Battery Management System with State ofCharge Indicator for Electric Vehicles

Aim To research and develop a battery management system(BMS)with the state of charge(SOC)indicator for electric vehicles (EVs).Methods On the basis of analyzing the electro-chemical characteristics of lead-acid. battery, the state of charge indicator for lead-acid battery was developed by means of an algorithm based on combination of ampere-hour, Peukert's equation and open-voltage method with the compensation of temperature,aging,self- discharging,etc..Results The BMS based on this method can attain an accurate surplus capa- city whose error is less than 5% in static experiments.It is proved by experiments that the BMS is reliable and can give the driver an accurate surplus capacity,precisely monitor the individual battery modules as the same time,even detect and warn the problems early,and so on. Conclusion A BMS can make the energy of the storage batteries used efficiently, develop the batteries cycle life,and increase the driving distance of EVs.

Reinforcement Learning-Based Electric Vehicles Energy Management Strategy with Battery Thermal Model

The promotion of electric vehicles(EVs)is restricted due to their short cruising range.It is desirable to design an effective energy management strategy to improve their energy efficiency.Most existing work concerning energy management strategies focused on hybrids rather than the EVs.The work focusing on the energy management strategy for EVs mainly uses the traditional optimization strategies,thereby limiting the advantages of energy economy.To this end,a novel energy management strategy that considered the impact of battery thermal effects was proposed with the help of reinforcement learning.The main idea was to first analyze the energy flow path of EVs,further formulize the energy management as an optimization problem,and finally propose an online strategy based on reinforcement learning to obtain the optimal strategy.Additionally,extensive simulation results have demonstrated that our strategy reduces energy consumption by at least 27.4%compared to the existing methods.

Experimental Investigation on Cooling/Heating Characteristics of Ultra-Thin Micro Heat Pipe for Electric Vehicle Battery Thermal Management

Due to the heat pipes’ transient conduction,phase change and fluid dynamics during cooling/heating with high frequency charging/discharging of batteries,it is crucial to investigate in depth the experimental dynamic thermal characteristics in such complex heat transfer processes for more accurate thermal analysis and design of a BTMS. In this paper,the use of ultra?thin micro heat pipe(UMHP) for thermal management of a lithium?ion battery pack in EVs is explored by experiments to reveal the cooling/heating characteristics of the UMHP pack. The cooling performance is evaluated under di erent constant discharging and transient heat inputs conditions. And the heating e ciency is assessed under several sub?zero temperatures through heating films with/without UMHPs. Results show that the pro?posed UMHP BTMS with forced convection can keep the maximum temperature of the pack below 40 °C under 1 ~ 3 C discharging,and e ectively reduced the instant temperature increases and minimize the temperature fluctuation of the pack during transient federal urban driving schedule(FUDS) road conditions. Experimental data also indicate that heating films stuck on the fins of UMHPs brought about adequate high heating e ciency comparing with that stuck on the surface of cells under the same heating power,but has more convenient maintenance and less cost for the BTMS. The experimental dynamic temperature characteristics of UMHP which is found to be a high?e cient and low?energy consumption cooling/heating method for BTMSs,can be performed to guide thermal analysis and optimiza?tion of heat pipe BTMSs.

Performance Study of the MPC based on BPNN Prediction Model in Thermal Management System of Battery Electric Vehicles

In this paper,a model predictive control(MPC)based on back propagation neural network(BPNN)prediction model was proposed for compressor speed control of air conditioning system(ACS)and battery thermal management system(BTMS)coupling system of battery electric vehicle(BEV).In order to solve the problem of high cooling energy consumption and inferior thermal comfort in the cabin of the battery electric vehicle thermal management system(BEVTMS)during summer time,this paper combines the respective superiorities of artificial neural network(ANN)predictive modeling and MPC,and creatively combines the two methods and uses them in the control of BEVTMS.Firstly,based on ANN and heat transfer theory,BPNN prediction model,ACS and BTMS coupling system were established and verified.Secondly,a mathematical method of MPC was established to control the speed of the compressor.Then,the state parameters of the coupled system were predicted using a BPNN prediction model,and the predicted values were passed to the MPC,thus achieving accurate control of the compressor speed using the MPC.Finally,the effects of PID control and MPC based on BPNN prediction model on thermal comfort of cabin and compressor energy consumption at different ambient temperatures were compared in simulation under New European Driving Cycle(NEDC)conditions.The results showed for the constructed BPNN prediction model predicted and tested values of the selected parameters the mean squared error(MSE)ranged from 2.498%to 8.969%,mean absolute percentage error(MAPE)ranged from 4.197%to 8.986%,and mean absolute error(MAE)ranged from 3.202%to 8.476%.At ambient temperatures of 25℃,35℃ and 45℃,the MPC based on the BPNN prediction model reduced the cumulative discomfort time in the cabin by 100 s,39 s and 19 s,respectively,compared with the PID control.Under three NEDC conditions,the energy consumption is reduced by 1.82%,2.35%and 3.48%,respectively.When the ambient temperature was 35℃,the MPC based on BPNN prediction model can make the ACS and BTMS coupling system have better thermal comfort,and the energy saving effect of the compressor was more obvious with the temperature.

