arXiv subjects

Guangdi Hu

Publications and source records attributed to Guangdi Hu.

4 recordsLinked to original sources

arXiv2026

Physics-guided residual Kalman learning for state-of-charge estimation of lithium iron phosphate batteries

Accurate state of charge (SOC) estimation of lithium iron phosphate (LFP) batteries remains challenging because of their flat open-circuit-voltage (OCV)-SOC characteristics, temperature-dependent dynamics, and sensitivity to initialization errors. Here, we propose a physics-guided residual Kalman learning (PRKL) framework for electrochemical-model-based SOC estimation. PRKL combines a control-oriented single-particle-model-based extended Kalman filter (EKF), which provides recursive physical state propagation, with a gated recurrent unit (GRU) residual learner that compensates structured EKF errors using electrochemical states and measurement features. The framework is evaluated on a public graphite/LFP dataset covering three dynamic drive cycles, eight temperatures from -10 to 50 degrees C, and initialization offsets up to 20 percent. Using dynamic stress test (DST) and federal urban driving schedule (FUDS) cycles for training and the supplemental federal test procedure (US06) cycle for cross-profile testing within the same cell dataset, PRKL achieves a global average root mean square error (RMSE) of 1.19 percent, corresponding to a 77 percent reduction relative to the physics-only EKF. These results show that electrochemical state information can guide residual learning and improve recursive SOC estimation for LFP batteries. The present validation supports cross-profile robustness within the studied dataset and provides a basis for future cross-cell, ageing-aware, and embedded-platform validation.

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arXiv2025

Stability-Guaranteed Dual Kalman Filtering for Electrochemical Battery State Estimation

Accurate and stable state estimation is critical for battery management. Although dual Kalman filtering can jointly estimate states and parameters, the strong coupling between filters may cause divergence under large initialization errors or model mismatch. This paper proposes a Stability Guaranteed Dual Kalman Filtering (SG-DKF) method. A Lyapunov-based analysis yields a sufficient stability condition, leading to an adaptive dead-zone rule that suspends parameter updates when the innovation exceeds a stability bound. Applied to an electrochemical battery model, SG-DKF achieves accuracy comparable to a dual EKF and reduces state of charge RMSE by over 45% under large initial state errors.

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arXiv2025

Residual Bias Compensation Filter for Physics-Based SOC Estimation in Lithium Iron Phosphate Batteries

This paper addresses state of charge (SOC) estimation for lithium iron phosphate (LFP) batteries, where the relatively flat open-circuit voltage (OCV-SOC) characteristic reduces observability. A residual bias compensation dual extended Kalman filter (RBC-DEKF) is developed. Unlike conventional bias compensation methods that treat the bias as an augmented state within a single filter, the proposed dual-filter structure decouples residual bias estimation from electrochemical state estimation. One EKF estimates the system states of a control-oriented parameter-grouped single particle model with thermal effects, while the other EKF estimates a residual bias that continuously corrects the voltage observation equation, thereby refining the model-predicted voltage in real time. Unlike bias-augmented single-filter schemes that enlarge the covariance coupling, the decoupled bias estimator refines the voltage observation without perturbing electrochemical state dynamics. Validation is conducted on an LFP cell from a public dataset under three representative operating conditions: US06 at 0 degC, DST at 25 degC, and FUDS at 50 degC. Compared with a conventional EKF using the same model and identical state filter settings, the proposed method reduces the average SOC RMSE from 3.75% to 0.20% and the voltage RMSE between the filtered model voltage and the measured voltage from 32.8 mV to 0.8 mV. The improvement is most evident in the mid-SOC range where the OCV-SOC curve is flat, confirming that residual bias compensation significantly enhances accuracy for model-based SOC estimation of LFP batteries across a wide temperature range.

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arXiv2025

Adaptive Dead-Zone Dual Sliding Mode Observer for Reliable Electrochemical Model-Based SOC Estimation

Accurate state of charge (SOC) estimation is critical for ensuring the safety, reliability, and efficiency of lithium-ion batteries in electric vehicles and energy storage systems. Electrochemical models provide high fidelity for SOC estimation but introduce challenges due to parameter variations, nonlinearities, and computational complexity. To address these issues, this paper proposes an adaptive dead-zone dual sliding mode observer(SMO) based on an improved electrochemical single-particle model. The algorithm integrates a state observer for SOC estimation and a parameter observer for online parameter adaptation. A Lyapunov-derived adaptive dead-zone is introduced to ensure stability, activating parameter updates only when the terminal voltage error lies within a rigorously defined bound. The proposed method was validated under constant-current and UDDS dynamic conditions. Results demonstrate that the adaptive dead-zone dual SMO achieves superior accuracy compared with conventional dual SMO and equivalent circuit model-based EKF methods, maintaining SOC estimation errors within 0.2% under correct initialization and below 1% under a 30% initial SOC error, with rapid convergence. Computational efficiency analysis further shows that the adaptive dead-zone dual sliding mode observer reduces execution time compared with the conventional dual SMO by limiting unnecessary parameter updates, highlighting its suitability for real-time battery management applications. Moreover, robustness under battery aging was confirmed using a cycle-aging model, where the adaptive dead-zone dual SMO maintained stable SOC estimation despite parameter drift. These findings indicate that the proposed method offers a reliable, accurate, and computationally efficient solution for SOC estimation.

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