Innovative optimization of hybrid energy storage systems for electric vehicles: Integrating FBPINN-SAO to enhance performance and efficiency

Investigating alternate energy sources is necessary given the transportation sector's significant impact on energy consumption and greenhouse gas emissions. Battery electric vehicles (BEVs) face challenges in meeting highpower demands during acceleration and efficient energy recovery during deceleration, which can compromise battery life and performance due to variable driving conditions. This research proposes improving hybrid energy storage systems (HESS) in electric vehicles, such as batteries and super capacitors (SC), to overcome these problems. The proposed hybrid approach combines the Finite Basis Physics-Informed Neural Network (FBPINN) and Snow Ablation Optimizer (SAO), usually referred to as the FBPINN-SAO technique. The main aim of the proposed approach is to offer prolonged battery life by effectively managing high-power demands and energy recovery during various driving conditions. The HESS system's power flow is optimized by the SAO to reduce system loss, and the load demand is predicted by the FBPINN. By then, the performance of the proposed technique is implemented in the MATLAB platform. This means that the proposed technique's efficiency is 98 %. But, with the existing methods, like Long Short Term Memory (LSTM) Particle Swarm Optimization (PSO) algorithm the efficiency is 96 % and 94 % correspondingly. From the outcomes, it can be observed that the proposed technique demonstrates higher efficiency compared to the existing methods.