Edge-Computing-Oriented Lightweight State of Charge Estimation Method for Energy Storage Batteries

Abstract

Long short-term memory (LSTM) networks have been widely applied to battery state-of-charge (SOC) estimation because of their capability to capture nonlinear battery dynamics and long-term temporal dependencies. However, deploying high-accuracy LSTM-based SOC estimation models on resource-constrained edge devices remains challenging, as conventional model compression methods often disrupt the gated structures responsible for temporal dependency modeling, resulting in degraded estimation accuracy. To address this issue, a lightweight SOC estimation method, termed Gate-Aware Pruned LSTM Enhanced by Knowledge Distillation (GAP-LSTM-KD), is proposed. A first-order Butterworth filter is employed to suppress measurement noise. A gate-aware pruning strategy is developed to evaluate hidden-unit importance by jointly considering gate weights and gradient sensitivity, enabling structural compression while preserving critical temporal information. Knowledge distillation is further introduced to compensate for the representation loss caused by pruning. Experimental results show that, at a 40% pruning rate, the proposed model reduces edge inference latency by more than 50%, while parameter count and computational cost are reduced by over 60%. The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) remain no higher than 0.76% and 0.60%, respectively. The proposed method maintains competitive SOC estimation accuracy across different temperatures and operating conditions while substantially improving computational efficiency.