Major transportation network companies (TNCs) have promised to shift to 100% electric vehicles (EVs) in the next two decades, which places an increasing need to investigate the issues of ride-hailing services provided by EVs. Existing studies that model the EV charging systems and the TNC service systems omit the influences of the charging costs (i.e., electricity rate, and value of waiting time) on driver supply and passenger demand, which results in inaccurate prediction of system dynamics. This study is the first attempt to understand the influence of the electricity rate on the demand/supply of ride-hailing services and its implications. We compute the charging cost as the sum of the electricity cost based on the charging volume and the values of the expected waiting time. Specifically, we construct a queueing model framework to calculate the expected waiting time with M/M/k/C and synchronized M/M/1 queues, which models the charging and ride-hailing service processes separately. The experiment results from a two-symmetric-unit network show that the system performance metrics, such as platform profit and ratios of passengers served, have decreasing trends with increasing electricity rates. These trends shift when electricity rates and wage/trip fare rates change simultaneously, indicating the TNC platforms are able to achieve high profits by adjusting wage/fare rates to handle changes in electricity rates. Similar performance trends are validated by increasing electricity rates on large-scale experiments based on real-world trip demand. We further undertake sensitivity analysis and conclude that as the passenger demand increases, the system’s performance metrics, such as platform profit and the percentage of served passengers, gradually converge, within the constraints of the number of EVs and their battery capacity; the usage frequencies of charging stations follow the Pareto Principle, where roughly 15% of stations could serve most of the charging demand.

中文翻译：

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充电成本对电动网约车影响的建模

主要交通网络公司 (TNC) 承诺在未来二十年转向 100% 电动汽车 (EV)，这使得越来越需要调查电动汽车提供的网约车服务问题。现有对电动汽车充电系统和跨国公司服务系统建模的研究忽略了充电成本（即电价和等待时间价值）对驾驶员供给和乘客需求的影响，导致系统动态预测不准确。这项研究是首次尝试了解电价对网约车服务需求/供应的影响及其影响。我们将充电成本计算为基于充电量和预期等待时间值的电费之和。具体来说，我们构建了一个排队模型框架来计算 M/M/k/C 和同步 M/M/1 队列的预期等待时间，该框架分别对充电和叫车服务流程进行建模。两对称单元网络的实验结果表明，平台利润、服务乘客比例等系统性能指标随着电费的增加呈下降趋势。当电价和工资/出行票价同时变化时，这些趋势就会发生变化，这表明跨国公司平台能够通过调整工资/票价来应对电价的变化，从而获得高额利润。根据现实世界的出行需求，在大规模实验中提高电费，验证了类似的性能趋势。我们进一步进行了敏感性分析，得出的结论是，随着乘客需求的增加，在电动汽车数量及其电池容量的限制下，系统的性能指标（例如平台利润和服务乘客百分比）逐渐收敛；充电站的使用频率遵循帕累托原则，大约15%的充电站可以满足大部分充电需求。