Vehicle Relocation Triggering Thresholds Determination in Electric Carsharing System under Stochastic Demand

Abstract

Vehicle unbalance usually occurs in multistation electric carsharing systems. Threshold triggering method is one of the most practicable approaches for vehicle relocation, while determination of thresholds has not been sufficiently studied particularly for electric carsharing system. This paper presents an approach on determining the thresholds considering the stochastic demands and system states. Firstly, we establish a state transition model involving the stochastic variables to capture the dynamics of the number and battery status of vehicles as well as the traffic demands. Consequently, a dual-objective optimization model was developed to determine the proper values of thresholds. The solution algorithm employed the min-max robust optimization to tackle the uncertainty and the Pareto optimum to decide the solution under dual objectives. To test the distribution stochastic variables, we involve the orders data and the supplementary user survey. Comparison is conducted among three methods: the empirical rules, the deterministic method, and the stochastic method, where the results suggest that the stochastic method achieves better solutions on the dual objectives under stochastic demands.

Vehicle Assignment considering Battery Endurance for Electric Vehicle Carsharing Systems

“…To improve the performance, researchers also attached optimization to the simulation framework [39,40]. Inventory control approaches [41][42][43] are also developed to cope with the dynamic fleet management tactics for instant access systems.…”

“…Wang et al [42] formulated SOC constraints and integrated them in the overall optimization model, while the actual vehicle selection judged by users on SOCs would highly differ from the system optimization expectation. Ma et al [43] involved a matching model between required distance and SOC under the objective of maximizing battery utilization, which is also not realistic enough to reveal vehicle selection by users in instant access systems or vehicle assignment in short-term reservation systems. In our paper, we attempt to explore the matching problem between user's desired travel distance and the vehicle's actual SOC, and to provide suggestions for vehicle assignment.…”

Dynamic prediction-based relocation policies in one-way station-based carsharing systems with complete journey reservations

“…The performance of the model is tested by dividing the day to several consecutive periods and generating a separate plan for each period. Ma et al (2018) present a methodology to derive station-specific relocation triggering threshold values in which demand stochasticity is explicitly considered. These thresholds constitute a simple and straightforward way for system operators to detect in real-time overloaded/depleted stations from/to which performing a relocation would be beneficial.…”