预测锂离子电池剩余使用寿命(remaining useful life,RUL)对于确保电池的安全和可靠使用至关重要.首先,针对电池容量序列因容量回升及外界干扰呈非线性变化、目标电池退化数据稀少,全生命周期数据难以获取的问题,本文结合变分模态分解(variational mode decomposition,VMD)和排列熵(permutation entropy,PE)的优势,对已有的其他类似衰退模式电池数据进行去噪重构,作为模型训练数据.其次,本文采用滚动预测策略,用滚动滑窗的方式对训练数据进行划分和拼接.然后,训练擅长捕捉全局依赖关系的Transformer网络.最后,预测过程当中输入目标电池部分数据,进行滚动迭代预测.本文先在马里兰大学先进生命周期工程中心(Center for Advanced Life Cycle Engineering,CALCE)提供的电池数据集上,采用留一评估,依次对其本身电池数据进行实验验证,实验证明本文预测方法的各项性能指标良好,4块电池RUL的平均相对误差为2.21%,具有较高的准确性.再基于美国航空航天局(National Aeronautics and Space Administration,NASA)的B0005电池进行模型泛化验证,B0005电池得到的RUL相对误差为2.34%,进一步验证了本文方法的有效性.
Accelerated life prediction of lithium-ion batteries using data-driven approaches
Predicting the remaining useful life(RUL)of lithium-ion batteries is crucial for maintaining their safe and reliable performance.This paper addresses several challenges such as nonlinear capacity changes due to recovery and external disturbances,sparse degradation data,and the difficulty in acquiring complete lifecycle data.The study introduces a novel approach that leverages variational mode decomposition and permutation entropy to denoise and reconstruct degradation data for similar batteries,normalizing it for effective model training.Additionally,a rolling prediction strategy is employed,using sliding windows to partition and concatenate training data.It then trains a Transformer network that is proficient at capturing global dependency relationships.For predictions,the initial 10%of the target battery data is iteratively used for rolling predictions.This approach's effectiveness is initially validated using battery capacity datasets from the Center for Advanced Life Cycle Engineering(CALCE)at the University of Maryland,using a leave-one-out evaluation.Experimental results demonstrate that the prediction method yields strong performance metrics,with an average relative error of 2.21%for RUL across four batteries.Additional validation is conducted with battery data from the National Aeronautics and Space Administration(NASA),specifically battery B0005,to assess model generalization.Battery B0005 achieves an RUL relative error of 2.34%,further confirming the method's effectiveness.
lithium-ion batteryVMDPEtransformerrapid life prediction
万方数据
维普
