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Network-level energy consumption estimation for electric vehicles considering vehicle and user heterogeneity

机译：考虑车辆和用户异质性的电动汽车网络级能耗估算

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摘要

As a green transport means, electric vehicle (EV) has received widespread attention in recent years and an increasing number of cities have been establishing their EV transport systems including EV fleet management and charging infrastructure. Transport policy-makers are concerning about the system performance of an EV charging system measured by energy consumption and user experience. We in this paper aim to develop an effective method for . the expected total energy consumption estimation (ETEC) of EV charging systems deployed in a dense city. To achieve this objective, we firstly introduce the nonlinear charging profile, its impacts on energy consumption as well as user experience, and present an approach for estimating the nonlinear charging time. We then elaborate our method to estimate energy consumption for one-time EV charging by addressing four kinds of energy losses in charging process. Next, charging frequency by considering multi-type EVs, and their heterogeneities and different charging needs for normal and fast charging systems is analyzed in depth. These two together determine the network-wide energy consumption and a pro-rated approach is used to figure out the spatial distribution of energy consumption. A case study of Singapore is conducted in the end to validate the proposed methodology. Numerical results reveal a trade-off between energy saving and user experience, and also demonstrate the importance of considering heterogeneities of driving range, charging preference and daily travel mileage.
机译：电动汽车（EV）作为绿色交通手段，近年来受到广泛关注，越来越多的城市正在建立其EV交通系统，包括EV车队管理和充电基础设施。运输政策制定者关注的是，通过能量消耗和用户体验来衡量的EV充电系统的系统性能。我们本文的目的是开发一种有效的方法。部署在人口稠密城市中的电动汽车充电系统的预期总能耗估算（ETEC）。为了实现这一目标，我们首先介绍了非线性充电曲线，其对能耗和用户体验的影响，并提出了一种估计非线性充电时间的方法。然后，我们通过解决充电过程中的四种能量损失，详细阐述了估算一次电动汽车充电的能耗的方法。接下来，深入分析考虑多种类型电动汽车的充电频率，其异质性以及普通和快速充电系统的不同充电需求。这两个因素共同决定了网络范围内的能耗，并且采用了按比例分配的方法来计算能耗的空间分布。最后对新加坡进行了案例研究，以验证所提出的方法。数值结果显示了在节能和用户体验之间的权衡，并且还表明了考虑行驶里程，充电偏好和每日行驶里程的异构性的重要性。

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