizing instantaneous equivalent fuel consumption; however, it relies on a fixed equivalence factor and lacks long-term planning. In this paper, a data-driven energy management strategy that combines ECMS and reinforcement learning (RL) is proposed. The controller calculates the effective equivalency ratio and the optimal torque split directly from the data, using the instantaneous ECMS fuel cost as the RL reward (case 4). The Pasadena, US06, and WLTC cycles were used to test the technique. The findings indicate that the RL + ECMS system outperforms the rule-based EMS, ECMS, and A-ECMS systems in terms of fuel consumption reduction and battery state of charge (SoC) stability. Simulations validate enhanced RL reward convergence, adaptive power distribution, and SoC stability.