Battery pack capacity estimation for electric vehicles based on enhanced machine learning and field data

Accurate capacity estimation is of great importance for the reliable state monitoring, timely maintenance, and second-life utilization of lithium-ion batteries. Despite numerous works on battery capacity estimation using laboratory datasets, most of them are applied to battery cells and lack satisfactory fidelity when extended to real-world electric vehicle (EV) battery packs. The challenges intensify for largesized EV battery packs, where unpredictable operating profiles and low-quality data acquisition hinder precise capacity estimation. To fill the gap, this study introduces a novel data-driven battery pack capacity estimation method grounded in field data. The proposed approach begins by determining labeled capacity through an innovative combination of the inverse ampere-hour integral, open circuit voltagebased, and resistance-based correction methods. Then, multiple health features are extracted from incremental capacity curves, voltage curves, equivalent circuit model parameters, and operating temperature to thoroughly characterize battery aging behavior. A feature selection procedure is performed to determine the optimal feature set based on the Pearson correlation coefficient. Moreover, a convolutional neural network and bidirectional gated recurrent unit, enhanced by an attention mechanism, are employed to estimate the battery pack capacity in real-world EV applications. Finally, the proposed method is validated with a field dataset from two EVs, covering approximately 35,000 kilometers. The results demonstrate that the proposed method exhibits better estimation performance with an error of less than 1.1% compared to existing methods. This work shows great potential for accurate large-sized EV battery pack capacity estimation based on field data, which provides significant insights into reliable labeled capacity calculation, effective features extraction, and machine learning-enabled health diagnosis. (c) 2024 Science Press and Dalian Institute of Chemical Physics, Chinese Academy of Sciences. Published by ELSEVIER B.V. and Science Press. All rights reserved.