コストと排出を考慮した戦略的最適化：シナリオ指向設計を支援するフリートレベルツール

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a replicable two-level-to-fleet optimization framework linking powertrain design with operational scheduling under carbon constraints.

🏢実務担当者:Logistics and fleet managers can use this methodology to size EV fleets and charging infrastructure while balancing cost and CO2 targets.

🏛政策担当者:Illustrates how carbon pricing signals (equivalent emission cost) can steer fleet configuration choices, useful for transport decarbonization incentives.

The transition toward low-emission transport systems requires not only technologically optimized Battery Electric Vehicles (BEVs) but also integrated methodologies capable of supporting industrial stakeholders throughout the deployment phase. In particular, for logistics operators, fleet sizing and charging infrastructure planning are tightly coupled with vehicle configuration and mission scheduling. Therefore, decision-support tools are required to minimize total operational costs and environmental impact while ensuring service continuity. Building upon a previously developed two-level BEV design framework, this work introduces a higher-level optimization tool aimed at extending powertrain design outcomes toward fleet-level decision-making, providing an integrated methodology capable of determining not only the optimal vehicle configuration but also the optimal number of vehicles and charging stations required to satisfy operational scheduling constraints. The proposed tool performs fleet charging management optimization under customizable objective functions. Two BEV configurations, equipped respectively with 7 and 10 battery packs, are selected as candidate solutions from the upstream two-level design framework. Starting from these configurations, the tool simultaneously optimizes fleet size, charging infrastructure dimensioning, and charging scheduling strategy. In the first case study, the objective is the minimization of fleet operational costs, primarily associated with charging energy, while introducing a tunable penalty factor on mission time-shifting for schedule flexibility. In the second case study, a CO2-based term is incorporated into the objective function through an equivalent emission cost. By varying its weighting factor, the analysis quantifies how environmental prioritization influences the optimal fleet and infrastructure configuration. Across all the examined scenarios, the optimal fleet size consistently converges to 3 vehicles with a single 50 kW DC charging station. The key difference between cost-driven and environmentally-oriented optimization lies in the battery configuration: in the cost-driven scenario, the 10-packs configuration achieves the lowest Total Cost of Ownership (1134 EUR/week), as its larger energy buffer reduces weekly grid energy demand and thus charging costs. Conversely, under CO₂-prioritized optimization, the optimal configuration shifts to the 7-packs, yielding a lower TCO of 1008 EUR/week and a 15% reduction in CO₂ emissions (127 vs 149 kgCO2/week). The proposed fleet-level optimization framework represents a scalable extension of the vehicle design methodology, enabling logistics companies to support electrification strategies through data-driven, application-specific, and sustainability-oriented decision-making.

• semanticscholar https://doi.org/10.4271/2026-24-0034first seen 2026-09-27 05:22:22 · last seen 2026-09-29 05:38:36