基于混合进化算法的电池系统放电寿命建模与预测

Hongqing Cao , Jingxian Yu , Lishan Kang , Hanxi Yang , Xinping Ai
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引用次数: 12

摘要

提出了一种混合进化建模算法(HEMA),用于建立具有多影响因素的电池系统放电寿命模型并进行预测。HEMA的主要思想是将遗传算法(GA)嵌入到遗传规划(GP)中,其中遗传算法用于优化模型的结构,遗传算法用于优化模型的参数。锂离子电池的实验结果表明,HEMA能够有效、自动、快速地模拟电池系统的放电寿命。与大多数现有的建模方法相比,该算法具有一定的优势,可广泛应用于解决许多领域的自动建模问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling and prediction for discharge lifetime of battery systems using hybrid evolutionary algorithms

A hybrid evolutionary modeling algorithm (HEMA) is proposed to build the discharge lifetime models with multiple impact factors for battery systems as well as make predictions. The main idea of the HEMA is to embed a genetic algorithm (GA) into genetic programming (GP), where GP is employed to optimize the structure of a model, while a GA is employed to optimize its parameters. The experimental results on lithium–ion batteries show that the HEMA works effectively, automatically and quickly in modeling the discharge lifetime of battery systems. The algorithm has some advantages compared with most existing modeling methods and can be applied widely to solving the automatic modeling problems in many fields.

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