Tang Xiaopeng, Wang Yujie, Liu Qi, Gao Furong
Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR 999077, China.
Department of Automation, University of Science and Technology of China, Hefei 230027, China.
iScience. 2021 Sep 10;24(10):103103. doi: 10.1016/j.isci.2021.103103. eCollection 2021 Oct 22.
The reliable assessment of battery degradation is fundamental for safe and efficient battery utilization. As an important health diagnostic method, the incremental capacity (IC) analysis relies highly on the low-noise constant-current profiles, which violates the real-life scenarios. Here, a model-free fitting process is reported, for the first time, to reconstruct the IC trajectories from noisy or even current-varying profiles. Based on the results from overall 22 batteries with three case studies, the errors of the peak positions in the reconstructed IC trajectories can be bounded within only 0.25%. With health indicators extracted from the reconstructed IC trajectories, the state of health can be readily determined from simple linear mappings, with estimation error lower than 1% only. By enabling the IC-based methods under complex load profiles, enhanced health assessment could be implemented to improve the reliability of the power systems and further promoting a more sustainable society.
可靠评估电池退化对于安全高效地使用电池至关重要。作为一种重要的健康诊断方法,增量容量(IC)分析高度依赖低噪声恒流曲线,这与实际应用场景不符。在此,首次报道了一种无模型拟合过程,用于从噪声甚至电流变化的曲线中重建IC轨迹。基于22个电池的三个案例研究结果,重建IC轨迹中峰值位置的误差可控制在仅0.25%以内。通过从重建的IC轨迹中提取健康指标,仅通过简单的线性映射就可以轻松确定健康状态,估计误差仅低于1%。通过在复杂负载曲线下启用基于IC的方法,可以实施增强的健康评估,以提高电力系统的可靠性,并进一步推动社会更加可持续发展。