Design and Implementation of a Battery Big Data Platform Through Intelligent Connected Electric Vehicles

The development of a battery management algorithm is highly dependent on high-quality battery operation data,especially the data in extreme conditions such as low temperatures.The data in faults are also essential for failure and safety management research.This study developed a battery big data platform to realize vehicle operation,energy interaction and data management.First,we developed an electric vehicle with vehicle navigation and position detection and designed an environmental cabin that allows the vehicle to operate autonomously.Second,charging and heating systems based on wireless energy transfer were developed and equipped on the vehicle to investigate optimal charging and heating methods of the batteries in the vehicle.Third,the data transmission network was designed,a real-time monitoring interface was developed,and the self-developed battery management system was used to measure,collect,upload,and store battery operation data in real time.Finally,experimental validation was performed on the platform.Results demonstrate the efficiency and reliability of the platform.Battery state of charge estimation is used as an example to illustrate the availability of battery operation data.

Performance Analysis of Plug-in Hybrid Passenger Vehicles

PHEVs （passenger plug-in hybrid electric vehicles） have shown significant fuel reduction potential. Furthermore, PHEVs can also improve longitudinal vehicle dynamics with respect to acceleration and engine elasticity. The objective of this study is to investigate potential of concurrent optimization of fuel efficiency and driving performance. For the studies, a backward vehicle model for a parallel PHEV was designed, where the power flow is calculated from the wheels to the propulsion units, the conventional ICE （internal combustion engine） and the EMG （electric motor/generator） unit. The hybrid drive train is according to a P2 layout, consequently the EMG is situated between the shifting clutch and the ICE. The implemented operation strategy distributes the power to both propulsion units depending on the vehicle speed, requested driving torque, the battery＇s SOC （state of charge） and SOP （state of power）. Additional information, such as the slope of the road, can be taken into account by the operation strategy. In the paper, the fuel saving potential as well as the longitudinal dynamics change of different PHEV configurations is presented as a function of battery capacity and EMG power. Consequently, applicable hybrid components can be defined. By using additional information of the environment like various sensor data, road slope amongst others, the fuel saving potential can be improved even more. By studying the dynamic model, the overall results of the backward model are confirmed. In conclusion, this study shows that it is possible to concurrently reduce fuel consumption and increase driving performance in PHEVs. The potential depends strongly on the configuration of the electric components and the implemented operation strategy. Consequently, the hybrid system configuration has to be chosen carefully and aligned to the vehicle performance.

Operation-area-constrained Adaptive Primary Frequency Support Strategy for Electric Vehicle Clusters

Due to their fast response and strong short-term power throughput capacity, electric vehicles(EVs) are promising for providing primary frequency support to power grids. However, due to the complicated charging demands of drivers, it is challenging to efficiently utilize the regulation capacity of EV clusters for providing stable primary frequency support to the power grid. Accordingly, this paper proposes an adaptive primary frequency support strategy for EV clusters constrained by the charging-behavior-defined operation area. First, the forced charging boundary of the EV is determined according to the driver's charging behavior, and based on this, the operation area is defined. This ensures full utilization of the available frequency support capacity of the EV. An adaptive primary frequency support strategy of EV clusters is then proposed. The output power of EV is adaptively regulated according to the real-time distance from the EV operating point to the forced charging boundary. With the proposed strategy, when the EV approaches the forced charging boundary, its output power is gradually reduced to zero. Then, the rapid state-of-charge declines of EVs and sudden output power reductions in EV clusters caused by forced charging to meet the driver's charging demands can be effectively avoided. EV clusters can then provide sustainable frequency support to the power grid without violating the driver's charging demands. Simulation results validate the proposed operation-area-constrained adaptive primary frequency support strategy, which outperforms the average strategy in terms of stable output maintenance and the optimal utilization of regulation capacities of EV clusters.

Operation Strategy of EV Battery Charging and Swapping Station

An operation strategy of the electric vehicle （EV） battery charging and swapping station is proposed in the paper. The strategy is established based on comprehensively consideration of the EV charging behaviors and the possible mutual actions between battery charging and swapping. Three energy management strategies can be used in the station： charging period shifting, energy exchange between EVs, and energy supporting from surplus swapping batteries. Then an optimization model which minimizes the total energy management costs of the station is built. The Monte Carlo simulation is applied to analyze the characteristics of the EV battery charging load, and a heuristic algorithm is used to solve the strategy providing the relevant information of EVs and the battery charging and swapping station. The operation strategy can efficiently reduce battery charging during the high electricity price periods and make more reasonable use of the resources. Simulations prove the feasibility and rationality of the strategy.

