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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 …