纯电动汽车动力总成系统匹配技术研究
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摘要
纯电动汽车具有高效、节能、终端零排放等特点,是解决能源危机和环境污染的重要途径。但电动汽车受电池能量密度和驱动系统效率的限制,续驶里程短,充电时间长,制约了纯电动汽车的推广应用。因此,对动力总成系统关键部件进行选型和匹配,确保这些部件高效区域与电动汽车频繁运行区域之间的合理匹配,并开发合适的控制策略,能够提高车辆驱动系统工作效率,有效延长纯电动汽车的续驶里程。
围绕纯电动汽车动力总成系统的匹配技术,本文开展了以下研究工作：
1.纯电动汽车动力总成系统性能测试试验台开发
为测试纯电动汽车动力总成关键部件及动力总成系统的性能,评价动力总成系统的匹配效果,验证电力驱动系统各控制单元的有效性,建立了由电源系统、驱动电机系统、测功机系统及数据采集控制系统构成的纯电动汽车动力总成系统性能测试试验台；试验台集成的各设备分别采用了CAN总线、485总线或232总线等不同的通信方式,为实现试验台数据的集中采集及对试验台各设备的远程控制,以英飞凌XC164CM单片机为核心,开发了基于CAN总线的信息采集及通信方式转换信息单元,将各设备通信方式统一转化为CAN总线通信方式,构建了试验台CAN总线通信网络；根据试验台所要实现的功能,参考SAEJ1939协议,对试验台各CAN节点源地址进行了分配,并定义了各节点的CAN报文内容,制订了试验台CAN通信网络应用层协议,构建了试验台数据采集及控制系统的基本结构框架,实现了所需的通讯、控制功能。论文以智能型放电仪为例,对数据采集及控制过程的实现方法进行了详细描述,并讨论了试验台的报警及保护机制。动力电池组的放电试验和基本城市循环工况下动力总成系统性能的测试结果表明,开发的试验台实现了纯电动汽车动力总成系统测试所需的功能,达到了设计要求。
2.纯电动汽车动力总成关键部件特性分析
对车辆动力总成系统进行优化匹配和控制策略开发时,需充分了解动力电池、驱动电机等关键部件的效率特性。为此,在试验台上,以320V/100A·h磷酸铁锂电池组为研究对象,对电池组开路电压、容量效率及电压性效率等特性进行了测试研究,结果表明磷酸铁锂电池组在不同充、放电电流下的容量效率达99%以上；电压性效率随电池组工作电流和SOC而变化,电池组在充电电流较小和SOC处于20%-80%时充电效率较高,达92%以上；在放电电流较低且SOC较高时,电池组放电效率较高。基于试验数据构建了电池组充、放电效率模型,用以描述电池组效率与充、放电电流及SOC之间的关系,利用实车测试的电池组工作电流对建立的电池组效率模型进行了验证,结果表明,模型计算值与实测值的最大相对误差为0.57%,表明建立的模型是有效的。
以32kW交流异步电机为研究对象,在试验台上对驱动电机系统常用工况范围和高速弱磁范围内的效率特性进行了测试分析。指出,在不同工况点,电机系统效率相差很大,在低速或低负荷时电机系统效率很低；在电机输出功率0.3Pe≤P≤1.4Pe的中等转速及中等转矩区域内效率较高,维持适当的电机负荷率可显著提高电机系统运行效率；在电机输出功率存在较大过载时,电机系统效率急剧降低。基于实测数据构建了驱动电机系统效率模型；利用驱动电机额定转矩下部分工况点的实测数据对模型进行了验证,结果表明：模型计算值与实测值的最大相对误差为3.4%,建立的模型是有效的。
对电力驱动系统的能量回馈效率特性和驱动效率特性进行了测试分析,结果表明,电力驱动系统高效区域主要集中在电机额定转速附近的中等负荷区域。基于实测数据构建了电力驱动系统能量回馈和驱动效率模型,并通过台架试验验证了模型的有效性。
3.济南市道路工况下车辆动力系统运行区域测试分析
不同城市的车辆行驶工况具有不同的特点,通过构建济南市车辆行驶工况,统计得到车辆实际行驶过程中电力驱动系统常用工作区域,可为动力总成系统匹配设计以及控制策略的优化开发提供依据。本文开发了车载信息单元,通过车辆CAN总线获取车辆实时运行数据,并将有效数据打包,通过GPRS远程无线通信网络发送至监控中心,实现车辆运行信息的实时采集。考虑车道数量、道路坡度及车流密度等因素,选择了济南市典型道路,利用纯电动微型客车连续进行了15天的数据采集,获得了260万条有效数据。本文提出了基于车辆能耗状态构建济南市道路行驶工况的思路,对道路坡度、瞬时比功率、车速及车辆加速度等反映车辆能耗状态的关键因素进行了分析,定义了27个参数反映运动学片段特征；运用主成分分析、快速聚类分析等方法,构建出候选工况,并综合考虑相关系数、相对误差及关键参数概率分布,选出了代表性行驶工况,即济南市车辆行驶工况。通过对济南市车辆行驶工况的统计分析,得到车辆行驶工况点主要集中在车速为10km/h～40km/h、车轮转矩为-200N·m～300N.m、需求功率为-2kW～3.5kW的区域内。
