【作者】 刘健；

【导师】 陈自强；

【作者基本信息】 上海交通大学 ， 轮机工程（专业学位）， 2019， 硕士

【摘要】 锂离子电池的健康状态与剩余寿命对系统的安全性与可靠性至关重要。进行锂离子电池剩余寿命准确和可靠预测对预防电池突然失效、提供维护保养建议、延长电池使用寿命有重要意义。针对锂离子电池系统具备时变、动态、非线性等特征,本文从具备不确定性能力的高斯过程回归模型入手,从预测精度、退化状态识别、在线适用性等方面开展锂离子电池剩余寿命预测方法研究。首先,分别从权值空间论和函数空间法两个角度阐述高斯过程回归模型建立过程与预测原理;确定均值函数和协方差函数是建立高斯过程回归模型的关键,基于最大似然估计和共轭梯度法优化超参数可以确定后验分布;并给出通过训练数据和测试数据建立高斯过程回归模型的流程。然后,基于随机电流放电下的锂离子电池老化实验,模拟锂离子电池的真实使用情况。从数据驱动方法中选取具备不确定性表达能力的高斯过程回归模型,选定核函数后通过训练数据来优化超参数建立预测模型。用不同实验温度和随机放电模式下的电池充放电循环实验数据验证预测结果。其次,针对锂离子电池在线寿命预测及容量非线性退化问题,构建基于等压差充电时间的健康状态估计与剩余寿命预测方法。基于两公开锂离子电池老化实验数据集,从恒流充电过程中提取等压差充电时间序列,进行不同提前步数和不同初始条件下的健康状态估计;采用组合核函数建立高斯过程回归模型,利用粒子群优化算法全局搜索最优超参数以优化模型;将等压差充电时间作为健康因子,通过预测等压差充电时间进行锂离子电池容量估计与寿命预测。通过实验证明该方法可有效预测容量非线性退化轨迹,具备良好的泛化能力和局部变化学习能力。剩余寿命预测结果具有较高的准确性及不确定性表达能力。最后,提出一种基于多高斯过程回归模型和健康因子的锂离子电池剩余寿命预测方法。从恒流恒压充电过程中提取3个健康因子,用Pearson和Spearman指数分析健康因子与电池容量之间的相关性。基于组合核函数优化的高斯过程回归模型,以健康因子和充放电周期数据建立高斯过程回归模型以预测健康因子,以健康因子和容量数据建立高斯过程回归模型以预测容量,获得剩余寿命预测结果。基于两个不同的实验数据集,实现锂离子电池在单点和长期的剩余寿命预测。更多还原

【Abstract】 The state of health and remaining useful life of lithium-ion battery is very important for the safety and reliability of system.Achieving accurate and reliable remaining useful life of lithium-ion battery is of great significance to prevent sudden failure of battery,provide maintenance suggestions and prolong the service life of battery.Due to the time-varying,dynamic and nonlinear characteristics of lithium-ion battery system,this thesis carries out the remaining useful life prediction by using the Gaussian process regression model with the ability of uncertainty expression,which will contain the aspects of prediction accuracy,degradation state identification and online applicability.At first,the establishment process and prediction principle of the Gaussian process regression model are elaborated in terms of the weight space theory and the function space method,respectively.The determination of mean function and covariance function is the key to the establishment of the Gaussian process regression model.Then,the posterior distribution can be determined by optimizing the hyper-parameters based on the maximum likelihood estimation and conjugate gradient method.Besides,the establishment procedure of Gaussian process regression model through training data and test data is introduced.Secondly,in order to simulate the actual use of battery,the li-ion battery aging test using randomized discharging current has been carried out.Gaussian process regression model with uncertainty expression ability is proposed based on data-driven methods.After selecting the kernel function,the forecast model is established by training data to optimize hyper-parameters.The data set of charge and discharge tests of li-ion battery under randomized use is used to verify the prognosis results.Then,aiming at solving the difficulty in measuring capacity directly and capacity regeneration during state of health estimation and remaining useful life prediction for lithium-ion battery,a new method is proposed based on time interval of equal charging voltage difference.According to the data sets of charge and discharge tests of lithium-ion battery,the time interval of equal charging voltage difference is extracted during the constant current charge process of lithium-ion battery.With different steps ahead,the state of health estimation is carried out under different initial conditions.The Gaussian process regression model is optimized by using combined kernel functions and particle swarm optimization.The time interval of equal charging voltage difference can act as a health indicator for remaining useful life prediction of lithium-ion battery.The verification experiments are carried out.The results show that the proposed method can predict nonlinear degradation of capacity well and have high prediction accuracy and online remaining useful life prediction ability for lithium-ion battery.Finally,a novel method which combines indirect health indicator and Gaussian process regression model is presented for remaining useful life forecast.Three health indicators are extracted in constant current and constant voltage charge process.Both Pearson and Spearman rank correlation analytical approaches show that the correlations between health indicators and capacity are good.Then,the Gaussian process regression model is optimized with combined kernel functions to improve the ability of predicting the capacity regeneration.Next,based the online health indicator versus cycle number data,three Gaussian process regression models are built and the health indicators prognosis results are achieved at single point.The health indicators prediction results are added in the multidimensional Gaussian process regression model which is accomplished by using health indicators and capacity as input and output,respectively.The predicted capacity is used to compare with the threshold to acquire remaining useful life prediction results.The approach is validated by the two different aging test datasets.Results indicate that an accurate and reliable remaining useful life forecast of lithium-ion battery can be realized by using the proposed approach.更多还原