Battery health prognostics are critical for ensuring safety, efficiency, and sustainability in modern energy systems. However, it has been challenging to achieve accurate and robust prognostics due to complex battery degradation behaviors with nonlinearity, noise, capacity regeneration, etc. Existing data-driven models capture temporal degradation features but often lack knowledge guidance, which leads to unreliable long-term health prognostics. To overcome these limitations, we propose Karma, a knowledge-aware model with frequency-adaptive learning for battery capacity estimation and remaining useful life prediction. The model first performs signal decomposition to derive battery signals in different frequency bands. A dual-stream deep learning architecture is developed, where one stream captures long-term low-frequency degradation trends and the other models high-frequency short-term dynamics. Karma regulates the prognostics with knowledge, where battery degradation is modeled as a double exponential function based on empirical studies. Our dual-stream model is used to optimize the parameters of the knowledge with particle filters to ensure physically consistent and reliable prognostics and uncertainty quantification. Experimental study demonstrates Karma's superior performance, achieving average error reductions of 50.6% and 32.6% over state-of-the-art algorithms for battery health prediction on two mainstream datasets, respectively. These results highlight Karma's robustness, generalizability, and potential for safer and more reliable battery management across diverse applications.

翻译：电池健康预测对于确保现代能源系统的安全、高效和可持续性至关重要。然而，由于电池退化行为具有非线性、噪声、容量再生等复杂性，实现准确且鲁棒的预测一直面临挑战。现有数据驱动模型能够捕捉时间退化特征，但往往缺乏知识引导，导致长期健康预测不可靠。为克服这些局限，我们提出Karma，一种用于电池容量估计与剩余使用寿命预测的、具备频率自适应学习的知识感知模型。该模型首先执行信号分解以获取不同频段的电池信号。我们开发了一种双流深度学习架构，其中一流捕捉长期低频退化趋势，另一流建模高频短期动态。Karma通过知识调控预测过程，其中基于实证研究将电池退化建模为双指数函数。我们的双流模型结合粒子滤波器优化知识参数，以确保物理一致且可靠的预测及不确定性量化。实验研究表明，Karma在两个主流数据集上的电池健康预测任务中，分别比最先进算法平均误差降低了50.6%和32.6%，展现出卓越性能。这些结果凸显了Karma的鲁棒性、泛化能力及其在不同应用中实现更安全可靠电池管理的潜力。