Performance Enhancement of Hybrid Energy Storage System for Electric Vehicle Using Intelligent-Based Controller

Electric vehicles (EVs) are becoming increasingly popular, but their widespread adoption is still limited by issues such as short battery life and limited driving range. To address these challenges, this study proposes an intelligent current management strategy using a battery/supercapacitor hybrid energy storage system (HESS). The goal is to optimize current distribution, extend battery life, and improve overall energy efficiency. An algorithm is developed and tested using a hardware-in-the-loop (HIL) emulation platform. Two intelligent controllers—based on artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS)—are designed and compared with a conventional rule-based controller and an EV system without a supercapacitor (SC). The ANN and ANFIS controllers are trained using data from the rule-based controller. The results show that the intelligent controllers, especially the ANFIS-based controller, significantly improve battery capacity reduction and energy management. In the Federal Test Procedure 75 (FTP-75) driving cycle, the ANFIS controller improved battery capacity by 13.27% at the 5000th cycle. In the European Driving Cycle (ECE-15) cycle, the improvement was 3.05%. For the Extra-Urban Driving Cycle (EUDC) cycle, the EV without a SC experienced 100% battery capacity loss by the 2000th cycle, while the ANFIS controller reduced this loss to 62.06% at the 5000th cycle. These findings confirm that the proposed ANFIS-based controller is the most effective in preserving battery life and enhancing driving range. This approach offers a practical and efficient solution to key limitations in EV performance, contributing to the development of more reliable and longer-lasting EVs.