電力系統応用におけるEV二次利用電池の適応的管理における反復的不確実性低減
Iterative Uncertainty Reduction in Adaptive Management of Second-Life EV Batteries for Power System Applications (原題)
Kostenko G, Denysov V, Zaporozhets A
🤖 gxceed AI 要約
日本語
本研究は、二次利用EV電池（SLB）の電力系統応用における不確実性を反復的に低減する枠組みを提案する。データ、状態、モデル、予測、シナリオ、決定、外部の不確実性を区別し、低減可能なものと残存するものを分離する。動的不確実性エンベロープを中心に、診断、劣化指標、RUL予測、シナリオ評価、KPI評価、最適化、監視が連携して不確実性を管理する。3シナリオの適用例で最終正規化エンベロープは0.33〜0.44となった。
English
This study proposes a framework for iterative uncertainty reduction in adaptive management of second-life EV batteries (SLBs) for power system applications. It distinguishes data, state, model, prognostic, scenario, decision, and external uncertainties, separating reducible from residual ones. A dynamic uncertainty envelope is central, with diagnostics, degradation indicators, RUL forecasting, scenario qualification, KPI assessment, optimization, and monitoring successively constraining and updating it. In an illustrative three-scenario application, final normalized envelope extents ranged from 0.33 to 0.44.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本ではEV普及に伴い使用済み電池の再利用が課題であり、系統安定化や再生可能エネルギー平滑化にSLBを活用する本枠組みは、エネルギー転換と資源循環の両面で有用。SSBJ開示やカーボンニュートラル戦略と関連し、蓄電池のライフサイクル評価や系統統合の実務に示唆を与える。
In the global GX context
Globally, second-life EV batteries are key to cost-effective energy storage and circular economy in the energy transition. This framework addresses uncertainty management in SLB deployment, which is critical for grid integration and renewable smoothing. It contributes to the growing literature on battery lifecycle management and supports climate disclosure by providing robust methods for assessing battery performance and residual value.
👥 読者別の含意
🔬研究者:Provides a structured taxonomy of uncertainties and a dynamic envelope method for SLB management, useful for advancing battery degradation and system integration research.
🏢実務担当者:Offers a decision framework for utilities and battery operators to manage SLB uncertainty, improving operational reliability and economic viability.
🏛政策担当者:Highlights the importance of uncertainty management in SLB deployment, informing policies that promote second-life battery use and grid resilience.
📄 Abstract（原文）
Second-life electric vehicle batteries (SLBs) enter stationary power-system applications with heterogeneous ageing histories, incomplete first-life information, and uncertain operating conditions. This study develops a framework for iterative uncertainty reduc-tion in adaptive SLB management. It distinguishes data, state, model, prognostic, sce-nario, decision, and external uncertainty, while separating reducible uncertainty from residual uncertainty that must be accommodated. Its central construct is a dynamic uncertainty envelope representing plausible states, trajectories, and outcomes at each decision stage. Diagnostics, the integrated degradation indicator (IDI), remaining useful life (RUL) forecasting, scenario qualification, KPI assessment, optimization, and moni-toring successively constrain, transform, propagate, and update this envelope. Uncer-tainty reduction is treated as generally decreasing but non-monotonic because new scenarios, abnormal degradation, or external disturbances may temporarily expand plausible outcomes. Adaptive decisions combine the estimated state with its uncertainty, while KPI and confidence thresholds trigger diagnostics, operating-intensity reduction, reserve adjustment, re-optimization, reassignment, replacement, or retirement. In an illustrative three-scenario application, the final normalized envelope extents were 0.33 for backup supply, 0.36 for renewable-energy smoothing, and 0.44 for load regulation. The framework thereby shifts the focus from isolated uncertainty quantification to managing uncertainty evolution throughout the SLB second-life cycle.
🔗 Provenance — このレコードを発見したソース
- Research Square https://doi.org/10.20944/preprints202608.2086.v1first seen 2026-09-02 04:34:07 · last seen 2026-09-11 04:42:19
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