Extending flight endurance is critical for unmanned aerial vehicles (UAVs) deployed in post-disaster surveillance missions. This study proposes a hybrid battery–laser power system in which a ground-based laser source continuously tracks and charges a photovoltaic (PV) array mounted on a UAV, and develops an integrated optical–thermal–electrical model coupled with a battery-sizing optimization framework. Using a realistic trajectory derived from experimentally measured UAV flight data, the results show that the proposed hybrid system reduces the required battery capacity by up to 23.8% compared with a laser-off configuration. Alternatively, for the same battery capacity, laser-assisted operation extends the achievable mission duration by approximately 23%, enabling longer disaster-relief missions without increasing the UAV payload. These findings provide practical design guidance for developing laser-powered UAV systems for extended disaster-relief operations.

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.

The state-of-charge (SoC) window used during cycling influences battery behaviour and is relevant to the assessment of cells considered for second-life use. This study investigates whether the SoC window in which a cell was cycled can be identified from the electro-thermal response of a single reference cycle that is identical for every cell, without cycling data recorded within that window. Twenty commercial 102 Ah prismatic lithium iron phosphate (LFP) cells were cycled in five fixed SoC windows with periodic reference cycles. Sixty-five features describing capacity, efficiencies, rest voltages, the voltage curve and surface temperatures were derived from each reference cycle. Under leave-one-cell-out cross-validation, a regularised logistic-regression classifier achieved a per-cycle balanced accuracy of 0.75, and aggregating each cell’s reference cycles correctly classified 19 of 20 cells. With feature-group, model and hyper-parameter selection nested within each fold, the per-cycle balanced accuracy was 0.61 and 16 cells were correctly classified. Misclassifications were concentrated between closely related windows, with a quadratically weighted $\kappa$ of 0.88 and a depth-of-discharge mean absolute error of 2.0 percentage points. Rest voltages after the full discharge provided the strongest discrimination. The results establish single-cycle window identification for the tested cells and conditions, providing a basis for second-life assessment.