Adaptive Neuro-Fuzzy Control-Based MultiObjective Energy Management for SolarIntegrated Battery–Supercapacitor Electric Vehicles

The integration of electric vehicles with renewable energy sources is a critical direction for establishing sustainable transportation systems, representing a core research focus within the current new energy transportation sector. This study specifically targets solar electric vehicles that incorporate on-board photovoltaics and are equipped with a hybrid energy storage system (HESS), which consists of power batteries and supercapacitors. A novel adaptive neuro-fuzzy inference system (ANFIS) energy management strategy is proposed to effectively manage this complex system. The HESS operates by capitalizing on the complementary characteristics of its two constituent types: power batteries deliver a continuous, fundamental power supply due to their high energy density, whereas supercapacitors, leveraging their high-power density,

are designed to meet the rapid charging and discharging requirements associated with vehicle acceleration, deceleration, and braking energy recovery. Furthermore, on-board photovoltaics function as an auxiliary energy source, significantly extending the vehicle's driving range and diminishing its overall dependence on the public power grid. The ANFIS controller developed in this research integrates the autonomous learning capabilities of neural networks with the inherent interpretability of fuzzy logic. This intelligent controller is engineered to respond in real-time to three fundamental dynamic operational variables: the prevailing driving mode, the intensity of solar irradiance, and the current state of charge of the energy storage system. This comprehensive approach aims to achieve multi-objective energy management,

optimizing performance and efficiency.

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