摘要
为了解决传统机器学习的电池健康状态(state of health,SOH)评估方法在应用于大规模电化学储能电站的应用场景时,存在的数据处理慢和评估效果差等问题,提出了一种基于单体到模组迁移的磷酸铁锂储能系统SOH评估方法。基于实验获取了磷酸铁锂电池和电池组的老化数据集,构建了迁移学习的SOH评估模型框架,验证了小规模样本再训练模型的评估效果;测试了采用长短时记忆神经网络(long short-term memory networks,LSTM)和门控循环单元(gated recurrent unit,GRU)等模型的评估效果;对比了不同片段的短特征样本数据对评估结果的影响。研究结果证明,经过小规模样本数据优化后的电池单体模型能够实现对电池组SOH的评估;以GRU为主网络的迁移学习模型对电池组SOH的评估综合性能最优;采用电压区间为24.5~30 V片段短特征数据集的模型,能够进一步提高评估准确性和速度,对电池组SOH评估均方差可降低至0.1%,符合大规模储能电站场景下的使用需求。研究成果为电化学储能电站的运行状态评估方法提供可能的技术参考和数据支撑。
A state of health(SOH)evaluation method for LiFePO₄(LFP)energy storage system based on cell-to-module transfer was proposed in this paper in order to solve the problems of slow data processing and poor evaluation results when traditional machine learning battery SOH evaluation methods were applied in the scenario of large-scale electrochemical energy storage power station.Aging datasets of LFP battery cells and modules were acquired through experiments in this paper.The SOH evaluation model framework of transfer learning was constructed and verified to evaluate the effect by few-shot sample retraining.The evaluation effects of models of LSTM(long short-term memory networks)and GRU(gated recurrent unit)were tested.The effect of different segments of short feature sample data on the results was compared.The research results verify that the battery cell model optimized by few-shot sample data can realize the evaluation of the SOH of the battery module.The transfer learning model using GRU as the main network has the best overall performance in evaluating the SOH.The model adopting a short feature data set with a voltage range of 24.5~30 V segments can further improve the evaluation accuracy and speed.The mean square error of the model’s SOH evaluation of the battery pack can be reduced to 0.1%,which meet the needs of large-scale energy storage power station scenarios.The research results can provide possible technical reference and data support for the evaluation method of the operating status of electrochemical energy storage power plants.
作者
杨智鹏
宋政湘
孟锦豪
郑琨
YANG Zhipeng;SONG Zhengxiang;MENG Jinhao;ZHENG Kun(State Key Laboratory of Electrical Insulation and Power Equipment,Xi’an 710049,China;School of Electrical Engineering,Xi’an Jiaotong University,Xi’an 710049,China)
出处
《高电压技术》
EI
CAS
CSCD
北大核心
2023年第10期4142-4149,共8页
High Voltage Engineering
基金
新疆维吾尔自治区重点研发计划(2022B01019-2)。
