Modelling the weekly electricity demand caused by electric cars

The travel demand model mobiTopp is used to analyse the effects of an increasing fleet of electric vehicles on the electricity demand. For this purpose mobiTopp has been extended to support EVs. These extensions comprise an EV ownership model, different types of EVs, namely Battery Electric Vehicles (BEVs) and Extended Range Electric Vehicles (EREVs), the charging process of EVs, and modified travel behaviour for persons using BEVs. The model is used to simulate the electricity demand for charging of EVs in the Greater Stuttgart Area over a period of one week. The daily electricity demand for EV charging contains a distinct peak around 6 p.m. for each workday and an additional morning peak around 8 a.m. when recharging at the workplace is possible. This additional electricity demand is concentrated in zones with residential use during the evening peak and in zones with office/industrial use during the morning peak.     

EVLibSim: A tool for the simulation of electric vehicles’ charging stations using the EVLib library

Electric Vehicles (EVs) are considered an efficient alternative to internal combustion engined ones, aiming to reduce global CO₂ emissions. In the last years, EVs are entering the market in an increasing pace. In contrast to conventional cars, EVs have a more complicated recharging procedure. Thus, the development of tools for the efficient simulation of the charging of large numbers of EVs is critical. In this vein, EVLibSim is a tool for the simulation of EV activities at a charging station level. EVLibSim unifies EVLib’s primary functions such as the charging and dis-charging of batteries, battery swapping, as well as parking/inductive charging. EVLib is a Java library that provides a simple, yet efficient framework for the management of a number of Electric Vehicle (EV) activities, at a charging station level, within a Smart Grid. EVLibSim provides a great variety of configuration options such as the types and number of chargers, the available energy, the waiting queues, etc. Furthermore, through plots and overview dashboards each user can supervise the operation of the tool in real time. Both EVLib’s and EVLibSim’s efficiency and scalability have been tested in realistic scenarios, while the correctness and safety of the code have been verified using state of the art tools. Finally, the user experience of the EVLibSim has been evaluated and improved through a detailed user evaluation.     

The Electric Vehicle Shortest-Walk Problem With Battery Exchanges

Electric vehicles (EV) have received much attention in the last few years. Still, they have neither been widely accepted by commuters nor by organizations with service fleets. It is predominately the lack of recharging infrastructure that is inhibiting a wide-scale adoption of EVs. The problem of using EVs is especially apparent in long trips, or inter-city trips. Range anxiety, when the driver is concerned that the vehicle will run out of charge before reaching the destination, is a major hindrance for the market penetration of EVs. To develop a recharging infrastructure it is important to route vehicles from origins to destinations with minimum detouring when battery recharging/exchange facilities are few and far between. This paper defines the EV shortest-walk problem to determine the route from a starting point to a destination with minimum detouring; this route may include cycles for detouring to recharge batteries. Two problem scenarios are studied: one is the problem of traveling from an origin to a destination to minimize the travel distance when any number of battery recharge/exchange stops may be made. The other is to travel from origin to destination when a maximum number of stops is specified. It is shown that both of these problems are polynomially solvable and solution algorithms are provided. This paper also presents another new problem of finding the route that minimizes the maximum anxiety induced by the route.     

Decentralised online charging scheduling for large populations of electric vehicles: a cyber-physical system approach

As the number of electric vehicles (EVs) grows, their electricity demands may have significant detrimental impacts on electric power grid when not scheduled properly. In this paper, we model an EV charging system as a cyber-physical system, and design a decentralised online EV charging scheduling algorithm for large populations of EVs, where the EVs can be highly heterogeneous and may join the charging system dynamically. The algorithm couples a clustering-based strategy that dynamically classifies heterogeneous EVs into multiple groups and a sliding-window iterative approach that schedules the charging demand for the EVs in each group in real time. Extensive simulation results demonstrate that our approach provides near-optimal solutions at significantly reduced complexity and communication overhead. It flattens the aggregated load on the power grid and reduces the costs of both the users and the utility.     

