Study on Artificial Neural Network Optimization for Electric Vehicle Battery State of Charge estimation

Accurate lithium-ion battery state estimation is crucial for dynamic applications, such as Electric Vehicles (EVs). Artificial Neural Networks (ANNs) are widely explored in recent years due to its high adaptability for any non-linear system. However, ANNs take longer to train with large datasets, especially with complex architectures. This can be solved by using optimization algorithms prior to ANN training that sets the hyper-parameters in the optimal region, leading to high convergence rate, which is desired for EV based battery state estimators. This paper studies some of the recently used population-based optimization algorithms. This includes Firefly, Bat, Gravitation Search, Particle-Swarm, Antlion and Backtracking Search algorithms along with their variants. The performance of each of these algorithms are compared, in terms of optimizing the weights and biases for a Feedforward Neural Network used for lithium-ion battery State of Charge (SoC) estimation. The obtained results show that Antlion optimization Algorithm with Double-Step Search (ALO-DSS) produced low estimation error (0.0194 MSE), with less variation in the performance when run multiple times and a shorter computation time (343.6s) as compared to Firefly Algorithm (605.3s). Thus, ALO-DSS may be considered viable for optimization of ANNs for EV battery state estimation, especially for SoC estimation. Moreover, using optimization algorithms can save training time and computational costs, leading to higher feasibility in deploying ANNs for EV applications.