Physics-informed mixture of experts network for interpretable battery degradation trajectory computation amid second-life complexities

• URL: http://arxiv.org/abs/2506.17755v1
• Date: Sat, 21 Jun 2025 16:26:59 GMT
• Title: Physics-informed mixture of experts network for interpretable battery degradation trajectory computation amid second-life complexities
• Abstract summary: PIMOE computes battery degradation trajectories using partial, field-accessible signals in a single cycle.<n>It is validated on 207 batteries across 77 use conditions and 67,902 cycles.
• Score: 15.698670286581597
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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