Battery Management System (BMS)
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LFP State-of-Charge Estimation: Still a Problem Challenges for state-of-charge estimation: Voltage-based method suffers from path-dependent behavior + strong hysteresis + flat OCV plateau Current-based method suffers from high sensitivity to initial SOC + sensor offset/noise + accumulated error. Che et al. keep current-based method as the backbone and periodically reset SOC using short (30–600 s) rest segments in normal driving; A ML model uses recent current, voltage, and temperature history to correct the initial SOC
keep error <3.25% over >430 conditions using only low-frequency signals, suitable for on-board BMS.
Source: [1] Che et al., ACS Energy Lett., 2025; [2] Yan et al., eTransportation, 2025.
Yan et al. propose a voltage-based hybrid model: an LSTM hysteresis OCV model (SOC history + temperature) + a 2-RC ECM, with a slope-adaptive Kalman filter that adjusts covariances by OCV slope.
Across varied temperatures/currents/partial cycles, >95% of samples reach SOC MAE <0.56%;
RMSE is ≈46% lower than two benchmark state-of-the-art models.
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