Joint Estimation of Inconsistency and State of Health for Series Battery Packs

Abstract

Battery packs are applied in various areas (e.g., electric vehicles, energy storage, space, mining, etc.), which requires the state of health (SOH) to be accurately estimated. Inconsistency, also known as cell variation, is considered a significant evaluation index that greatly affects the degradation of battery pack. This paper proposes a novel joint inconsistency and SOH estimation method under cycling, which fills the gap of joint estimation based on the fast-charging process for electric vehicles. First, fifteen features are extracted from current change points during the partial charging process. Then, a joint estimation system is designed, where fusion weights are obtained by the analytic hierarchy process and multi-scale sample entropy to evaluate inconsistency. A wrapper is used to select the optimal feature subset, and Gaussian process regression is implemented to estimate the SOH. Finally, the estimation performance is assessed by the test data. The results show that the inconsistency evaluation can reflect the aging conditions, and the inconsistency does affect the aging process. The wrapper selection method improves the accuracy of SOH estimation by about 75.8% compared to the traditional filter method when only 10% of data is used for model training. The maximum absolute error and root mean square error are 2.58% and 0.93%, respectively.

Health prognostics for lithium-ion batteries: mechanisms, methods, and prospects

“…In addition, lithium-ion batteries are widely used in other fields, such as satellites, laptops, and smart grids [5][6][7] . However, the internal electrochemical mechanism of lithium-ion batteries is very complex due to their dynamic, timevarying, and nonlinear characteristics 8,9 . Besides the main reactions, aging-related side reactions occur at the same time.…”

“…[5][6][7] However, the internal electrochemical mechanism of lithium-ion batteries is very complex due to their dynamic, time-varying, and nonlinear characteristics. 8,9 Besides the main reactions, aging-related side reactions occur at the same time. The capacity of lithium-ion batteries will gradually degrade, and the internal resistance will increase with storage and usage, which can be regarded as calendar aging and cyclic aging, respectively.…”

Data-Based Interpretable Modeling for Property Forecasting and Sensitivity Analysis of Li-ion Battery Electrode

“…With the rapid development of machine learning and intelligent computing techniques, data-based modeling solutions have become one of many popular tools for the final battery production management [5,6]. A great deal of research has been conducted for internal battery states estimation [7,8], battery aging prognostics [9,10], battery fault diagnostics [11], cell equalization management [12], charging control [13,14], and energy management [15,16]. Usually, effective battery management can be obtained based on suitable data-based models development [17].…”

Internal Short Circuit Detection for Parallel-Connected Battery Cells Using Convolutional Neural Network

“…First, the solution to the problem will be developed assuming sensor limitation, i.e., only one current sensor and one voltage sensor for a parallel battery string. Second, non-uniformity in cell capacity and internal resistance is assumed as cell-to-cell variations inevitably exist under manufacturing variability or non-uniform operating conditions (e.g., exposed to different temperatures) [18][19][20]. In this work, ISC detection is formulated as a binary classification problem.…”