Abstract
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.
Highlights
Silver-zinc battery as a new-type chemical power source emerging from the early 1940s, characterized by high energy, high density, stable platform of discharge voltage, high reliability and high safety characteristic than lithium ion batteries
The unsorted battery pack 7 showed the fastest rate of capacity decay, while battery pack 1 to 6 had little difference in the rate of capacity decay, suggesting the consistency of batteries sorted by the battery classification model improved significantly
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 was proposed in this paper
Summary
Silver-zinc battery as a new-type chemical power source emerging from the early 1940s, characterized by high energy, high density, stable platform of discharge voltage, high reliability and high safety characteristic than lithium ion batteries. R. Li et al.: Toward Group Applications of Zinc-Silver Battery: Classification Strategy Based on PSO-LSSVM may not guarantee the consistency of the dynamic characteristics [8]. In literature [9]–[12], charge capacity grading curve and standard characteristic parameters on discharge voltage platform were extracted as the input of the battery group strategy. Particle swarm optimization algorithm was used simultaneously to autonomously search the optimal parameters of the least squares support vector machine, saving time for parameter adjustment and allowing battery sorting without manual sorting [21], [22] This method breaks the limitation of building battery classification model based on prior knowledge, reduces the dependence on parameter selection, improve the dynamic consistency of battery packs and enhances the rate of capacity decay after sorting silver-zinc batteries in groups. Introduce radial basis kernel function into equation 9 to obtain the specific expression of the discrimination function:
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