Algorithms and experiments on routing of unmanned aerial vehicles with mobile recharging stations

We study the problem of planning a tour for an energy‐limited Unmanned Aerial Vehicle (UAV) to visit a set of sites in the least amount of time. We envision scenarios where the UAV can be recharged at a site or along an edge either by landing on stationary recharging stations or on Unmanned Ground Vehicles (UGVs) acting as mobile recharging stations. This leads to a new variant of the Traveling Salesperson Problem (TSP) with mobile recharging stations. We present an algorithm that finds not only the order in which to visit the sites but also when and where to land on the charging stations to recharge. Our algorithm plans tours for the UGVs as well as determines the best locations to place stationary charging stations. We study three variants for charging: Multiple stationary charging stations, single mobile charging station, and multiple mobile charging stations. As the problems we study are nondeterministic polynomial time (NP)‐Hard, we present a practical solution using Generalized TSP that finds the optimal solution that minimizes the total time, subject to the discretization of battery levels. If the UGVs are slower than the UAVs, then the algorithm also finds the minimum number of UGVs required to support the UAV mission such that the UAV is not required to wait for the UGV. Our simulation results show that the running time is acceptable for reasonably sized instances in practice. We evaluate the performance of our algorithm through simulations and proof‐of‐concept field experiments with a fully autonomous system of one UAV and UGV.     

Optimal control of an electric vehicle’s charging schedule under electricity markets

As increasing numbers of electric vehicles (EVs) enter into the society, the charging behavior of EVs has got lots of attention due to its economical difference within the electricity market. The charging cost for EVs generally differ from each other in choosing the charging time interval (hourly), since the hourly electricity prices are different in the market. In this paper, the problem is formulated into an optimal control one and solved by dynamic programming. Optimization aims to find the economically optimal charging solution for each vehicle. In this paper, a nonlinear battery model is characterized and presented, and a given future electricity prices is assumed and utilized. Simulation results indicate that daily charing cost is reduced by smart charing.     

A review on charging behavior of electric vehicles: data, model, and control

The adoption and usage of electric vehicles (EVs) have emerged recently due to the increasing concerns on the greenhouse gas issues and energy revolution. As a part of the smart grid, EVs can provide valuable ancillary services beyond consumers of electricity. However, EVs are gradually considered as nonnegligible loads due to their increasing penetration, which may result in negative effects such as voltage deviations, lines saturation, and power losses. Relationship and interaction among EVs, charging stations, and micro grid have to be considered in the next generation of smart grid. Therefore, the topic of smart charging has been the focus of many works where a wide range of control methods have been developed. As one of the bases of simulation, the EV charging behavior and characteristics have also become the focus of many studies. In this work, we review the charging behavior of EVs from the aspects of data, model, and control. We provide the links for most of the data sets reviewed in this work, based on which interested researchers can easily access these data for further investigation.     

基于DEB-ABC算法的电动汽车充电优化调度模型

随着电动汽车保有量不断上升, 其相关配套设施也面临巨大挑战, 不合理的充电资源分配在充电高峰期会造成部分充电站过度拥挤, 并且影响电网稳定运行. 提出一种考虑多目标优化的调度模型, 通过分析充电站内不同充电选项的排队时间, 并根据排队率和分时电价提出一种动态定价模型, 影响车主充电行为, 结合动态定价模型与充电需求计算充电成本, 考虑基于起讫点的充电总路径行驶时间, 以总成本最少为优化目标, 基于DEB-ABC算法进行求解. 在某区域内对1 500辆电动汽车进行仿真验证, 结果表明提出的优化调度模型可减少充电等待时间、充电成本和总行驶时间, 提高区域内充电站利用率.     

基于BMS与云平台的动力电池健康管理体系

为解决电动汽车现有BMS系统对锂离子动力电池SOH评估与预测难以满足多种工况条件、各种类动力电池,且难同时兼顾预测精度与反馈速度等应用缺陷,提出了一套全新的EVs电池健康管理系统设计思路,采用了结合云计算与存储平台,融入BMS评估体系等关键方法；通过BMS增加5G通讯模块,利用5G/4G信号实时上传电芯数据,经过云平台搭载的多种SOH评估模型与算法,多线程在线计算得到预测结果,及时反馈至用户端和BMS,实现电池健康管理；该体系的设计案例展示出较好的未来应用价值,为电动汽车电池管理设计提供了新方向。     

Intelligent power management in micro grids with EV penetration

Large deployment of Electric Vehicles (EVs) adds new challenges in the operation of a microgrid. Assuming that a number of EV owners allow their batteries to charge when their cars are parked, this paper proposes an approach that aims to find suitable individual active power set-points corresponding to the hourly charging rate of each EV battery connected to the microgrid. A multi agent system based controller is designed to find these active power set points for optimal power management of EVs, distributed energy resources in the microgrid, and the loads.     

