Aging-Aware Hypernetwork LSTM With Multi-Constraint Physics-Informed Neural Network (AAH-LSTM-PINN) for Lithium-Ion Battery SOC Estimation

Abstract

Accurate State-of-Charge (SOC) estimation of batteries remains challenging across aging states, temperature variations, and operating conditions. This paper introduces a novel Aging-Aware Hypernetwork Long Short-Term Memory with Multi-Constraint Physics-Informed Neural Network (AAH-LSTM-PINN) aimed at addressing some of the most pertinent challenges of estimating battery SOC through three key contributions. First, the introduction of a continuous four-component age parameter to quantify the temporal progression, the effects of capacity fade, resistance growth, and thermal stress. Second, the ability of a hypernetwork architecture to generate age-specific adaptation weights for explicit feature transformation. Third, the incorporation of physics-informed constraints based on Coulomb Counting (CC), Equivalent Circuit Model (ECM), and Arrhenius temperature with weights optimized using a three-phase systematic grid search. The effectiveness of the AAH-LSTM-PINN was evaluated on the Lithium-ion Battery (LIB) dataset under three extreme scenarios, which are temperature interpolation with a 0°C holdout, temperature extrapolation with a -20°C holdout, and drive cycle generalization. AAH-LSTM-PINN achieved 0.516% average Root Mean Square Error (RMSE) which gives a 42.8% improvement over age-agnostic LSTM. Age awareness, hypernetwork adaptation, and physics-informed constraints implementation contributed to 27.8%, 15.3%, and 6.5% improvement respectively. The AAH-LSTM-PINN framework has a real-time inference capability of 11.94μs, which is appropriate for embedded Battery Management Systems (BMS) while also offering robust generalization to unseen situations. This work advanced the battery SOC estimation by showing that accurate state estimation across different aging states and operational conditions is possible when explicit age-aware mechanisms, coupled with deep learning and physics-informed regularization are employed.