Toward Group Applications of Zinc-Silver Battery: A Classification Strategy Based on PSO-LSSVM

To solve problem of the reliability and consistency of silver-zinc batteries after being sorted into groups, a proposed classification strategy of zinc-silver battery based on least squares support vector machine with PSO (PSO-LSSVM) was proposed in this paper. Sample data was extracted from the charging curve of silver-zinc batteries to pre-sort training samples using FCM clustering. The least squares support vector machine model parameters were optimized and improved using particle swarm optimization algorithm. The method breaks the limitation of building battery classification model based on prior knowledge, reduces the dependence on parameter selection, and enhances model training speed and accuracy. In the end, experimental data was used for battery classification model training and testing. Test results show that the battery pack obtained by the group strategy has good dynamic consistency, the rate of capacity decay is significantly reduced. The rate of capacity decay is no more than 10% after 30 cycles of life test. The silver-zinc battery group classification strategy proposed to this paper improves the consistency and reliability of the battery and the life of battery packs.