怠速充电工况下的电池SOC平衡控制

在分析单电机和双电机混合动力电动车发动机怠速充电工况下电池能量稳定性控制要求的基础上,提出了一种怠速充电工况电池SOC平衡的主动控制策略,并给出相应控制过程的能量控制目标值计算公式和相应的分析。通过对所提出的怠速充电工况电池SOC平衡控制策略进行仿真和实车测试,结果表明,该控制策略能有效控制电池SOC平衡,怠速充电过程中电池主动能量的过充和过放控制的稳定性也得到改善。     

锂离子动力电池充电方式的研究

综述了国内外锂离子动力电池充电方式所经历的各个发展阶段和最新的成果,并阐述了每种充电模式的优缺点和对电池寿命的影响,着重介绍了快速充电方式的发展历程,总结出其发展的规律。在众多的充电模式中,脉冲充电和快速智能充电由于省时、高效和寿命长等优点已被大量研究和应用。本研究对锂动力电池的实际应用具有重要的指导意义。     

收费公路中相关法律问题的研究

收费公路是我国公路在新的建设环境下的产物，其在我国的发展过程中存在着很多问题，为解决这些问题。就必须提高收费公路制度的法律层次，完善收费公路的法律法规。     

铁路充电间酸雾治理措施的研究

酸性蓄电池在铁路内应用较多。充电过程中散发的酸雾为气溶胶，酸雾对人体和设备可造成危害，必须采取通风措施进行治理。在通风设计中，应对车间空气的含氢气量、换气次数进行计算，以保证通风效果。排气罩是通风系统中关键部件，通过对多种罩口型式的比较，说明活动式密闭罩是控制酸雾扩散的较好罩口形式。冲击式填料酸雾净化塔，以氢氧化钠溶液为吸收液，可使酸雾的排放浓度达到国家卫生标准。     

涡轮增压器与柴油机全转速范围内联合运行关系模型

建立涡轮增压柴油机部分转速的增压压比和最高转速的增压压比的关系模型。对模型求解,得到表征两个压比的比值随柴油机转速、最高转速的压比变化的MAP图。与某型柴油机实验结果的对比表明,该模型可以较准确地预测两个压比之间的关系。模型还给出获得同样的增压压比时,低速和高速之间匹配的涡轮当量面积的关系。     

低温BMS系统剩余充电时间预估的研究

当前新能源汽车大规模使用,充电时间过长是影响车主使用的主要问题,而剩余充电时间预估不准又给车主带来另外的烦恼,特别是低温状态下充电预估时间,因低温加热影响又增加了预估时间难度。本文介绍一种低温BMS系统剩余充电时间预估的策略,并结合真实的充电柜和温度箱进行验证。     

某纯电动汽车直流充电异常分析及解决方案

随着纯电动汽车的普及,电动汽车充电系统运行的可靠性及稳定性关乎整车的安全性,车辆在充电过程中出现充电异常的情况,应受到足够的重视。本文通过某纯电动汽车在点火锁处于关闭状态下直流充电异常的案例,结合整车控制通信架构讲述该纯电动汽车快充系统无法充电的异常原因分析及排除过程,为电动汽车从业者提供一些整车通信网络架构设计思路和充电异常的分析思路。     

沪昆高铁防灾系统改造与优化

通过统计告警分布,对高铁防灾设备电源类和网络类告警的原因、特点进行分析和判断,并给出处理和减少此两类告警的思路和实施方案。     

Electric vehicle charging station locations: Elastic demand,station congestion,and network equilibrium

Battery-only electric vehicles (BEVs) generally offer better air quality through lowered emissions, along with energy savings and security. The issue of long-duration battery charging makes charging-station placement and design key for BEV adoption rates. This work uses genetic algorithms to identify profit-maximizing station placement and design details, with applications that reflect the costs of installing, operating, and maintaining service equipment, including land acquisition. Fast electric vehicle charging stations (EVCSs) are placed across a congested city's network subject to stochastic demand for charging under a user-equilibrium traffic assignment. BEV users’ station choices consider endogenously determined travel times and on-site charging queues. The model allows for congested-travel and congested-station feedback into travelers’ route choices under elastic demand and BEV owners’ station choices, as well as charging price elasticity for BEV charging users.Boston-network results suggest that EVCSs should locate mostly along major highways, which may be a common finding for other metro settings. If 10% of current EV owners seek to charge en route, a user fee of $6 for a 30-min charging session is not enough for station profitability under a 5-year time horizon in this region. However, $10 per BEV charging delivers a 5-year profit of $0.82 million, and 11 cords across 3 stations are enough to accommodate a near-term charging demand in this Boston-area application. Shorter charging sessions, higher fees, and/or allowing for more cords per site also increase profits generally, everything else constant. Power-grid and station upgrades should keep pace with demand, to maximize profits over time, and avoid on-site congestion.     

Integrated planning of static and dynamic charging infrastructure for electric vehicles

This paper investigates the optimal deployment of static and dynamic charging infrastructure considering the interdependency between transportation and power networks. Static infrastructure means plug-in charging stations, while the dynamic counterpart refers to electrified roads or charging lanes enabled by charging-while-driving technology. A network equilibrium model is first developed to capture the interactions among battery electric vehicles’ (BEVs) route choices, charging plans, and the prices of electricity. A mixed-integer bi-level program is then formulated to determine the deployment plan of charging infrastructure to minimize the total social cost of the coupled networks. Numerical examples are provided to demonstrate travel and charging plans of BEV drivers and the competitiveness of static and dynamic charging infrastructure. The numerical results on three networks suggest that (1) for individual BEV drivers, the choice between using charging lanes and charging stations is more sensitive to parameters including value of travel time, service fee markup, and battery size, but less sensitive to the charging rates and travel demand; (2) deploying more charging lanes is favorable for transportation networks with sparser topology while more charging stations can be more preferable for those denser networks.