Hybrid Vehicle Battery Health State Estimation Based on Intelligent Regenerative Braking Control

In response to the evolving transportation landscape, the safety and durability of hybrid electric vehicles (HEVs) necessitate the development of high-performance, reliable health management systems for batteries. The state of health (SOH) provides vital insights about the performance and longevity of batteries, thus enhancing opportunities for efficient energy management in hybrid systems. Despite various research efforts for battery SOH estimation, many of them fall short of the demands for real-time automotive applications. Real-time SOH estimation is crucial for accurate battery fault diagnosis and maintaining precise estimation of the state of charge (SOC) and state of power (SOP), which are essential for the optimal functioning of hybrid systems. In this study, a fuzzy logic estimation method is deployed to determine the tire road friction coefficient (TRFC) and various control strategies are adopted to establish regenerative cut-off points. A MATLAB-based SOH estimation model was developed using a Kalman SOH estimator, which helps to observe the effects of different control strategies on the battery’s SOH. This approach enhances the accuracy and reliability of SOH estimation in real-time applications and improves the effectiveness of battery fault diagnosis. From the results, ANFIS outperformed standard methods, showing approximately 4–6% higher SOH retention across various driving cycles.