Dynamic Cell Modeling for Accurate SOC Estimation in Autonomous Electric Vehicles

This paper presents findings on dynamic cell modeling for state-of-charge (SOC) estimation in an autonomous electric vehicle (AEV). The studied cells are Lithium-Ion Polymer-based with a nominal capacity of around 8 Ah, optimized for power-needy applications. The AEV operates in a harsh environment with rate requirements up to ±25C and highly dynamic rate profiles, unlike portable-electronic applications with constant power output and fractional C rates. SOC estimation methods effective in portable electronics may not suffice for the AEV. Accurate SOC estimation necessitates a precise cell model. The proposed SOC estimation method utilizes a detailed Kalman-filtering approach. The cell model must include SOC as a state in the model state vector. Multiple cell models are presented, starting with a simple one employing “Coulomb counting” as the state equation and Shepherd’s rule as the output equation, lacking prediction of cell relaxation dynamics. An improved model incorporates filter states to account for relaxation and other dynamics in closed-circuit cell voltage, yielding better performance. The best overall results are achieved with a method combining nonlinear autoregressive filtering and dynamic radial basis function networks. The paper includes lab test results comparing physical cells with model predictions. The most accurate models obtained have an RMS estimation error lower than the quantization noise floor expected in the battery-management-system design. Importantly, these models enable precise SOC estimation, allowing the vehicle controller to utilize the battery pack’s full operating range without overcharging or undercharging concerns.

Intelligent Vehicle Electrical Power Supply System with Central Coordinated Protection

The current research of vehicle electrical power supply system mainly focuses on electric vehicles（EV） and hybrid electric vehicles（HEV）.The vehicle electrical power supply system used in traditional fuel vehicles is rather simple and imperfect;electrical/electronic devices（EEDs） applied in vehicles are usually directly connected with the vehicle＇s battery.With increasing numbers of EEDs being applied in traditional fuel vehicles,vehicle electrical power supply systems should be optimized and improved so that they can work more safely and more effectively.In this paper,a new vehicle electrical power supply system for traditional fuel vehicles,which accounts for all electrical/electronic devices and complex work conditions,is proposed based on a smart electrical/electronic device（SEED） system.Working as an independent intelligent electrical power supply network,the proposed system is isolated from the electrical control module and communication network,and access to the vehicle system is made through a bus interface.This results in a clean controller power supply with no electromagnetic interference.A new practical battery state of charge（So C） estimation method is also proposed to achieve more accurate So C estimation for lead-acid batteries in traditional fuel vehicles so that the intelligent power system can monitor the status of the battery for an over-current state in each power channel.Optimized protection methods are also used to ensure power supply safety.Experiments and tests on a traditional fuel vehicle are performed,and the results reveal that the battery So C is calculated quickly and sufficiently accurately for battery over-discharge protection.Over-current protection is achieved,and the entire vehicle＇s power utilization is optimized.For traditional fuel vehicles,the proposed vehicle electrical power supply system is comprehensive and has a unified system architecture,enhancing system reliability and security.

Equalization Charging and Protection System for Electric Vehicle

A system is developed to improve the series battery packs uniformities and charging protection and the implementation of battery equalization charging and protection system is also introduced. The functions of equalization charging and overcharging protection are analyzed and the control model of series battery packs equalization charging is setup. The diverting-current and feedback bus voltage are measured during the series Li-ion battery packs equalization charging experiment. The field operation on Electric luxury transit bus BFC6100EV shows that the system betters the battery series charging uniformities and overcharging protection, improves the battery performance and extends the battery life.

Design of the Control System about Central Signals in Electric Vehicle

Based on the development demand of electric vehicles, this paper studies the communication network in electric vehicles based on CAN bus after analyzing the LCD (liquid crystal display) in electric vehicles and the status quo of research and development trend about communication network. This system uses TMS320LF2407 DSP as the controller of the control system about central signals. It has also developed the LCD system based on CAN bus and the experiment platform which is applied pure electric vehicles.