Simulation Research on Combined Operation of Electric Vehicle Battery Swapping Station and Wind Farm

Towards the analysis of the developmental situation of wind power generation and electric vehicles,a novel idea for stabilizing the fl uctuation of wind farms’output by the use of battery swapping stations of electric vehicles is put forward in this paper,to effectively alleviate the impact of grid-connected operation of wind farms on the power system while promoting the fi eld operation of charging and battery swapping stations.A battery swapping station is treated as a capacity-variable energy storage power station,connected to the output terminal of a wind farm.A combined operation model for wind farm and battery swapping station is established based on the MATLAB/SIMULINK simulation platform and the control strategy is proposed for the operation of battery swapping stations.The simulation results show that the introduction of a battery swapping station can effectively stabilize the fl uctuation of wind farm output.

电动汽车与清洁能源融合发展技术研究与展望

以光电、风电为代表的清洁能源发电技术可为电动汽车提供清洁的电力供给,减少全生命周期碳排放,助力交通领域“双碳”战略目标的实现,因此近年来得到迅猛发展。为了推动交能融合技术的应用,明确相关研究中的关键问题,该文综述了国内外学者过去10年在电动汽车与清洁能源融合技术方面的研究进展。首先,介绍了二者融合发展的必要性与现实意义;其次,将已有研究成果分为公路环境与城市道路环境两类,分别从电网、充电站、车辆3个层面归纳了当前电动汽车与清洁能源融合技术的研究成果;最后,考虑到清洁能源发电以及电动汽车充电存在较强的随机波动,对未来发展提出了加强公共领域交能融合发展、构建多种互补型清洁能源发电策略、建立“源—网—荷—储”集成优化技术、强化分布式微电网能源自洽调度、加强无线光伏充电公路运营管理等建议。

“人-车-桩-路-网”深度耦合下的配电网协同规划与运行优化

电动汽车(EV)作为连接交通电气化和电网清洁化的纽带和桥梁,可实现电力-交通-信息之间的深度耦合,形成电力-交通一体化的规划及运行架构。配电-交通融合系统(DTIS)中,EV充放电行为多时空动态交织、电能流-交通流-信息流深度融合、多利益主体动态博弈等大量不确定性因素使配电网的规划及运行优化的边界都将发生重大转变,但同时也给挖掘利用EV的移动储能特性和提升配电网灵活性带来了契机。为此,在分析“人-车-桩-路-网”深度耦合下配电网的形态演化特征基础上,剖析了新形态下配电网协同规划与运行优化所面临的新挑战,进而针对“人-车-桩-路-网”耦合下的EV灵活性建模、灵活域高效构建及预测、协同规划、运行优化4个方面关键技术展开讨论,并对相关技术问题的研究方向进行了展望。

考虑充放电激励机制的电动汽车聚合商参与能量-调频市场多阶段运营策略

完善电动汽车(EV)主动参与充放电双向互动调频的激励机制,综合考虑EV充放电行为在能量-调频市场中的多阶段交互影响,有利于准确评估聚合商参与电力市场的收益。提出了EV聚合商参与多品种市场的多阶段运营策略,以实现聚合商的运营收益最大化。基于充电行为特征的统计数据,建立单体EV的功率及能量响应边界,并考虑用户参与充电和放电控制的差异性,结合用户自身偏好、电池损耗,提出EV集群的充放电激励机制,并在此基础上建立EV集群功率及能量的响应边界约束。基于EV充放电调节控制的时序性和多阶段市场的调频需求,建立考虑多阶段时序交互的多品种市场运营收益优化模型。结合PJM市场的运营案例,验证所提充放电激励机制和运营策略的有效性,结果表明其能在保障用户充电需求的基础上,有效提升聚合商参与能量-调频市场的收益。

某纯电驱动重载车辆能耗预测模型

高精度能耗预测模型是准确预测车辆续驶里程的重要前提。针对载荷大幅度变化且非结构化道路运行的纯电驱动重载车辆,建立其组合能耗模型,该模型由能耗计算基本模型与长短时记忆(Long Short-Term Memory,LSTM)神经网络差值修正两部分组成。基于能量流动过程驱动电机和变速器效率建模,结合汽车行驶动力学建立能耗计算基本模型;采用LSTM神经网络来修正基本模型能耗预测结果与车辆典型工况功率测试值的差值,有效提高了大幅变载荷且低信噪比坡度环境下的车辆能耗预测精度,因此组合能耗模型具有参数简单和模型拟合不需解释能耗规律的优点。经试验测试分析,与VT-Micro能耗模型和径向基(Radial Basis Function,RBF)神经网络能耗模型相比,所提组合能耗模型的功率预测平均误差率分别降低了17.76%和3.35%,能够实现纯电驱动重载车辆复杂工况下能耗的准确实时预测。