面向用户行驶计划的电动汽车充电调度策略

充电站（桩）尚未普及以及电动汽车有限的行驶里程,使得大多数汽车用户关于是否选择电动汽车犹豫不决。为了减少用户对于电动汽车有限电池容量的担心,并且降低因频繁充电以及偏离原定行驶路线绕路进行充电所增加的电动汽车使用费用,提出一种基于匹配理论面向用户行驶计划的电动汽车充电调度方案TPCS。首先,分别根据电动汽车用户的行驶计划和对各充电站的电量需求构建电动汽车用户与充电站的偏好表;然后,建立电动汽车用户与充电站之间的多对一匹配模型;最后,以系统总效用为优化目标进行充电站接口分配。数值仿真结果显示,与随机分配（RCS）算法和仅考虑电动汽车效用分配（OEVS）算法相比,TPCS算法将系统总效用较RCS算法最多提高了39.3%,较OEVS算法最多提高了5%;而在电动汽车充电需求轻负载时,TPCS算法始终保证用户满意度在90%以上,高于RCS算法。所提算法能够有效地提高系统总效用和用户满意度,同时降低计算复杂度。     

基于太阳能充电站中风光火多目标优化管理问题的研究

新能源汽车行业的蓬勃发展,带来了新能源汽车充电站的建设和运营相关问题。无人值守是今后所有行业的一个主流趋势,充电站当然也不例外。无人值守的充电站被要求不仅能对站内突发情况自主解决,而且充电站能够充分、合理利用当地电力资源,实现电力资源的合理优化调度。因此文中的重点便是研究如何均衡使用当地已有的电力资源,使得充电站运营成本和电池损耗最小。文中在分析了太阳能充电站的系统功能和运营方式基础之上,建立了该系统接入风力、火力等电力资源的优化调度模型,通过非支配遗传算法NSGA-II对多目标函数优化模型进行求解,验证模型的有效性和可靠性,为未来涉及多种电力资源的运营管理问题提供了一条参考途径。     

换电站对配网无功优化的影响研究

换电站作为电动汽车的主要充电基础设施,随着电动汽车的普及也将大规模建设。为了分析大规模换电站接入配网对无功优化产生的影响,提出了以换电站充电负荷对配网无功优化的影响为研究对象。首先基于力帆换电站的历史数据分析了换电站充电负荷影响因素的分布规律,然后采用蒙特卡洛仿真模拟建立了换电站的充电负荷模型。还建立了配网无功优化的数学模型,采用禁忌搜索算法求解数学模型。考虑到换电站充电负荷为概率模型,采用基于蒙特卡洛模拟的随机潮流进行潮流计算。最后从换电站充电负荷的渗透率角度分析了对配网无功优化的影响,得出的结论是换电站充电负荷的渗透率为一定值时,相比未接入换电站充电负荷的情况,可以降低电容器的投切次数,延长电容器的使用寿命。     

基于POF-CAN通信总线的充电桩/站监测系统的设计

为解决电动汽车充电站总线通信问题，设计了一种基于塑料光纤（POF，Polymer Optical Fiber）通信总线的电动汽车充电桩/站监控系统。研究了塑料光纤传输介质的通信特性，开发了POF与CAN总线的POF-CAN转换模块。结合电动汽车充电站装置应用，设计了相应的硬件系统、嵌入式软件和POF-CAN系统应用层协议。在实验室环境下搭建虚拟充电站测试平台进行验证实验，结果表明该系统能够满足对实时性、可靠性的要求，且具有抗电磁干扰、施工维护灵活经济的特点，为提高电动汽车充电站监控系统的可靠性和抗干扰性提供了一种新的解决方案。     