基于DSP的动力电池管理系统CAN通信设计

电池管理可以有效地延长电池组的使用寿命,是电动汽车设计和研究中的重要组成部分。针对系统需求,设计了基于CAN总线技术、以DSP为核心控制器的电池管理系统,介绍了该系统的组成和功能;对CAN模块的扩展方案进行了比较,优选出带SPI口的CAN控制器MCP2515的扩展方案,详细介绍了该方案的软、硬件实现;最后给出该系统的CAN总线通信协议。

Communication-less Management Strategy for Electric Vehicle Charging in Droop-controlled Islanded Microgrids

Adopting high penetration levels of electric vehicles(EVs) necessitates the implementation of appropriate charging management systems to mitigate their negative impacts on power distribution networks. Currently, most of the proposed EV charging management techniques rely on the availability of high-bandwidth communication links. Such techniques are far from realization due to(1) the lack of utility-grade communication systems in many cases such as secondary(low-voltage) power distribution systems to which EVs are connected, rural areas, remote communities, and islands, and(2) existing fears and concerns about the data privacy of EV users and cyber-physical security. For these cases, appropriate local control schemes are needed to ensure the adequate management of EV charging without violating the grid operation requirements. Accordingly, this paper introduces a new communication-less management strategy for EV charging in droop-controlled islanded microgrids. The proposed strategy is autonomous, as it is based on the measurement of system frequency and local bus voltages. The proposed strategy implements a social charging fairness policy during periods when the microgrid distributed generators(DGs) are in short supply by allocating more system capacity to the EVs with less charging in the past. Furthermore, a novel communication-less EV load shedding scheme is incorporated into the management strategy to provide relief to the microgrid during events of severe undervoltage or underfrequency occurrences due to factors such as high loading or DG outages. Numerical simulations demonstrate the superiority of the proposed strategy over the state-of-the-art controllers in modulating the EV charging demand to counteract microgrid instability.

Online SOC estimation based on modified covariance extended Kalman filter for lithium batteries of electric vehicles

To offset the defect of the traditional state of charge(SOC)estimation algorithm of lithium battery for electric vehicle and considering the complex working conditions of lithium batteries,an online SOC estimation algorithm is proposed by combining the online parameter identification method and the modified covariance extended Kalman filter(MVEKF)algorithm.Based on the parameters identified on line with the multiple forgetting factors recursive least squares methods,the newly-established algorithm recalculates the covariance in the iterative process with the modified estimation and updates the process gain which is used for the next state estimation to decrease errors of the filter.Experiments including constant pulse discharging and the dynamic stress test(DST)demonstrate that compared with the EKF algorithm,the MVEKF algorithm produces fewer estimation errors and can reduce the errors to 5%at most under the complex charging and discharging conditions of batteries.In the charging process under the DST condition,the EKF produces a larger deviation and lacks stability,while the MVEKF algorithm can estimate SOC stably and has a strong robustness.Therefore,the established MVEKF algorithm is suitable for complex and changeable working conditions of batteries for electric vehicles.

A Vehicle Routing Problem Based on Intelligent Batteries Transfer Management for the EV Network

Batteries transfer management is one important aspect in electric vehicle(EV)network's intelligent operation management system.Batteries transfer is a special and much more complex VRP(Vehicle Routing Problem) which takes the multiple constraints such as dynamic multi-depots,time windows,simultaneous pickups and deliveries,distance minimization,etc.into account.We call it VRPEVB(VRP with EV Batteries).This paper,based on the intelligent management model of EV's battery power,puts forward a battery transfer algorithm for the EV network which considers the traffic congestion that changes dynamically and uses improved Ant Colony Optimization.By setting a reasonable tabv range,special update rules of the pheromone and path list memory functions,the algorithm can have a better convergence,and its feasibility is proved by the experiment in an EV's demonstration operation system.

The research of controller area network on hybrid electrical vehicle

It is of increasing importance to design and implement vehicle networks for transferring information between electrical control units on Hybrid Electrical Vehicle （HEV）. This paper presents a scheme of using Controller Area Network （CAN） technology to realize communication and datasharing between the electrical units on the HEV. The principle and communication protocol of Electrical Control Units （ECU） CAN node are introduced. By considering different sensitivity of the devices to the latency of data transportation, a new design procedure is proposed for the purpose of simplifying network codes and wiring harness, reducing assembly space and weight, improving assembly efficiency, and enhancing fault-diagnose in auto networks.

