<ul data-eligibleForWebStory="true"><li>Accurate electrochemical models are crucial for safe and efficient lithium-ion battery operation in applications like electric vehicles and grid storage.</li><li>A study introduces an Adaptive Ensemble Sparse Identification (AESI) framework to enhance reduced-order li-ion battery models by addressing unpredictable dynamics.</li><li>The AESI framework combines an Extended Single Particle Model (ESPM) with an evolutionary ensemble sparse learning strategy and conformal prediction for uncertainty quantification.</li><li>Evaluation highlights improved voltage prediction accuracy (up to 46% error reduction on unseen data) and reliable prediction intervals with high coverage ratios for ensemble models.</li></ul>

Augmented Physics-Based Li-ion Battery Model via Adaptive Ensemble Sparse Learning and Conformal Prediction

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