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An Improved PSO Algorithm for Battery Parameters Identification Optimization Based on Thevenin Battery Model

机译:基于Visumin电池模型的电池参数识别优化改进的PSO算法

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Battery parameters identification is crucial for accurate prediction of battery life in electric vehicles. In order to optimize battery parameter identification, an improved PSO (particle swarm optimization) algorithm was proposed based on the use of Thevenin battery model to abstract the problem into an optimization problem. The experimental results show that the computational accuracy of the improved PSO algorithm is higher than that of the genetic algorithm and the original PSO algorithm, and the battery parameters calculated by the improved PSO algorithm are also more accurate.
机译:电池参数识别对于精确预测电动汽车电池寿命至关重要。为了优化电池参数识别,提出了一种改进的PSO(粒子群优化)算法,基于使用紫色电池模型将问题归入优化问题。实验结果表明,改进的PSO算法的计算精度高于遗传算法和原始PSO算法的计算准确性,并且通过改进的PSO算法计算的电池参数也更准确。

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