As a promising sustainable mode of transportation, electric carsharing can relieve the urban traffic pressure and environmental pollution problems, which has been promoted all over the world to reduce or replace fossil-fueled private cars. However, the operation of electric carsharing is faced with difficulties in profitability and management. Vehicle relocation can alleviate the imbalance between supply and demand, and compared with operator-based relocation, user-based relocation is more sustainable and cost-efficient. In this paper, we develop a user-based relocation model with the optimization objectives of profit maximization and using failure rate minimization. Two station pricing schemes of pure preferential and preferential combined with fine are proposed as the constraints. For the input of the relocation model, the back propagation neural network of the quantity demand and the distribution fitting model of the energy demand are constructed to predict user demand. The demand prediction model and relocation model are validated through the operation data of EVCARD in Shanghai. The results demonstrate that the user-based relocation can effectively reduce the using failure rate and has the potential to increase the net profit. Compared with the situation without vehicle relocation, the user-based relocation strategy can reduce using failure rate by 13.6% and increase net profit by 76.9% in the best situation. Through sensitivity analysis of key parameters, it is found that the station pricing of pure preferential can reduce the using failure rate, but the profitability of the electric carsharing system is weakened. The introduction of fine can effectively slow down the erosion of user incentive on the system profit and increase the profit.

作为一种有希望的可持续交通方式，电动汽车共享可以缓解城市交通压力和环境污染问题，这在世界范围内已得到推广，以减少或替代以化石燃料为燃料的私家车。然而，电动汽车共享的运营在盈利能力和管理方面面临困难。车辆搬迁可以缓解供需之间的不平衡，与基于运营商的搬迁相比，基于用户的搬迁更加可持续且具有成本效益。在本文中，我们开发了一个基于用户的重定位模型，其优化目标是利润最大化和故障率最小化。提出了纯优惠，优惠与罚款相结合的两站定价方案作为约束。对于重定位模型的输入，构建了需求量的反向传播神经网络和能量需求量的分布拟合模型，以预测用户需求量。通过上海EVCARD的运行数据验证了需求预测模型和搬迁模型。结果表明，基于用户的重定位可以有效降低使用失败率，并有可能增加净利润。与没有车辆搬迁的情况相比，基于用户的搬迁策略在最佳情况下可以减少13.6％的使用失败率，并增加76.9％的净利润。通过对关键参数的敏感性分析，发现纯优惠的站点定价可以降低使用失败率，但电动汽车共享系统的盈利能力却被削弱。