AI-based Smart Energy Management of Battery-Supercapacitor Hybrid Energy Storage System for Electric Vehicle Applications

Abstract

This study explores an intelligent energy management framework for hybrid energy storage systems (HESS) that combine lithium-ion batteries and supercapacitors in electric vehicle applications. A Python-based simulation model incorporating a hierarchical AI controller was developed and evaluated using a 60-second real-world driving profile. The proposed method enables efficient power sharing between storage components, resulting in an overall system efficiency of 93.12%. Regenerative braking performance reached 88.5% ± 2.3%, indicating effective energy recovery. The strategy also reduces battery current stress by 42.3%, contributing to improved lifespan and operational reliability. Temperature regulation remains stable, with battery values maintained between 25.86°C and 29.93°C due to adaptive load allocation. The controller dynamically adjusts behavior across driving conditions such as cruising, acceleration, and braking. Comparative results confirm that integrating supercapacitors improves efficiency relative to battery-only systems. Economic evaluation further suggests a lifecycle cost reduction of 18.5% despite higher initial investment. These findings support the feasibility of AI-assisted HESS for next-generation electric mobility.