[目的]锂电池健康状态(state of health,SOH)的精确预测评估可以提高电池设备的安全性,降低故障的发生率.针对数据驱动方法在模型训练过程中需要大量标签样本数据的问题,提出了一种新的基于扩散模型和双向长短期记忆网络的锂电池SOH估计方法.[方法]首先,建立电池充电时间、电压和温度三者间的长期依赖关系云图;其次,设计一个时空信息捕捉模块,将该模块捕获的长期依赖信息作为扩散模型的生成条件,赋予扩散模型电池SOH数据生成能力;最后,利用双向长短期记忆网络(Bi-LSTM)对部分由原始数据和生成数据混合而成的电池数据集进行训练,并利用剩余的原始数据作为测试集对所提方法进行验证.[结果]验证结果表明,该方法不仅可以减少收集电池数据类型的周期和成本,而且能够有效预测电池SOH.[结论]该方法在电池SOH估计上具备良好的精度,可进一步探索其他电池数据集组合,优化模型结构,提高电池管理系统.
SOH Estimation of Li-Battery Based on Diffusion Model and Bi-LSTM
[Purposes]Accurate predictive assessment of the state of health(SOH)of lithium batteries can improve the safety of battery devices and reduce the risk of failure.To solve the problem that the data-driven method requires a large amount of label sample data in the process of model training,a new bat-tery SOH estimation method is proposed.[Methods]Firstly,the long-term dependence of battery charg-ing time,voltage and temperature was established.Then,a spatiotemporal perception module is de-signed,and the long-term dependent information captured by the module is used as the generation condi-tion of diffusion model,and the SOH data generation capability is given to the battery of diffusion model.Lastly,bidirectional long short-term memory(Bi-LSTM)network is used to train part of the original and generated hybrid battery data set,and the remaining raw data is used as a test set to verify the method.[Findings]The verification results show that this method can effectively predict SOH while reducing the cycle and cost of collecting battery data types.[Conclusions]This method has a good accuracy in SOH estimation,and can further explore other battery data set combinations,optimize the model structure,and improve the battery management system.
SOH of batterydata-drivenspatiotemporal informationdiffusion modelBi-LSTM
万方数据
维普
