| 引用本文: | 吴青峰,杨艺涛,刘立群,等.基于GA-SA-BP神经网络的锂电池健康状态估算方法[J].电力系统保护与控制,2024,52(19):74-84.[点击复制] |
| WU Qingfeng,YANG Yitao,LIU Liqun,et al.Lithium battery state of health estimation method based on a GA-SA-BP neural network[J].Power System Protection and Control,2024,52(19):74-84[点击复制] |
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| 摘要: |
| 锂电池健康状态(state of health, SOH)可表征锂电池的老化状况，准确估算SOH对锂电池可靠运行至关重要。为解决模拟退火算法(simulated annealing, SA)和遗传算法(genetic algorithms, GA)优化的BP神经网络收敛效率低、易陷入局部最优无法到达全局最优解的问题，提出一种GA-SA-BP神经网络算法来提高SOH估算精度。首先，分析NASA公开数据集数据各个健康因子(health indicator, HI)与SOH相关性，选取与SOH相关性更高的锂电池输出电压、输出电流、容量和等压降放电时间4个HI作为BP神经网络的输入值，以提高SOH估算精度。其次，提出GA-SA-BP神经网络算法来估算SOH，通过在陷入局部最优时跳出局部最优找到全局最优解，以便进一步提高SOH估算精度。最后，NASA锂电池数据集和锂电池实验测试平台取得的结果表明，与传统BP神经网络、GA-BP神经网络和SA-BP神经网络相比，所提方案提高了SOH估算精度，在部分数据缺失的情况下仍具有效性。 |
| 关键词: 锂电池 健康状态估算 神经网络 健康因子 |
| DOI：10.19783/j.cnki.pspc.240248 |
| 投稿时间：2024-03-04修订日期：2024-07-19 |
| 基金项目:国家重点研发计划项目资助(2018YFA0707305)；山西省基础研究计划面上项目资助(202203021221153)；阳泉市应用基础研究计划项目资助(2022JH059)；山西省研究生教育创新项目资助(2024KY659) |
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| Lithium battery state of health estimation method based on a GA-SA-BP neural network |
| WU Qingfeng1,YANG Yitao1,LIU Liqun1,HU Xiufang1,BO Liming2,YANG Jiebao3 |
| (1. College of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China;
2. State Grid Shanxi Electric Power Research Institute, Taiyuan 030001, China; 3. Shanxi Institute of
Engineering and Technology, Yangquan 045000, China) |
| Abstract: |
| The state of health (SOH) of lithium batteries can characterize their aging status, and accurately estimating SOH is crucial for reliable operation. A GA-SA-BP neural network algorithm is proposed to improve the accuracy of SOH estimation to solve the problem of low convergence efficiency and susceptibility to local optima of BP neural networks optimized by simulated annealing (SA) and genetic algorithms (GA), which cannot reach the global optimum. First, the correlation between various health indicators (HI) and SOH in NASA’s publicly available dataset is analyzed, and the four HI values of lithium battery output voltage, output current, capacity, and equal voltage drop discharge time with higher correlation with SOH is selected as input values for the BP neural network to improve the accuracy of SOH estimation. Secondly, the GA-SA-BP neural network algorithm is proposed to estimate SOH, and the global optimal solution is found by jumping out of the local optimum when trapped in order to further improve the accuracy of SOH estimation. Finally, the results obtained on the NASA lithium battery dataset and lithium battery experimental testing platform indicate that the proposed approach improves the accuracy of SOH estimation compared to traditional BP neural networks, GA-BP neural networks, and SA-BP neural networks, and remains effective even in the absence of some data. |
| Key words: lithium battery estimate of state of health neural network health indicator |
