Accurate state-of-health (SOH) estimation is critical for reliable and safe operation of lithium-ion batteries. However, reliable and stable battery SOH estimation remains challenging due to diverse battery types and operating conditions. In this paper, we propose a physics-informed neural network (PINN) for accurate and stable estimation of battery SOH. Specifically, we model the attributes that affect the battery degradation from the perspective of empirical degradation and state space equations, and utilize neural networks to capture battery degradation dynamics. A general feature extraction method is designed to extract statistical features from a short period of data before the battery is fully charged, enabling our method applicable to different battery types and charge/discharge protocols. Additionally, we generate a comprehensive dataset consisting of 55 lithium-nickel-cobalt-manganese-oxide (NCM) batteries. Combined with three other datasets from different manufacturers, we use a total of 387 batteries with 310,705 samples to validate our method. The mean absolute percentage error (MAPE) is 0.87%. Our proposed PINN has demonstrated remarkable performance in regular experiments, small sample experiments, and transfer experiments when compared to alternative neural networks. This study highlights the promise of physics-informed machine learning for battery degradation modeling and SOH estimation.

Lithium-ion batteries are widely used in modern society. Accurate modeling and prognosis are fundamental to achieving reliable operation of lithium-ion batteries. Accurately predicting the end-of-discharge (EOD) is critical for operations and decision-making when they are deployed to critical missions. Existing data-driven methods have large model parameters, which require a large amount of labeled data and the models are not interpretable. Model-based methods need to know many parameters related to battery design, and the models are difficult to solve. To bridge these gaps, this study proposes a physics-informed neural network (PINN), called battery neural network (BattNN), for battery modeling and prognosis. Specifically, we propose to design the structure of BattNN based on the equivalent circuit model (ECM). Therefore, the entire BattNN is completely constrained by physics. Its forward propagation process follows the physical laws, and the model is inherently interpretable. To validate the proposed method, we conduct the discharge experiments under random loading profiles and develop our dataset. Analysis and experiments show that the proposed BattNN only needs approximately 30 samples for training, and the average required training time is 21.5 s. Experimental results on three datasets show that our method can achieve high prediction accuracy with only a few learnable parameters. Compared with other neural networks, the prediction MAEs of our BattNN are reduced by 77.1%, 67.4%, and 75.0% on three datasets, respectively. Our data and code will be available at: https://github.com/wang-fujin/BattNN.

Accurately estimating the state of health (SOH) of satellite batteries is crucial for reliable satellite operation and safe decision-making. However, the complex degradation processes of batteries in space, coupled with limited labeled data, pose challenges for existing SOH estimation methods. We propose a physics-informed neural network (PINN) for satellite battery SOH estimation using only an extreme segment data within 2 min. To address battery degradation complexity, we model degradation attributes from an empirical and state-space perspective. We combine neural networks with physical principles to capture the degradation dynamics of batteries, enabling accurate SOH estimation. To tackle limited and inconsistent data acquisition per cycle, health features are constructed from relaxation voltage data within 2 min, ensuring independence from charging/discharging strategies. Unique factors specific to satellite operations are also considered. To validate the proposed method, the battery degradation experiments simulating a geostationary earth orbit (GEO) satellite are conducted, and an aging dataset is generated. The proposed method achieves a mean absolute percentage error (MAPE) of 1.33% for SOH estimation using a 2-min extreme data segment. Our studies provide valuable insights into battery prognosis and health management for space missions, aiding in optimizing battery performance and longevity in satellite operations.