Department of Unmanned Vehicle Engineering, Sejong University, Seoul 05006, Republic of Korea.
Department of Electrical Engineering, The Ibadat International University, Islamabad 54590, Pakistan.
Comput Intell Neurosci. 2022 Jun 13;2022:1575303. doi: 10.1155/2022/1575303. eCollection 2022.
In this paper, a novel multistep ahead predictor based upon a fusion of kernel recursive least square (KRLS) and Gaussian process regression (GPR) is proposed for the accurate prediction of the state of health (SoH) and remaining useful life (RUL) of lithium-ion batteries. The empirical mode decomposition is utilized to divide the battery capacity into local regeneration (intrinsic mode functions) and global degradation (residual). The KRLS and GPR submodels are employed to track the residual and intrinsic mode functions. For RUL, the KRLS predicted residual signal is utilized. The online available experimental battery aging data are used for the evaluation of the proposed model. The comparison analysis with other methodologies (i.e., GPR, KRLS, empirical mode decomposition with GPR, and empirical mode decomposition with KRLS) reveals the distinctiveness and superiority of the proposed approach. For 1-step ahead prediction, the proposed method tracks the trajectory with the root mean square error (RMSE) of 0.2299, and the increase of only 0.2243 RMSE is noted for 30-step ahead prediction. The RUL prediction using residual signal shows an increase of 3 to 5% in accuracy. This proposed methodology is a prospective approach for an efficient battery health prognostic.
本文提出了一种基于核递归最小二乘法（KRLS）和高斯过程回归（GPR）融合的新型多步预测器，用于准确预测锂离子电池的健康状态（SoH）和剩余使用寿命（RUL）。利用经验模态分解将电池容量分为局部再生（固有模态函数）和全局降解（残差）。KRLS 和 GPR 子模型用于跟踪残差和固有模态函数。对于 RUL，利用 KRLS 预测的残差信号。使用在线可用的实验电池老化数据来评估所提出的模型。与其他方法（即 GPR、KRLS、基于 GPR 的经验模态分解和基于 KRLS 的经验模态分解）的比较分析表明了所提出方法的独特性和优越性。对于 1 步预测，所提出的方法以均方根误差（RMSE）为 0.2299 跟踪轨迹，而 30 步预测仅增加了 0.2243 RMSE。使用残差信号进行 RUL 预测可提高 3%至 5%的准确性。该方法是一种高效电池健康预测的有前景的方法。
