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.
Highlights
Battery packs are widely used in many important areas, such as electric vehicles (EVs), plug-in electric vehicles (PHEVs), smart grids, and aerospace [1]
The fusion weights-based battery pack inconsistency evaluation results are compared with the analytic hierarchy process (AHP)-based and MSEbased evaluation results. It shows that inconsistency evaluations do not have obvious differences in the early stages of aging cycles, which indicates that all three methods could be applied for inconsistency evaluation during this period
The state of health (SOH) estimation results based on wrapper feature selection and Gaussian progress regression (GPR) prediction are evaluated
Summary
Battery packs are widely used in many important areas, such as electric vehicles (EVs), plug-in electric vehicles (PHEVs), smart grids, and aerospace [1]. A battery pack consists of hundreds of battery cells connected in series and parallel, which makes it difficult to manage [2]. Due to inconsistencies (variation of the cells) in production, packaging, and usage, the state of health (SOH) of a battery pack deteriorates faster than a single-battery cell, making it hard to estimate [3]. The inconsistency evaluation and SOH estimation of battery packs are drawing increasing attention. The inconsistencies of battery packs mainly include internal and external parameter inconsistencies [4]. Internal parameters such as capacity, resistance, open-circuit voltage (OCV), and state of charge (SOC) may have inconsistencies
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