Adaptive equivalent consumption minimization strategy based on Bayesian Optimization

The Equivalent Consumption Minimization Strategy (ECMS) is an effective approach for managing energy flow in hybrid electric vehicles (HEVs), balancing the use of electric energy and fuel consumption. The strategy's performance depends heavily on the Equivalent Factor (EF), which governs this trade-off. However, the optimal EF varies under different driving conditions and is influenced by the inherent randomness in factors such as traffic, road gradients, and driving behavior, making it challenging to determine through traditional methods. This paper introduces Bayesian Optimization (BO) as a solution to address the stochastic nature of the EF parameter tuning process. By using a probabilistic model, BO efficiently navigates the complex, uncertain performance landscape to find the optimal EF parameters that minimize fuel consumption and emissions across variable conditions. Simulation results for the WLTP and Artemis driving cycles demonstrate that the EF parameters optimized with BO lead to improved ECMS performance, reducing fuel consumption and enhancing energy efficiency. The method outperforms traditional approaches by better adapting to the randomness of real-world driving scenarios, ensuring more consistent and optimal performance. This study highlights the advantages of Bayesian Optimization in tuning ECMS for hybrid vehicles, offering a robust approach to handle the uncertainties and variability in driving cycles, ultimately contributing to more efficient and sustainable vehicle energy management.