A novel charged state prediction method of the lithium ion battery packs based on the composite equivalent modeling and improved splice Kalman filtering algorithm

State of health (SOH) is a key parameter to assess lithium-ion battery feasibility for secondary usage applications SOH estimation based on machine learning has attracted great attention in recent years and holds potentials for battery informatization and cloud battery management techniques In this article, a comprehensive study of the data-driven SOH estimation methods is conducted A new classification for health indicators (HIs) is proposed where the HIs are divided into the measured variables and calculated variables To illustrate the significance of data preprocessing, four noise reduction methods are assessed in the HIs extraction process; different feature selection methods, including filter-based method, wrapper-based method, and fusion-based method, are applied to select HIs subsets The four widely used machine learning algorithms, including artificial neural network, support vector machine, relevance vector machine, and Gaussian process regression (GPR), are applied and compared In order to evaluate the estimation performance in potential real usages under future big data era, the three HIs selection methods and four machine learning methods are evaluated using three public data sets and two estimation strategies The results show that the combination of the fusion-based selection method and GPR has an overall superior estimation performance in terms of both accuracy and computational efficiency

Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning

Accurate, reliable, and robust prognosis of the state of health (SOH) and remaining useful life (RUL) plays a significant role in battery pack management for electric vehicles. However, there still exist challenges in computational cost, storage requirement, health indicators extraction, and algorithm design. This paper proposes a novel dual Gaussian process regression model for the SOH and RUL prognosis of battery packs. The multi-stage constant current charging method is used for aging tests. Health indicators are extracted from partial charging curves, in which capacity loss, resistance increase, and inconsistency variation are examined. A dual Gaussian process regression model is designed to predict SOH over the entire cycle life and RUL near the end of life. Experimental results show that the predictions of SOH and RUL are accurate, reliable, and robust. The maximum absolute errors and root mean square errors of SOH predictions are less than 1.3% and 0.5%, respectively, and the maximum absolute errors and root mean square errors of RUL predictions are 2 cycles and 1 cycle, respectively. The computation time for the entire training and testing process is less than 5 seconds. This article shows the prospect of health prognosis using multiple health indicators in automotive applications.

Health Prognosis for Electric Vehicle Battery Packs: A Data-Driven Approach

Lithium-ion battery capacity estimation — A pruned convolutional neural network approach assisted with transfer learning

State of health (SOH) monitoring and remaining useful life (RUL) prediction are the key to ensuring the safe use of lithium-ion batteries. However, the commonly used models are inefficient in predicting accuracy and do not have the ability to capture local regeneration of battery cells. In this paper, a temporal convolutional network (TCN) based SOH monitoring model framework of lithium-ion batteries is proposed. Causal convolution and dilated convolution techniques are used in the model to improve the ability of the model to capture local capacity regeneration, thus improving the overall prediction accuracy of the model. Residual connection and dropout technologies are used to improve the training speed of the model and avoid overfitting in deep network. The empirical mode decomposition (EMD) technology is used to denoise the offline data in RUL prediction, so as to avoid RUL prediction errors caused by local regeneration. The proposed model is verified on two kinds of datasets and the results show that it has the ability to capture local regeneration phenomena in Lithium-ion batteries. Compared with the commonly used models, it has higher accuracy and stronger robustness in SOH monitoring and RUL prediction.

/pdf/state-of-health-monitoring-and-remaining-useful-life-1cq7bolwlw.pdf

Ze Cheng

Papers

A data-driven remaining capacity estimation approach for lithium-ion batteries based on charging health feature extraction