基于灰狼算法优化极限学习机的锂离子电池SOC估计

doi:10.19799/j.cnki.2095-4239.2020.0389

摘要/Abstract

摘要：

准确的电池荷电状态(SOC)估计是电动车辆正常工作的基本前提。针对目前电池荷电状态估计时存在的非线性、不平稳等干扰因素的影响，本工作提出了基于灰狼优化算法的极限学习机的锂离子电池SOC估计方法，以提高估计精度并缩短估计时长。传统的极限学习机(ELM)直接随机生成模型参数，并对SOC进行估计，该方法运行速度快且泛化性能好。但极限学习机需要找出最优的隐含层神经元参数才能达到较高的精度。因此，通过灰狼优化算法(GWO)进一步优化模型参数，并通过选择合适的激活函数，弥补了传统极限学习机的不足。最后通过与粒子群优化的前馈神经网络算法(BPNN-PSO)和极限学习机算法从多维度进行对比分析，在不同工况下验证了此方法在电池SOC估计中的优越性。结果表明，基于灰狼优化算法的极限学习机的锂离子电池荷电状态估计精度高、估算时长较短且鲁棒性较好，明显优于传统SOC估计方法。本研究有助于推动新能源车辆电池管理系统的开发与应用，为可靠的电池管理系统的研发提供支持。

关键词: 锂离子电池, 荷电状态, 极限学习机, 灰狼优化

Abstract:

Accurate estimation of battery state of charge (SOC) is the basic premise for the normal operation of electric vehicles. An extreme learning machine (ELM) based on the gray wolf optimizer (GWO) algorithm was proposed to estimate the SOC of lithium-ion batteries, improve the estimation accuracy, and shorten the estimation time. The traditional ELM generates model parameters randomly. This method runs fast and has good generalization performance. However, the ELM must determine the optimal hidden layer neuron parameters to achieve high accuracy. Therefore, the GWO algorithm was adopted to further optimize the model parameters. The appropriate activation function was selected to make up for the shortcomings of the traditional ELM. Finally, the advantages of this method in battery SOC estimation are verified under different working conditions by comparison with the particle swarm optimization-feedforward neural network algorithm (BPNN-PSO) and the ELM. The result shows that the ELM based on the GWO algorithm has high accuracy, short estimation time, and good robustness, which is better than those of the traditional SOC estimation method. This research helps promote the development and application of new energy vehicle battery management systems and provide support for the research and development of reliable battery management systems.

Key words: lithium-ion battery, state of charge, extreme learning machine, gray wolf optimizer

中图分类号:

TM 911

王桥, 魏孟, 叶敏, 李嘉波, 徐信芯. 基于灰狼算法优化极限学习机的锂离子电池SOC估计[J]. 储能科学与技术, 2021, 10(2): 744-751.

Qiao WANG, Meng WEI, Min YE, Jiabo LI, Xinxin XU. Estimation of lithium-ion battery SOC based on GWO-optimized extreme learning machine[J]. Energy Storage Science and Technology, 2021, 10(2): 744-751.

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工况	RMSE/%	MAE/%	运行时间/s
CCCV	1.49	1.06	4.054
DST	1.14	0.79	4.064

方法	工况	RMSE/%	MAE/%	运行时间/s
ELM-GWO	CCCV	1.49	1.06	4.054
ELM-GWO	DST	1.14	0.79	4.064
BPNN-PSO	CCCV	2.41	1.44	183.570
BPNN-PSO	DST	2.48	1.64	165.138
ELM	CCCV	4.74	3.69	0.584
ELM	DST	2.14	1.36	0.141

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