Learning When Not to Use a Battery: Multihorizon Failure Intelligence.
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| Title: | Learning When Not to Use a Battery: Multihorizon Failure Intelligence. |
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| Authors: | Shikdar, Tareq Anwar1 (AUTHOR), Laaksonen, Hannu1 (AUTHOR) hannu.laaksonen@uwasa.fi, Niu, Wangqiang1 (AUTHOR) wqniu@shmtu.edu.cn |
| Source: | International Transactions on Electrical Energy Systems. 5/8/2026, Vol. 2026, p1-14. 14p. |
| Subject Terms: | *Risk assessment, *Reliability in engineering, *Calibration, *Lithium-ion batteries, *Battery storage plants |
| Abstract: | Battery energy storage systems (BESSs) are increasingly dispatched to provide flexibility services in renewable‐dominated power systems. However, conventional battery analytics focuses on state‐of‐health (SOH) and remaining useful life (RUL), which describe long‐term degradation rather than short‐term operational safety. In practical grid operation, the relevant decision is whether a battery can reliably complete a specific service commitment within a predefined time horizon. This paper reformulates battery prognostics as an operational reliability estimation problem and proposes a decision‐oriented framework based on multihorizon discrete‐time hazard learning. The method predicts the probability of failure within a service window and applies probability calibration to obtain a trustworthy risk metric. The calibrated risk is integrated into a reliability‐aware dispatch policy that regulates participation in flexibility according to an acceptable failure tolerance. The framework is evaluated on 37 lithium‐ion batteries using leave‐battery‐out cross‐validation to ensure cross‐device generalization. The hazard model achieves strong discrimination performance, reaching an area under the ROC curve (AUC) of 0.944 before calibration for the 20‐cycle horizon. After probability calibration, the operational probabilities used for decision‐making achieve an AUC of 0.891 with a Brier score of 0.064, demonstrating reliable probabilistic risk estimation across service horizons. When incorporated into dispatch decisions, the reliability‐aware policy reduces the operational failure rate from 10.3% to 2.95% and increases delivered energy from 43.4 to 47.0 kWh compared with non‐reliability‐aware operation. An early‐life analysis further shows that operational risk becomes predictable only after degradation observability emerges, distinguishing reliability estimation from long‐term lifetime prediction. The results demonstrate that battery management for power systems should transition from predicting lifetime to controlling operational risk. The proposed framework provides an interpretable reliability signal enabling safe participation of storage assets in flexibility markets and bridges the gap between battery diagnostics and operational decision‐making. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Abstract: | Battery energy storage systems (BESSs) are increasingly dispatched to provide flexibility services in renewable‐dominated power systems. However, conventional battery analytics focuses on state‐of‐health (SOH) and remaining useful life (RUL), which describe long‐term degradation rather than short‐term operational safety. In practical grid operation, the relevant decision is whether a battery can reliably complete a specific service commitment within a predefined time horizon. This paper reformulates battery prognostics as an operational reliability estimation problem and proposes a decision‐oriented framework based on multihorizon discrete‐time hazard learning. The method predicts the probability of failure within a service window and applies probability calibration to obtain a trustworthy risk metric. The calibrated risk is integrated into a reliability‐aware dispatch policy that regulates participation in flexibility according to an acceptable failure tolerance. The framework is evaluated on 37 lithium‐ion batteries using leave‐battery‐out cross‐validation to ensure cross‐device generalization. The hazard model achieves strong discrimination performance, reaching an area under the ROC curve (AUC) of 0.944 before calibration for the 20‐cycle horizon. After probability calibration, the operational probabilities used for decision‐making achieve an AUC of 0.891 with a Brier score of 0.064, demonstrating reliable probabilistic risk estimation across service horizons. When incorporated into dispatch decisions, the reliability‐aware policy reduces the operational failure rate from 10.3% to 2.95% and increases delivered energy from 43.4 to 47.0 kWh compared with non‐reliability‐aware operation. An early‐life analysis further shows that operational risk becomes predictable only after degradation observability emerges, distinguishing reliability estimation from long‐term lifetime prediction. The results demonstrate that battery management for power systems should transition from predicting lifetime to controlling operational risk. The proposed framework provides an interpretable reliability signal enabling safe participation of storage assets in flexibility markets and bridges the gap between battery diagnostics and operational decision‐making. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20507038 |
| DOI: | 10.1155/etep/6000810 |
