Collaborative evaluation of SoC, SoP and SoH of lithium-ion battery in an electric bus through improved remora optimization algorithm and dual adaptive Kalman filtering algorithm

Many countries are switched to electric vehicles (EVs) for public transportation, which increases the adoption of electric buses. Batteries are the key component in battery-operated electric vehicles and must be monitored for the system to operate efficiently. This work has proposed a joint estimation method to examine the battery states. The lithium-ion battery (Li-B) is initially designed through a first-order RC equivalent (FO-RC) circuit. To optimize the modelling parameters of a battery, an improved remora optimization algorithm (IROA) is proposed in this work. After detecting the optimum values for the parameters, the SoC is evaluated by a dual adaptive Kalman filtering algorithm (DAKF). Then the SoH is estimated based on the predicted SoC of a battery, whereas the SoP is evaluated by considering current and voltage constraints during battery operation. After that, the battery's remaining useful life (RUL) is examined based on the estimated SoC. The proposed work is implemented on the MATLAB platform, and the results will be validated under varying operating conditions. The comparative analysis shows that the IROA provides optimum parameters near the actual parameters' actual values, thereby improving the prediction accuracy of battery states.