Health Status Assessment and Prediction of Power Lithium Battery Based on ELM and Improved PSO

In the research of battery management driven by computer intelligent algorithms, Extreme Learning Machine (ELM) are suitable for lithium battery health assessment due to their high efficiency, but their single model is sensitive to parameters and has limited generalization ability. Therefore, this study proposes an integrated ELM model based on quantum particle swarm algorithm and K-means clustering, which realizes high-efficiency health assessment and life prediction of multi-parameter fusion by globally optimizing the base model parameters and clustering mining the data distribution law. In the performance test experiments, the fault identification accuracy rate of the model proposed by the research institute reached 0.99. In the 100 battery cycle aging experiment prediction scenarios, the average prediction error of the remaining battery service life was as low as 1, which was only 33.3% of similar models. The study provides a high-precision algorithm solution for the intelligent management of power lithium batteries. Its technical path of integrating swarm intelligence and clustering optimization provides new ideas for the application of artificial intelligence in the field of energy system status assessment.