移动充放电设施技术及其规划与运营研究综述

目前,充放电需求区域性、时段性短缺,固定充电设施存在总量不足、扩容难、利用率低等问题,移动充电设施因其移动便捷性,受到广泛关注。阐明移动充放电设施的8种典型应用场景,阐述了“智能插座+移动充电桩”、不带储能装置的移动充电车、带储能装置的移动充放电车3种移动充放电设施类型。分析规划布局、调度运行、运营模式与价格机制、关键组件以及安全等关键技术。针对移动模式选取等7个关键方面对移动充放电设施的发展模式进行总结和展望。

高速电驱系统的油量和效率关系研究

新能源汽车的高速发展,推动了电驱动系统技术的快速迭代升级,驱动电机的转速越来越高。为研究电驱动系统高速化对系统效率的影响,文章深入探究了高速运行状态下润滑油量与电驱系统效率之间的关系。以一款120 kW电驱系统为研究对象,首先利用Masta-Nano FlowedX软件进行不同扭矩、不同转速下的效率仿真分析,随后,通过台架实验对仿真结果进行验证,探究了润滑油量、转速与电驱系统运行效率之间的关联性,并确认了理论分析与实际应用的一致性。文章提出了一种基于实际工况的优化策略,对电驱动系统优化设计具有一定的理论指导价值,也为实际操作提供了方法论参考,对推动新能源汽车相关技术的发展具有重要影响。

考虑电力-交通耦合网动态协调的EV集群灵活性挖掘与优化调度

电动汽车(EV)作为兼具交通与能量双重属性的跨域主体,发挥其时空灵活性将助力电力-交通耦合网的协调运行。为此,文中考虑电力-交通耦合网综合效益,提出了EV集群调控策略。首先,基于弧阻抗函数构建了动态交通网络加载模型。其次,考虑EV用户的各特征参数存在的差异性与耦合性,构建了单体EV灵活运行域模型,并采用基于zonotope线性近似的闵可夫斯基和算法对其聚合,得到EV集群的时变灵活运行域。在此基础上,提出了动态交通流最优分配下EV集群灵活性调控的双层模型,迭代求解得到上下两层耦合变量构成的瞬时单位流量出行成本,从而引导EV的出行与充放电行为。最后,通过与最短路径引导策略进行比较,验证了所提EV集群调控策略的有效性。

电动汽车公共充电设施建设与运维质量的建议

电动汽车充电设施作为新能源汽车产业发展的重要支撑,其建设与运维质量直接关系到新能源汽车的普及与推广。通过对目标区域公共充电设施的调查,深入剖析了当前充电设施在选址布局、建设标准、价格体系及使用效率等方面存在的问题。在此基础上,从主管部门、参与企业及消费者三个维度出发,提出了针对性的改进措施和建议。相关研究成果对于推动南昌地区充电基础设施的高质量发展具有很好的参考价值。

增程式电动汽车发动机的多工作点控制策略

为了研究不同的发动机多工作点控制策略对整车燃油经济性的影响,以某增程式电动汽车为研究对象,在满足整车动力性前提下,设计了发动机三工作点和五工作点控制策略。在不同循环工况下,对比分析了2种控制策略。仿真结果表明,三工作点和五工作点控制策略能够满足整车动力性要求,且三工作点控制策略的燃油经济性更优。

交通网络和电力网络融合承载力的研究综述

电动汽车作为交通电气化的核心,对减少温室气体排放和提高能源效率起到了积极的作用。显著增长的电动汽车保有量和市场占比对充电基础设施产生了一定的影响,如充电设施不足和电网负荷波动等问题凸显。本文深入梳理了交通网络承载力和电力网络承载力的基本概念、计算方法和评估指标,分析了交通与能源网络两网评估方法与两网融合韧性,并探讨了交通-电力融合系统潜在的挑战与策略;揭示出目前亟待开展的研究领域并指出了未来的研究方向,以期提高充电设施效率,缓解交通拥堵,保障电网稳定运行。

Emission Trading Based Optimal Scheduling Strategy of Energy Hub with Energy Storage and Integrated Electric Vehicles

This paper focuses on the optimal scheduling of the district energy system with multiple energy supply modes and flexible loads.For multi-energy system(MES),the energy hub(EH)model including energy storage system and integrated electric vehicle(EV)is established.Based on the model,the influence of pollutant trading market on total operation cost is analyzed,and the optimal scheduling strategy is further put forward to realize the minimum purchase cost and emission tax cost of the MES.Finally,this paper compares the economic benefit of the fixed mode and the response mode,and discusses the contribution of the energy storage device and the multi-energy complementary mode to energy utilization efficiency.The simulation results indicate that optimal scheduling strategy of the EH can coordinate various energy complementary modes reasonably.Meanwhile,the proposed strategy is able to improve the operation economy of the EH,and ensure the better response effect of the demand side.The sensitivity analysis demonstrates the impact of pollutant emission price change on emission reduction.

