Battery incremental capacity curve extraction by a two-dimensional Luenberger–Gaussian-moving-average filter

Xiaopeng Tang, Kailong Liu, Jingyi Lu, Boyang Liu, Xin Wang and Furong Gao

Applied Energy, 2020, vol. 280, issue C, No S0306261920313635

Abstract: Incremental capacity analysis is a popular tool for the evaluation of state-of-health in battery management. In digital systems, the incremental capacity is generally approximated with the ratio of the capacity difference to voltage difference (ΔQ∕ΔV), which unavoidably amplifies measurement noises. To enhance its resilience against noises and improve the estimation accuracy, a two-dimensional filter is designed by employing historical information from both time and batch (cycle) directions inspired by batch-wise repetitiveness of the incremental capacity trajectories. Specifically, in the batch direction, a Luenberger observer is utilised to provide a batch-to-batch smoothing at the beginning of each charging cycle, while in the time direction, a bias-corrected Gaussian moving average filter is applied to smooth the incremental capacity value with respect to the voltage at every sampling time. Experimental results show that the root-mean-square-error of the proposed filter is 50% lower than the benchmark algorithms, and the noise sensitivity is significantly reduced by 93%. When using incremental capacity peaks extracted from the proposed filter for state-of-health modelling, the width of the 99% confidence interval would be narrowed by 45%. Moreover, the model-free nature of the proposed method enables its application to different batteries, paving a reliable way for effective battery health assessment.

Keywords: Electric vehicle; Lithium-ion battery management; Incremental capacity analysis; State of health; Two-dimensional filtering (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (15)

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DOI: 10.1016/j.apenergy.2020.115895

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