Physics-informed mixture of experts network for interpretable battery degradation trajectory computation amid second-life complexities
This work addresses the challenge of safe and scalable deployment of second-life batteries for low-carbon energy systems, offering a deployable, history-free solution for degradation prediction.
The paper tackles the problem of predicting battery degradation trajectories for retired electric vehicle batteries under second-life use, where uncertainties and data inaccessibilities exist, by proposing a Physics-Informed Mixture of Experts (PIMOE) network that achieves an average mean absolute percentage error (MAPE) of 0.88% with a 0.43 ms inference time, reducing computational time and MAPE by 50% compared to state-of-the-art methods.
Retired electric vehicle batteries offer immense potential to support low-carbon energy systems, but uncertainties in their degradation behavior and data inaccessibilities under second-life use pose major barriers to safe and scalable deployment. This work proposes a Physics-Informed Mixture of Experts (PIMOE) network that computes battery degradation trajectories using partial, field-accessible signals in a single cycle. PIMOE leverages an adaptive multi-degradation prediction module to classify degradation modes using expert weight synthesis underpinned by capacity-voltage and relaxation data, producing latent degradation trend embeddings. These are input to a use-dependent recurrent network for long-term trajectory prediction. Validated on 207 batteries across 77 use conditions and 67,902 cycles, PIMOE achieves an average mean absolute percentage (MAPE) errors of 0.88% with a 0.43 ms inference time. Compared to the state-of-the-art Informer and PatchTST, it reduces computational time and MAPE by 50%, respectively. Compatible with random state of charge region sampling, PIMOE supports 150-cycle forecasts with 1.50% average and 6.26% maximum MAPE, and operates effectively even with pruned 5MB training data. Broadly, PIMOE framework offers a deployable, history-free solution for battery degradation trajectory computation, redefining how second-life energy storage systems are assessed, optimized, and integrated into the sustainable energy landscape.