The reliable assessment of battery degradation is fundamental for safe and efficient battery utilization. As an important in situ 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.

对电池退化的可靠评估是安全高效利用电池的基础。作为重要的现场健康诊断方法，增量容量（IC）分析高度依赖于低噪声恒流曲线，这违反了现实生活场景。在这里，首次报告了无模型拟合过程，以从嘈杂甚至电流变化的轮廓重建 IC 轨迹。基于 22 个电池和三个案例研究的结果，重建 IC 轨迹中峰值位置的误差可以限制在 0.25% 以内。通过从重建的 IC 轨迹中提取健康指标，可以通过简单的线性映射轻松确定健康状态，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，