考虑未来运营收益的自动驾驶出租车充放电协同路径规划

现有的出租车调度模型通常只优化实时成本而忽视当前路径规划对未来运营收益的影响,这不利于自动驾驶环境下的连续调度。为此,本文提出一个专注于长期收益的路径规划模型,并利用强化学习将预估的未来运营收益整合到实时调度问题中。模型的具体求解方法是先利用神经网络来拟合车辆的不同时空状态的状态价值函数,再通过双神经网络和经验池的方式加快算法收敛。深圳路网仿真实验表明,所提出的调度模型能够预先精准地调度车队,服务更多乘客,获得更大的运营收益;并且模型能够利用分时电价的峰谷特征和电动汽车入网(vehicle to grid,V2G)技术进行充放电,从而降低车队的能耗成本。相较于其他调度模型,该模型在长期运营中实现乘客匹配服务率增加4%,总收益提高25%,能耗成本节省50%以及乘客等待时间降低20%。

计及出行行为调整的综合能源系统双层博弈优化策略

针对园区综合能源系统中各个主体的利益难以兼顾及新能源汽车出行计划临时变动会对系统运行带来不利影响的问题,提出了考虑车主出行行为调整的电热氢综合能源系统双层博弈优化运行策略。首先,建立以能源运营商(IESO)、负荷聚合商(ULA)和新能源汽车负荷聚合商(NEVLA)为主体的Stackelberg-Shapley双层博弈框架;其次,将双层博弈框架嵌入两阶段模型预测控制滚动调度模型中,并在第1阶段引入惩罚博弈机制,抑制车主按照个人意愿无序改变充电计划的行为;最后,联合沙丘猫优化算法与CPLEX求解器对博弈模型进行求解。仿真结果表明:所提策略与无惩罚博弈机制的滚动优化运行策略及考虑惩罚博弈机制的单断面优化运行策略对比,能源运营商的收益分别提高了4.43%、7.75%,负荷聚合商的剩余价值分别提高了6.48%、12.18%,电动汽车负荷调度成本分别减少了13.64%、4.43%,验证了所提策略具有更好的系统运行经济性。

低碳目标下电气化交通网最优定价策略

作为降低化石燃料消耗、缓解环境危机的有效途径,采用电动汽车(EV)替代燃油汽车(GV)越来越受到人们的重视,这促进了EV的发展和普及,使城市交通网(UTN)和配电网(PDN)的运营紧密耦合。本文从充电网运营商(CNO)自身利益最大化的角度出发,解决电动汽车充电站(EVCS)最优充电定价问题。计及UTN和PDN的相互影响,并保护各个部门的隐私信息。综合考虑GV和EV的碳排放,通过充电价格调控EV充电行为,使充电负荷在不同PDN之间转移,促进电气化交通网的最优经济-低碳运行。数值算例验证了所提出模型和方法的有效性,以及CNO优化定价对电气化交通网的影响。

Grid Integration of Electric Vehicles for Economic Benefits:A Review

Emissions from the internal combustion engine(ICE) vehicles are one of the primary cause of air pollution and climate change. In recent years, electric vehicles(EVs) are becoming a more sensible alternative to these ICE vehicles. With the recent breakthroughs in battery technology and large-scale production, EVs are becoming cheaper. In the near future,mass deployment of EVs will put severe stress on the existing electrical power system(EPS). Optimal scheduling of EVs can reduce the stress on the existing network while accommodating large-scale integration of EVs. The integration of these EVs can provide several economic benefits to different players in the energy market. In this paper, recent works related to the integration of EV with EPS are classified based on their relevance to different players in the electricity market. This classification refers to four players: generation company(GENCO), distribution system operator(DSO), EV aggregator, and end user. Further classification is done based on scheduling or charging strategies used for the grid integration of EVs. This paper provides a comprehensive review of technical challenges in the grid integration of EVs along with their solution based on optimal scheduling and controlled charging strategies.