Efficient Energy Management Strategy for an Electric Vehicle Powered by a Hybrid Energy Storage System Based on Hybrid GBDT-RSA Approach

This paper proposes an efficient energy management scheme for an EV with a hybrid energy storage system like super capacitor and battery based on hybrid optimization method. The proposed hybrid approach is a parallel performance of gradient boosting decision tree algorithm and reptile search algorithm. The major purpose is to diminish the variance among real and reference power on battery and super capacitor. The HESS is divided into two segments: (1) Determine the super capacitor reference voltage, which is affected by load dynamics. (2) Maximize the power flow via the HESS. The super capacitor reference voltage is first calculated by assessing the load dynamics on real time, such as vehicle dynamics, motor characteristics, and regenerative braking systems. The super capacitor's input parameters include load current, battery current, and state of charge. The RSA is created on proposed system by combining the probable data set of HESS control signals. The GBDT algorithm is trained and forecasts optimal HESS parameters using the RSA's practiced data set. The proposed approach also improves battery current magnitude, super capacitor voltage, battery current ranges, and battery power. It optimizes the HESS parameter provides specific solutions. The proposed hybrid system is implemented on MATLAB and its performance is related to that of existing approaches.