State-of-health estimation for Li-ion batteries by combing the incremental capacity analysis method with grey relational analysis
By Xiaoyu Li, Zhenpo Wang, Lei Zhang, Changfu Zou, D. Dorrell. Journal of Power Sources, 2019
329 citations recorded by the source corpus.
Abstract An accurate battery state-of-health (SOH) monitoring is crucial to guarantee safe and reliable operation of electric vehicles (EVs). In this paper, an incremental capacity analysis (ICA) method for battery SOH estimation is proposed. This uses grey relational analysis in combination with the entropy weight method. First, an interpolation method is employed to obtain incremental capacity (IC) curves. The health indexes are then extracted from the partial IC curves for grey relational analysis, and the entropy weight method is used to evaluate the significance of each health index. The battery SOH is assessed by calculating the grey relational degree between the reference and comparative sequences. Experimental tests are conducted on two battery cells with the same specifications to verify the efficacy of the proposed method. The results show that the maximum estimation error is limited to within 4%, thus proving its effectiveness.
Subfields: Automotive Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/7fe253df837c7758e5fbffd4b2d239dbfce625a2
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Citations in the corpus
- Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning (2020, methodology): “The existing studies mainly focus on correlation analysis, such as gray correlation [38], Pearson correlation coefficient [39], et al.”
- Advances in Batteries, Battery Modeling, Battery Management System, Battery Thermal Management, SOC, SOH, and Charge/Discharge Characteristics in EV Applications (2023, methodology): “of Least Square-based algorithms, as discussed in [234] and”