A multi-criteria evaluation framework for adaptability of hybrid energy storage system energy management strategies to dynamic driving style

Incorporating driving style can substantially enhance the adaptability of energy management strategies in complex urban traffic, however, comprehensively and effectively evaluating different strategies remains challenging. To address this gap, this study proposes an evaluation framework based on multi-criteria decision making (MCDM), incorporating driving style characteristics for optimal adaptability of hybrid energy storage system (HESS) control strategies. Utilizing real-world urban driving data and driving styles classification, a structured indicator system is established covering system stability, battery health, efficiency, and economy. A hybrid analytic hierarchy process (AHP) and grey relational analysis (GRA) method combines expert judgment with data-driven analysis to assign indicator weights and compute comprehensive scores. Simulation comparisons show that different strategies exhibit different performance across driving scenarios, within the proposed framework, logic threshold control (LTC) and LTC with genetic algorithm (LTC-GA) perform best under conservative and standard driving styles, respectively, whereas wavelet packet transform with GA (WPT-GA) performs best under aggressive driving for its advantage in enhancing system stability through power smoothing. By comprehensively quantifying strategy adaptability, the framework provides a rigorous basis for benchmarking and for designing personalized, flexible energy management strategies.