Lightweight State of Health Estimation for Lithium-Ion Batteries Based on Hybrid PSO-NM Algorithm and Event-Triggered Adaptive Closed-Loop Correction

Abstract

Advanced battery management systems (BMS) demand high-precision online prediction of lithium-ion battery state of health (SOH). To address engineering challenges where deep learning and multi-physics models struggle with edge deployment on automotive-grade microcontrollers (MCUs) due to high memory footprints and inference latency, and where traditional methods obscure true capacity regeneration during rest periods, this study proposes a lightweight, low-latency, and high-precision Adaptive Closed-loop Correction Model (ACCM). First, the equal-voltage-drop discharge time (EVDDT) and maximum temperature rise (ΔT) are extracted as indirect health indicators (HIs). Subsequently, a double-exponential model with explicit capacity regeneration compensation (CRC-DEM) is established, utilizing a Hybrid Particle Swarm Optimization-Nelder-Mead (Hybrid PSO-NM) algorithm for initial parameter identification. Furthermore, through an event-triggered adaptive closed-loop correction driven by HIs, an asymmetric time-window weighted objective function is employed to successfully overcome the historical inertia trap typical of the Open-loop Extrapolation Prediction Model (OEPM). Blind testing on NASA battery aging datasets demonstrates that this closed-loop model accurately tracks capacity regeneration while restricting the maximum root mean square error (RMSE) for SOH and capacity within 1.4% (average RMSE: 1.1%). With a peak dynamic memory of merely 4.08 KB and a maximum online inference latency of 0.1045 ms, the proposed model demonstrates superior lightweight efficiency and competitive accuracy compared to traditional deep learning approaches (e.g., CNN, BiLSTM, CNN-LSTM-Attention).