CausalHealth detects lithium-ion battery degradation with 100% sensitivity and up to 402-cycle lead time using causal anomaly scores from voltage, current, temperature, and resistance time series across seven cells.
Degradation diagnostics for lithium ion cells
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BatteryMFormer is a multi-level Transformer that adds an aging-condition-aware decoder, meta degradation pattern memory, and dual-view encoder to forecast battery state-of-health trajectories from early operational data and outperforms baselines on four domains.
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Causal Anomaly Detection for Lithium-Ion Battery Degradation
CausalHealth detects lithium-ion battery degradation with 100% sensitivity and up to 402-cycle lead time using causal anomaly scores from voltage, current, temperature, and resistance time series across seven cells.
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BatteryMFormer: Multi-level Learning for Battery Degradation Trajectory Forecasting
BatteryMFormer is a multi-level Transformer that adds an aging-condition-aware decoder, meta degradation pattern memory, and dual-view encoder to forecast battery state-of-health trajectories from early operational data and outperforms baselines on four domains.