Gaussian Quantum-Behaved PSO Strategy for Lithium Battery Model Optimization

The integration of quantum-inspired techniques into optimization algorithms allows for enhanced exploration of complex parameter spaces, enabling efficient convergence toward optimal solutions. This article presents the application of Gaussian Quantum-behaved Particle Swarm Optimization (GQPSO) strategy, tailored for optimizing the parameters of the Lithium Battery Extended Thevenin model. Inspired by the principles of swarm intelligence and quantum mechanics, the GQPSO leverages Gaussian distributions to enhance the exploration of parameter spaces, capitalizing on the advantages offered by quantum-inspired optimization techniques. Through tests on high-accuracy data, the effectiveness of the GQPSO strategy is demonstrated. The optimized Lithium Battery Extended Thevenin model showcases remarkable accuracy, with an RMSE of 2.06116086e−02 and a standard deviation of 1.27510624e−02. The GQPSO strategy’s exceptional performance showcases the advantages of combining quantum mechanics’ principles and Gaussian distributions with swarm intelligence for accurate and efficient lithium battery modeling, paving the way for improved energy storage system applications.