Economic adaptive cruise control for electric vehicles based on hierarchical ADHDP

To enhance battery longevity in electric vehicles (EVs), Hybrid Energy Storage Systems (HESS) integrate the high power density of supercapacitors with the high energy density of batteries. This paper proposes an economic adaptive cruise control (Eco-ACC) strategy for HESS-powered EVs based on hierarchical action-dependent heuristic dynamic programming (HESS-ADHDP). The method incorporates vehicle longitudinal dynamics, battery capacity degradation models, and power distribution mechanisms to construct a two-layer actor–critic learning architecture. The upper layer ensures vehicle safety and driving comfort, while the lower layer optimizes real-time energy allocation between the battery and the supercapacitor. Simulation experiments under various standard drive cycles validate the effectiveness of the proposed strategy. Compared to the baseline control method, the HESS-ADHDP-based approach reduces battery capacity degradation by 14.67%–73.75% and improves energy efficiency by up to 39.5%. The proposed strategy is model-free, real-time, and adaptable to complex driving scenarios, offering promising potential for intelligent energy management in future EV applications.