Optimal State-of-Charge Management for Electric Vehicle Batteries Using Eagle Particle Swarm Optimization-Based Hybrid Deep Reinforcement Learning

With the increasing popularity of electric vehicles (EVs), proper battery management is essential for good operation performance, maximum battery life, and minimal charging expenses. In this research, we combine a hybrid deep reinforcement learning (DRL) algorithm with the Eagle Particle Swarm Optimization (EPSO) technique to present a new strategy for improving EV battery state of the charge (SOC) management. The proposed method combines DRL and EPSO's strengths for superior SOC management. The DRL part uses a deep neural network to learn the best charging and discharging procedures and to approximate the charging value function. In order to make smart choices about the battery's SOC, a wide range of information is taken into account, including driving habits, weather, and the cost of energy. We introduce the EPSO approach as a way of exploration and exploitation to improve the DRL algorithm's performance. EPSO takes cues from eagle behavior and employs particle swarm optimization to find the global optimum solution during DRL training. The algorithm can then develop more stable solutions for SOC management and reach convergence more quickly because of this combination. Extensive simulations were run using actual EV driving data, and the proposed method was compared to industry standards. The outcomes prove that EPSO-HDRL, which is based on the Eagle Particle Swarm Optimization, is the best method among tested ones for attaining the optimal SOC management strategy. In terms of battery life, charging efficiency, and cost savings, EPSO-HDRL outperforms other state-of-the-art technologies. The study's conclusions have major bearing on the actual deployment of EV battery management systems. Electric vehicle (EV) owners, fleet managers, and grid operators can strike a better balance between performance, battery life, and cost effectiveness by utilizing a hybrid strategy that combines DRL and EPSO. In addition, the proposed approach may be readily included into preexisting EV charging infrastructure, bolstering the long-term health of the electric transportation ecosystem as a whole.