With the assistance of artificial intelligence,advanced health prognosis technique plays a critical role in the lithium-ion(Li-ion) batteries management system.However,conventional data-driven early aging prediction e...With the assistance of artificial intelligence,advanced health prognosis technique plays a critical role in the lithium-ion(Li-ion) batteries management system.However,conventional data-driven early aging prediction exhibited dramatic drawbacks,i.e.,volatile capacity nonlinear fading trajectories create obstacles to the accurate multistep ahead prediction due to the complex working conditions of batteries.Herein,a novel deep learning model is proposed to achieve a universal and accurate Li-ion battery aging prognosis.Two battery datasets with various electrode types and cycling conditions are developed to validate the proposed approaches.Knee-point probability(KPP),extracted from the capacity loss curve,is first proposed to detect knee points and improve state-of-health(SOH) predictive accuracy,especially during periods of rapid capacity decline.Using one-cycle data of partial raw voltage as the model input,the SOH and KPP can be simultaneously predicted at multistep ahead,whereas the conventional method showed worse accuracy.Furthermore,to explore the underlying characteristics among various degradation tendencies,an online model update strategy is developed by leveraging the adversarial adaptationinduced transfer learning technique.This work gains new sights into the comprehensive Li-ion battery management and prognosis framework through decomposing capacity degradation trajectories and adversarial learning on the unlabeled samples.展开更多
对锂离子电池的荷电状态(state of charge,SOC),健康状态(state of health,SOH)和剩余使用寿命(remaining useful life,RUL)进行准确估计是锂离子电池安全稳定运行的重要保障,该文提出一种结合充电电压片段和等效电路模型(equivalent ci...对锂离子电池的荷电状态(state of charge,SOC),健康状态(state of health,SOH)和剩余使用寿命(remaining useful life,RUL)进行准确估计是锂离子电池安全稳定运行的重要保障,该文提出一种结合充电电压片段和等效电路模型(equivalent circuit model,ECM)-数据驱动(data driven method,DDM)融合方法的锂离子电池SOC-SOH-RUL联合估计框架,实现对电池全生命周期的SOC、SOH和RUL的联合估计。首先提取与电池当前容量关联度最高的恒流充电电压曲线片段的上升时间作为健康特征(health factor,HF),利用外部训练集电池的老化数据,离线建立电池老化的最小二乘支持向量机(least squares support vector machine,LSSVM)模型。应用阶段,通过采集待测电池充电电压片段提取HF并代入老化模型进行SOH估计;对该电压区段进行ECM拟合,用阻容参数辨识值和容量估计值建立状态方程和观测方程,结合无迹卡尔曼滤波算法(unscented Kalman filter,UKF)进行SOC估计;用高斯过程回归(Gaussian process regression,GPR)对当前循环次数以前的DV随循环次数的变化进行映射,并借助老化模型预测容量的退化轨迹,实现RUL估计。实验结果表明,所提方法能够联合实现SOC、SOH和RUL的长期稳定估计。展开更多
基金supported by the financial support from the National Key Research and Development Program of China(2022YFB3807200)the Fundamental Research Funds for the Central Universities(2242022K330047)+3 种基金the dual creative talents from Jiangsu Province(JSSCBS20210152,JSSCBS20210100)the National Natural Science Foundation of Jiangsu Province(BK20221456,BK20200375)the Natural Science Foundation of China with(22109021)the Research Fund Program of Guangdong Provincial Key Lab of Green Chemical Product Technology(6802008024)。
文摘With the assistance of artificial intelligence,advanced health prognosis technique plays a critical role in the lithium-ion(Li-ion) batteries management system.However,conventional data-driven early aging prediction exhibited dramatic drawbacks,i.e.,volatile capacity nonlinear fading trajectories create obstacles to the accurate multistep ahead prediction due to the complex working conditions of batteries.Herein,a novel deep learning model is proposed to achieve a universal and accurate Li-ion battery aging prognosis.Two battery datasets with various electrode types and cycling conditions are developed to validate the proposed approaches.Knee-point probability(KPP),extracted from the capacity loss curve,is first proposed to detect knee points and improve state-of-health(SOH) predictive accuracy,especially during periods of rapid capacity decline.Using one-cycle data of partial raw voltage as the model input,the SOH and KPP can be simultaneously predicted at multistep ahead,whereas the conventional method showed worse accuracy.Furthermore,to explore the underlying characteristics among various degradation tendencies,an online model update strategy is developed by leveraging the adversarial adaptationinduced transfer learning technique.This work gains new sights into the comprehensive Li-ion battery management and prognosis framework through decomposing capacity degradation trajectories and adversarial learning on the unlabeled samples.
文摘对锂离子电池的荷电状态(state of charge,SOC),健康状态(state of health,SOH)和剩余使用寿命(remaining useful life,RUL)进行准确估计是锂离子电池安全稳定运行的重要保障,该文提出一种结合充电电压片段和等效电路模型(equivalent circuit model,ECM)-数据驱动(data driven method,DDM)融合方法的锂离子电池SOC-SOH-RUL联合估计框架,实现对电池全生命周期的SOC、SOH和RUL的联合估计。首先提取与电池当前容量关联度最高的恒流充电电压曲线片段的上升时间作为健康特征(health factor,HF),利用外部训练集电池的老化数据,离线建立电池老化的最小二乘支持向量机(least squares support vector machine,LSSVM)模型。应用阶段,通过采集待测电池充电电压片段提取HF并代入老化模型进行SOH估计;对该电压区段进行ECM拟合,用阻容参数辨识值和容量估计值建立状态方程和观测方程,结合无迹卡尔曼滤波算法(unscented Kalman filter,UKF)进行SOC估计;用高斯过程回归(Gaussian process regression,GPR)对当前循环次数以前的DV随循环次数的变化进行映射,并借助老化模型预测容量的退化轨迹,实现RUL估计。实验结果表明,所提方法能够联合实现SOC、SOH和RUL的长期稳定估计。
