A review of expert hybrid and co-estimation techniques for SOH and RUL estimation in battery management system with electric vehicle application

To improve the functionality and efficiency of electric vehicles (EVs), the smart battery management system (BMS) is essential. The accurate estimation of the state of health (SOH) and remaining useful life (RUL) in BMS enhance battery safety, longevity, and reliability, which enhances EV performance and efficiency. However, the accurate estimation of SOH and RUL is challenging because of capacity degradation during charging and discharging operations. The conventional research to estimate the SOH and RUL of lithium-ion battery (LIB) is based on the single model framework. However, the single model for SOH and RUL estimation may not deliver accurate outcomes due to the complex internal LIB mechanism and varying external conditions. In recent times, the application of expert hybrid techniques (combining two or more models) has drawn huge attention from the research community due to their high accuracy and robustness under varying environmental conditions. Nonetheless, the implementation of hybrid techniques for SOH and RUL estimation for BMS in EVs is currently limited. Therefore, the originality of this work is to provide a thorough review of hybrid methods for SOH and RUL estimation in LIB with an emphasis on methodologies, executions, advantages, disadvantages, accuracy, and contributions. Additionally, the co-estimation of SOH and RUL utilizing the same model is gaining global popularity among researchers. Henceforth, the presented review work also investigates various techniques utilized to co-estimate the SOH and RUL simultaneously. Furthermore, some critical operation factors associated with SOH and RUL estimation framework are analyzed related to the dataset, model execution, battery parameters and their features. The applicability of the reviewed hybrid SOH and RUL estimation techniques are discussed along with current issues and limitations. Lastly, selected future suggestions are provided to guide the automobile sector to develop a reliable and accurate framework utilizing the hybrid and co-estimation framework to estimate the SOH and RUL in LIB.