arXiv:2605.27044v1 Announce Type: new Abstract: Early battery degradation trajectory forecasting (BDTF), which predicts the full-life state-of-health trajectory from early operational data, is critical for battery optimization, manufacturing, and deployment. Battery degradation data exhibit two key characteristics. First, degradation data present a multi-level structure, including regularities shared within aging conditions and trajectory patterns shared across batteries. Second, degradation-related variations in voltage-current profiles are often localized to specific state-of-charge (SOC) in
Accurate battery degradation forecasting is becoming increasingly critical with the rapid expansion of EV and grid storage markets, where battery longevity translates directly to economic viability and sustainability.
Improved battery degradation trajectory forecasting can significantly enhance the predictability, optimization, and lifetime management of energy storage systems, which are foundational to the energy transition and computation.
The ability to more accurately predict battery lifespan from early operational data fundamentally shifts how batteries are designed, deployed, and maintained, reducing costs and extending utility.
- · EV manufacturers
- · Grid storage operators
- · Battery recyclers
- · Battery analytics software companies
- · Battery manufacturers with poor quality control
- · Legacy battery testing methods
- · Companies reliant on conservative battery over-provisioning
More efficient and longer-lasting battery systems will accelerate the adoption of electric vehicles and renewable energy storage.
Reduced uncertainty in battery health could lead to new financing models for energy projects and second-life applications for batteries.
The enhanced lifespan of batteries may reduce demand for raw materials over time, impacting mining investment cycles.
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Read at arXiv cs.AI