计算智能在电动车充电站规划的应用研究综述

虽然近年来电动汽车销售市场在不断扩大,但过低的充电桩利用率使得电动汽车充换电服务运营商的收益并不乐观。通过大数据分析充电站的部署方式可以有效提升充电桩利用率。阐述了演化计算和群体智能主要算法的原理,研究了充电站规划的多目标优化数学模型,论述了演化计算和群体智能在充电站规划中的应用,研究了演化计算和群体智能在充电站规划过程中的改进方式,讨论了人工神经网络和模糊系统应用于充电站规划的可能性,并对发展现状和未来趋势进行了总结与展望。     

基于阿里云的电动汽车状态监控系统设计

针对电动汽车预约充电过程中云端无法实现获取多电动汽车电量以及位置的问题,设计并实现了一种用于电动汽车预约充电的电动汽车状态监控系统.系统主要由主控模块和通信模块以及定位模块构成,其中主控模块使用STM32F429,通信模块使用ATK-M750模组,定位模块为SkyTra_GPS,利用分压模块将电动汽车电池电压对应降低后...     

考虑用户选择偏好的电动汽车充电站规划研究

在国家大力发展新能源汽车的过程中,充电问题一直阻碍着电动汽车的发展,充电基础设施尤其是快速充电站的规划和建设尤为重要。大规模发展电动汽车（electric vehicle,EV）的关键是根据用户的充电选择偏好,建立完善的充电基础设施,减少用户的里程焦虑,彻底解决充电不方便的问题。在考虑了各方面社会因素并确定一定数量的候选节点背景研究的基础上,提出了一种双目标规划模型,在满足需求、距离、容量等约束条件下,分析了建设充电站总成本和充电覆盖范围之间的关系,寻找最优的充电站建设方案,并以A城市B区为例,通过多目标粒子群算法进行求解,求出充电站的最佳节点和数量。用不同算法进行求解,通过对结果进行分析比较,表明多目标粒子群算法（MOPSO）在求解双目标问题时更具有实际意义。     

Truly autonomous robots: hardware design for an energy trophallactic robot

This article describes the mechanical and electrical design of a mobile robot called the CISSbot. CISSbot is abbreviated from our center’s name in Danish: Center for Indlejrede Software Systemer. The robots are designed toward truly autonomous robots in large populations through energy trophallaxes. Unlike present mobile robots, the CISSbots are energetically autonomous robots because they are not able not only to autonomously refuel by picking up batteries at a charging station, but also to share energy by exchanging batteries with other robots. The CISSbots basically consist of their own processing power, sensors, and actuators. However, to achieve the capability of battery exchange, the CISSbots need a special design of battery exchange mechanism. In this paper, we present the realization of the design in both the mechanics and the electronics of the CISSbot. Details of the battery exchange technique and power management are clarified. Finally, the article gives an outline of our future work on the CISSbots.     

A dynamic model of electric vehicle adoption: The role of social commerce in new transportation

This research explores the adoption of electric vehicles (EVs) as a substitute for internal combustion engine vehicles (ICVs) and examines its emergence as a mobile intelligent terminal of social commerce. We present a system dynamics (SD) model incorporating fuzzy logic to simulate the adoption process. The results suggest that consumers’ vague perceptions and the dispersion of pilot EV projects have caused EV adoption to be delayed; nevertheless, the introduction of social commerce to EVs can help relieve this problem and promote EV adoption. However, the timing of introducing social commerce is critical for enhancing its positive effect on EV adoption.     

考虑灵活充电策略的带时间窗物流配送路径优化研究

电动汽车作为一种新型绿色交通运输工具,目前被广泛的应用于多种物流场景中.然而,电池容量有限、充电时间长以及配套设施不健全等问题制约着其在物流配送领域中的有效推广.为此,针对电动汽车的物流配送路径优化问题,引入一种部分充电策略,提出了考虑部分充电策略的带时间窗电动汽车物流配送路径优化问题,建立了该问题的整数规划模型,并设计混合模拟退火算法对其求解.最后,利用一个研究算例对模型和算法进行了测试和数值分析,验证了其有效性.