Modeling the Prospects of Plug-In Electric Buses to Reduce GHG Emissions and Cost While Meeting Route Demands: A Case Study of the “Unitrans” Bus Fleet Serving the Davis, California Urbanized Area

As university campuses look to decrease their greenhouse gas emissions, plug-in electric buses may provide a low carbon alternative to conventionally fossil-powered buses. This study investigates the viability for Unitrans, the bus service for the greater Davis area and the university campus, to replace current compressed natural gas buses with plug-in electric versions. This study presents an inventory of market available electric buses, their associated costs, incentives, and infrastructure concerns, and compares projected energy use, net present cost, and greenhouse gas emissions with their CNG counterparts. ADVISOR vehicle simulation software is used to estimate the energy use of a typical electric bus (New Flyer Xcelsior XE40 300 kW) and compare to the current CNG model (Orion V) along an actual Unitrans route. The model estimates that the selected bus can travel 146 miles on a single charge, with a fuel economy of 1.75 kWh per mile, which meets the service requirements. Results for bus replacement schedules between 5 and 49 in the 12-year analysis period indicate that between 1600 and 22,000 MT of carbon can be avoided. The net present cost analysis indicates that the potential savings from the replacement of a single CNG bus with an electric bus (with available incentives) ranges from $146,000 - $211,000 per bus over its lifetime, depending on infrastructure costs.

基于LabVIEW的电动汽车动力电池管理系统测试平台设计

为更直观地监测动力电池管理系统的实时数据,设计一款基于LabVIEW的电动汽车动力电池管理系统测试平台,可以展示动力电池管理系统的工作状态。该系统采用主流新能源磷酸铁锂动力电池包,总容量80V50Ah一体式电池管理,具有主从通信、外部通信、状态估算、安全管理、充放电管理、控制输出、控制输入、总压检测、绝缘检测、单体电压采集、温度采集等功能。

商用电动车电池模组热管理研究

文章在已有的研究成果基础上,提出搭建特定模型分析电池模组产热情况的研究思路,并结合有限元仿真计算方法,进一步探究基于不同环境温度条件,电池在不同恒流倍率工况下的热流特性。研究结果表明,根据仿真分析结果所提出的电池模块风道改进方案,可以有效降低电池模组最高温度,同时解决内部温度分布不均匀问题,能为后续优化商用电动车电池设计提供参考。

BMS电路板多模块协同热-力耦合建模及仿真分析

针对现有电动汽车电池管理系统(BMS)电路板研究仅仅考虑单一功能模块的温度场及其散热效果,缺乏考虑BMS多个功能模块在温度场和力场下的相互影响及协同效应的研究的情况,以某商用BMS电路板为研究对象,采用ANSYS构建了表征BMS多模块协同作用下的热-力耦合数值仿真分析模型并验证了其有效性.在此基础上,针对BMS电路板各功能模块温度场及热变形行为开展了数值仿真研究.结果表明:BMS电路板温度分布不均,最大温差达20.5℃.均衡模块存在积热,温度高达54.4℃.高温导致电路板组件发生热膨胀变形,同时电路板约束诱发均衡模块及供电模块边缘的贴片电阻出现热应力集中,两者共同作用致使BMS均衡模块、供电模块产生凸起翘曲变形,且Z轴热变形量随着温度升高而增大,最大变形量达9.5μm,应针对BMS电路板上积热模块开展散热优化设计.

Exergy Analysis of Electric Vehicle Heat Pump Air Conditioning System with Battery Thermal Management System

The exergy analysis of an electric vehicle heat pump air conditioning system(HPACS) with battery thermal management system was carried out by studying the exergy loss of each component. The results indicate that the compressor is the main source of system exergy loss in all operation conditions. The exergy loss distribution of HPACS is almost the same when the battery thermal management system integrated into the HPACS in cabin and battery mixed cooling mode and the system exergy loss was linearly related to the compressor speed in cooling modes. The performance of the HPACS is better than that of the positive temperature coefficient(PTC) heater in cabin heating mode. The degree of exergy efficiency improvement of the alternative mode was discussed at all operation conditions in cabin heating mode. The results indicate that the optimization effect using the electric vehicle HPACS to replace the PTC heater is obvious at lower compressor speed, surrounding temperature and internal condenser air flow rate.

Modeling and optimization of an enhanced battery thermal management system in electric vehicles

This paper models and optimizes an air-based battery thermal management system(BTMS)in a battery module with 36 battery lithium-ion cells.A design of experiments is performed to study the effects of three key parameters(i.e.,mass flow rate of cooling air,heat flux from the battery cell to the cooling air,and passage spacing size)on the battery thermal performance.Three metrics are used to evaluate the BTMS thermal performance,including(i)the maximum temperature in the battery module,(ii)the temperature uniformity in the battery module,and(iii)the pressure drop.It is found that(i)increasing the total mass flow rate may result in a more non-unifbi*m distribution of the passage mass flow rate among passages,and(ii)a large passage spacing size may worsen the temperature uniformity on the battery walls.Optimization is also performed to optimize the passage spacing size.Results show that the maximum temperature difference of the cooling air in passages is reduced from 23.9 K to 2.1 K by 91.2%,and the maximum temperature difference among the battery cells is reduced from 25.7 K to 6.4 K by 75.1%.