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Shared e-scooter systems are a key component of urban micromobility; however, battery pack selection still relies on nominal cell specifications or isolated performance metrics. This creates a design gap because existing energy, cost, and life-cycle-oriented assessments do not provide a pack level selection method that jointly accounts for rider heterogeneity, route severity, experimentally observed load profiles, and degradation-driven lifetime cost. To address this need, this study proposes a real-driving-data-informed degradation-aware optimization framework for selecting the cost-optimal battery pack by minimizing the levelized service cost per kilometer over battery lifetime. Real-world driving data, including vehicle speed, route position, battery voltage, and current, were collected using a GPS-enabled data acquisition system and incorporated into the proposed lithium-ion battery management and pack selection framework. Battery degradation was predicted using a literature-based aging model, and the agreement between the model-generated capacity trajectories and their piecewise OLS approximations was evaluated. The framework captures capacity fade, efficiency degradation, rider-dependent loading, slope-induced route severity, and economic factors, including electricity, maintenance, and aging costs. Battery selection is formulated as a unified cost-minimization problem across candidate lithium-ion chemistries and series-parallel pack configurations, enabling objective comparison under realistic constraints. Sensitivity analyses quantify the effects of rider mass, route slope, and cell-price variation on the optimal design. The results show that the optimal configuration depends strongly on both operating and market conditions, indicating that battery selection is context dependent rather than a fixed design choice. By linking capacity fade, current stress, efficiency degradation, and levelized service cost, the framework provides a transferable and degradation-aware lithium-ion battery management perspective for cost-effective battery deployment in shared micromobility applications.
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Toprak et al. (2026) studied this question.
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