Fast LiFePO4 Battery State-of-health Estimation Via Force-Electrical Coupled Signal and Fixed References to Overcome Voltage Plateaus

The long voltage plateaus in LiFePO4 (LFP) batteries may cause the failure of existing data-driven state-of-health (SOH) estimation methods due to overly long health feature (HF) sampling intervals, rendering them almost indistinguishable from direct capacity measurement. However, most studies still rely solely on electrical signals, whereas force signal-based methods remain underdeveloped with limited practicality. To this end, we first propose a force-electrical coupled method (FECM) for feature engineering, which uses the second inflection points of charging force curves (SIPCF) as unified fixed references and deeply integrates force and electrical signals during feature extraction, rather than extracting HFs separately from each signal as model inputs. This increases the likelihood of extracting more effective HFs. These HFs reflect multidimensional aging mechanisms and comprehensively describe aging processes. We employ support vector regression (SVR) with hyperparameters optimized by particle swarm optimization (PSO) for fast LFP battery SOH estimation. Across the two in-house battery aging datasets with different initial conditions, the mean root-mean-square error (RMSE) for SOH estimation is only 0.45%. Compared with five typical methods, FECM improves estimation accuracy by up to 78% while reducing sampling time by up to 74%. Additionally, extracting specific HFs can further balance sampling time and estimation accuracy.