Leveraging Operational Data from Urban Transportation EVs for Efficiency Improvement, Environmental Sustainability, and AI-Based Operational Optimization
DOI:
https://doi.org/10.58445/rars.3580Keywords:
Electric Vehicles (EVs), Telematics, Fleet Management, Reinforcement Learning (RL), Route Optimization, Charging Management, Last-Mile Logistics, Sustainability, Data-Driven Optimization, Battery State-of-Health (SOH)Abstract
In the field of sustainable transportation, urban electric last-mile logistics fleets represent a critical frontier. However, their operational efficiency remains constrained by limited data utilization as well as range limitations and charging infrastructure. This research presents a comprehensive analysis of how operational data from telematics systems can, and has been, utilized to improve fleet performance in the aspects of efficiency, environmental sustainability, and artificial intelligence-driven operational optimization.
By analyzing real-world case studies from fleet operators such as SIRO, Geotab deployments, and municipal EV programs, this research also underscores the significant benefits of using operational data such as extended driving range and lower energy consumption, while quantitative evidence from the case studies supports this.
The research particularly emphasizes the opportunity to harness the power of reinforcement learning (RL) applications for intelligent charging management and route optimization. Using Markov Decision Process models, RL agents can adapt charging and routing decisions to operational conditions, achieving near-optimal performance over time. Through the integration of telematics with artificial intelligence, operators can reduce operational costs, improve sustainability, and unlock the potential for autonomous fleet management. This paper also provides policy and technical recommendations, highlighting the significance of federated learning, edge computing, and smart-city integration for future research endeavors.
Using systematic evaluation, this paper argues that, in the current competitive landscape, data-driven optimization is not merely beneficial but essential for the future viability of urban electric transportation. The findings also provide insights for EV logistics companies seeking to leverage operational data for a competitive advantage while simultaneously contributing to wider sustainability objectives.
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