隨著全球推動電動交通與淨零碳排策略，電動巴士之導入已成為綠色城市運輸之重要推動方向。當車載電池汰役時尚具一定之可用剩餘容量，其潛力可進一步經重組利用成二次生命儲能電池系統，以用於參與場站支援或電力輔助服務市場，進而提升資源使用效率與整體效益。然而，如何妥善規劃電池汰役時機與汰役後之再利用策略，現今鮮有相關研究涉及討論，亦缺乏系統性之規劃策略。
本研究從電動巴士營運商角度出發，建立一套整合電動巴士最佳化排程、電池壽命規劃分配與再利用分析之規劃架構，進而求出最適化之電動巴士車載電池汰換時機。因車載電池汰換時機與電動車使用模式及充電模式有關，故本文模型採用雙層迴圈求解流程，內層透過混合整數線性規劃(Mixed-Integer Linear Programming, MILP)求解巴士充電排程；外層則運用差分進化演算法(Differential Evolution, DE)尋找最佳電池汰役門檻值，演算法中整合考量了電池重組後之可用容量估算模型，使演算法可兼顧重組電池於後續應用之潛力，提升汰換決策之實用性。本研究共設計五種應用策略，包含不進行重組、支援場站用電及三種輔助服務參與模式，並根據最佳之應用情境再進行不同之靈敏度分析。
模擬結果顯示，電池於最適化健康度門檻值汰役後，重組為二次生命儲能電池系統並投入具經濟誘因之輔助服務市場，可有效提升電池整體使用效益及增加營運商之收益。此外，應用模式不同亦將導致顯著之營運效益差異。分析結果顯示，電池汰役時機對重組後之容量、殘值及替換頻率具高度相關，顯示彈性的汰役策略結合應用情境評估，將成為未來電動巴士或電動車充電站業者營運規劃之重要策略。

With the global push toward electric mobility and net-zero carbon strategies, the deployment of electric buses has become a vital element in promoting sustainable urban transportation. When onboard batteries reach the end of their primary service life but still retain a usable level of residual capacity, they hold potential for repurposing into second-life energy storage systems. These systems can be further utilized in applications such as depot energy support or participation in ancillary service markets, thereby enhancing resource utilization efficiency and overall system value. However, the planning of optimal battery retirement timing and subsequent reuse strategies remains underexplored, with limited existing research offering systematic planning frameworks.

This study proposes an integrated planning framework from the perspective of electric bus fleet operators, combining day-ahead scheduling, battery retirement optimization, and second-life utilization analysis. The framework adopts a two-level iterative solution structure. The inner layer applies Mixed-Integer Linear Programming(MILP) to optimize the daily charging schedules based on operational constraints, while the outer layer utilizes a Differential Evolution(DE) algorithm to determine the optimal battery State of Health(SoH) retirement threshold. The algorithm incorporates a second-life battery capacity estimation model, allowing it to simultaneously account for the repurposed battery’s application potential, thereby enhancing the practicality of the retirement strategy. Five application strategies are considered in the analysis, including no repurposing, depot support, and three different ancillary service participation scenarios. Sensitivity analyses are subsequently performed based on the optimal application case.

Simulation results indicate that retiring batteries at an optimized SoH thresholds, repurposing them into second-life energy storage systems, and deploying them in ancillary service markets with economic incentives can significantly enhance overall battery utilization and increase operator revenues. Moreover, different application models yield varying operational benefits. The analysis reveals that battery retirement timing is strongly correlated with repurposed capacity residual value, and replacement frequency. These findings underscore that a flexible retirement strategy, integrated with scenario-specific application evaluation, will be a crucial consideration in future planning for electric bus operators or electric vehicle charging station providers.

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