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This paper introduces a data-driven framework for predicting the hysteresis factor in silicon-graphite anode-based batteries, which is crucial for accurate state-of-charge (SoC) estimation. The approach uses a data harmonization technique to standardize driving cycles and then employs statistical and deep learning models to predict the hysteresis factor along with uncertainty quantification. Experiments demonstrate the generalizability of the models across different vehicle models through retraining, zero-shot prediction, fine-tuning, and joint training.
Unlock more accurate state-of-charge estimation for EV batteries with silicon-graphite anodes using a computationally efficient, data-driven approach that predicts hysteresis factors with quantified uncertainty.
Batteries with silicon-graphite-based anodes, which offer higher energy density and improved charging performance, introduce pronounced voltage hysteresis, making state-of-charge (SoC) estimation particularly challenging. Existing approaches to modeling hysteresis rely on exhaustive high-fidelity tests or focus on conventional graphite-based lithium-ion batteries, without considering uncertainty quantification or computational constraints. This work introduces a data-driven approach for probabilistic hysteresis factor prediction, with a particular emphasis on applications involving silicon-graphite anode-based batteries. A data harmonization framework is proposed to standardize heterogeneous driving cycles across varying operating conditions. Statistical learning and deep learning models are applied to assess performance in predicting the hysteresis factor with uncertainties while considering computational efficiency. Extensive experiments are conducted to evaluate the generalizability of the optimal model configuration in unseen vehicle models through retraining, zero-shot prediction, fine-tuning, and joint training. By addressing key challenges in SoC estimation, this research facilitates the adoption of advanced battery technologies. A summary page is available at: https://runyao-yu.github.io/Porsche_Hysteresis_Factor_Prediction/
