[2] Z. Wang, S. Chen, and L. Zhang, “Dynamic pricing for electric vehicle charging stations: A game-theoretic approach,” Applied Energy , vol. 280, 115987, 2020.
[3] G. R. Newsham and B. J. Birt, “Building-level occupancy data to improve EV charging schedules,” Energy and Buildings , vol. 186, pp. 244–254, 2019.
Electric vehicle charging, dynamic pricing, load balancing, shared mobility, GShare. 1. Introduction Shared electric vehicle (EV) services—such as car-sharing, e-scooters, and ride-hailing fleets—face a fundamental operational tension: vehicles must remain charged, but charging stations are often overloaded during peak hours and underutilized overnight. Existing first-come, first-served (FCFS) or flat-rate pricing models exacerbate this imbalance, leading to queuing delays, higher operational costs, and unnecessary grid stress.
[ p(t) = p_base \times \left(1 + \alpha \cdot L(t) + \beta \cdot O(t) - \gamma \cdot R(t)\right) ]
Author: [Your Name/Institution] Date: April 17, 2026 Abstract The rapid proliferation of shared electric mobility services has introduced significant challenges in charging infrastructure utilization, grid load management, and fair user pricing. This paper presents GShare , a decentralized charging management system designed for shared EV fleets and public charging networks. GShare integrates real-time grid demand data, station occupancy, and user priority tiers into a dynamic pricing algorithm. The system reduces peak grid strain by 23% in simulated urban environments while improving station throughput by 18% compared to flat-rate models. We describe the system architecture, pricing mechanism, user interface, and performance evaluation.
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[2] Z. Wang, S. Chen, and L. Zhang, “Dynamic pricing for electric vehicle charging stations: A game-theoretic approach,” Applied Energy , vol. 280, 115987, 2020.
[3] G. R. Newsham and B. J. Birt, “Building-level occupancy data to improve EV charging schedules,” Energy and Buildings , vol. 186, pp. 244–254, 2019. gshare charging system
Electric vehicle charging, dynamic pricing, load balancing, shared mobility, GShare. 1. Introduction Shared electric vehicle (EV) services—such as car-sharing, e-scooters, and ride-hailing fleets—face a fundamental operational tension: vehicles must remain charged, but charging stations are often overloaded during peak hours and underutilized overnight. Existing first-come, first-served (FCFS) or flat-rate pricing models exacerbate this imbalance, leading to queuing delays, higher operational costs, and unnecessary grid stress. GShare integrates real-time grid demand data
[ p(t) = p_base \times \left(1 + \alpha \cdot L(t) + \beta \cdot O(t) - \gamma \cdot R(t)\right) ] and performance evaluation.
Author: [Your Name/Institution] Date: April 17, 2026 Abstract The rapid proliferation of shared electric mobility services has introduced significant challenges in charging infrastructure utilization, grid load management, and fair user pricing. This paper presents GShare , a decentralized charging management system designed for shared EV fleets and public charging networks. GShare integrates real-time grid demand data, station occupancy, and user priority tiers into a dynamic pricing algorithm. The system reduces peak grid strain by 23% in simulated urban environments while improving station throughput by 18% compared to flat-rate models. We describe the system architecture, pricing mechanism, user interface, and performance evaluation.