SOC Online Estimation Error Correction Algorithm Based on Particle Swarm Optimization Particle Filter Algorithm

With the popularization of new energy vehicles and renewable energy, accurate prediction of battery state of charge (SOC) is crucial for vehicle performance and safety. This article proposes an error correction method for SOC online estimation by using particle swarm optimization particle filtering method and improving on traditional algorithms. Through experimental verification, this method outperforms traditional methods in terms of SOC estimation accuracy, convergence speed, and computational efficiency. The experimental results show that the particle filter algorithm based on particle swarm optimization outperforms traditional methods in terms of average absolute error, root mean square error, and maximum error. Meanwhile, this method can quickly obtain accurate SOC estimation results and has higher computational efficiency. Therefore, this algorithm is of great significance for the performance optimization and safety control of electric vehicles, providing new ideas for further research in the field of SOC estimation.