4.动力总成系统软件在环仿真分析
开发了纯电动汽车动力总成系统软件在环仿真系统,用于进行动力总成系统参数匹配研究。以MATLAB/Simulink为基础,搭建了包括道路工况描述模块、车辆行驶动力学模块、整车控制器模块、动力总成关键部件选型模块、驱动电机模块、电机控制器模块以及动力电池组模块在内的动力总成系统在环仿真系统。仿真结果与试验结果以及与ADVISOR仿真结果的对比表明,建立的软件在环仿真系统是有效的。基于所建立的仿真系统,结合台架试验和底盘测功机试验,对一辆纯电动轿车动力总成系统中电池组、电机及传动系统参数进行了选型匹配,实现了电力驱动系统高效区域与车辆实际道路行驶工况点密集区域相吻合。对匹配额定功率7.5kW电机,192V/100A-h磷酸铁锂电池组,传动比6.18的车辆实测结果表明,车辆40km/h匀速行驶时的续驶里程达169km；在基本城市循环工况下百公里能耗为12.01kW.h,续驶里程达160km。
5.电力驱动系统控制单元及控制策略开发
基于Infineon TC1782F微控制器和Hybrid PACK1功率模块开发了电机控制单元,并基于矢量控制算法开发了电机控制策略,控制策略包括坐标变换、转子磁通角计算、电压空间矢量扇区定位、电压矢量作用时间计算等模块。针对纯电动汽车用驱动电机的特点,分析了电机控制器直流母线电压波动、电机温升引起的转子电阻变化、电机高速弱磁控制、转速控制环的PI参数整定及供电电源电压和放电电流对电机系统性能的影响规律,并在试验台上通过转矩动态响应试验和电机转速闭环控制试验,验证了电机控制系统的有效性。
对车辆运行模式进行了划分,并利用Matlab软件中的Simulink、Stateflow建立了驱动模式识别和转换控制模型。设计开发了纯电动汽车驱动控制策略,对加速踏板信号进行了抗干扰、防抖动及滤波处理；车辆在稳态模式下,采用基于车速偏差的增量式PID控制；在瞬态模式下,按照效率最优路径进行控制；在失效模式下,限制电机输出功率。为了最大限度地提高驱动系统效率,提出了基于动力总成系统效率模型实现车辆变工况下转矩轨迹最优的控制策略。模型仿真分析和实车测试结果表明,开发的驱动控制策略是有效的。
在试验台上,以交流异步驱动电机及LiFePO4/C锂离子电池组为研究对象,测试分析了电机转速、制动转矩、电池组SOC及电池组温度对能量回馈效率的影响规律；讨论了电机温度对能量回馈最大制动转矩的限制：针对滑行能量回馈,开发了基于动态矩阵预测控制算法的滑行能量回馈控制策略,参考传统车辆滑行时发动机产生的阻力和电动汽车能量回馈效率模型,确定滑行能量回馈时电机制动转矩参考轨迹,在确保司机驾驶舒适性的前提下,有效回收车辆滑行时的能量；制动能量回馈时,考虑驱动电机最大制动转矩的限制,基于滑动率合理分配机械制动力和电机制动力,确保车辆制动安全性。
实测结果表明,纯电动汽车行驶过程中,驾驶特性对车辆能耗的影响很大。利用济南市区实际运行的纯电动物流用车,对比分析了不同司机驾车行驶时的能耗及其影响因素；对车辆加速度、车速、制动减速度及电机过载特性等对车辆能耗的影响进行了测试分析；在保证车辆性能指标的前提下,通过增加电机极限参数控制模式降低了车辆能耗对驾驶特性的敏感度。试验结果表明,优化后车辆的能耗较原车最高可降低34.9%。
6.匹配车辆性能的试验验证
对匹配开发的车辆进行了底盘测功机试验和实车道路验证试验。在底盘测功机上的测试结果表明,车辆最高车速满足设计指标ua>80km/h,城市工况下的百公里能耗为10.71kW.h,续驶里程为177km。实车道路试验表明,转矩限值为120N-m时,车辆0-60km/h加速时间为10.88s,满足车辆设计指标要求。对驱动模式管理系统功能测试结果表明,车辆运行模式识别准确,模式间切换平稳,整车控制策略达到了预期的效果。在底盘测功机上对动力总成系统安全保护功能进行了测试,结果表明,电池管理系统和电机控制器能根据设定的极限参数对动力总成系统关键部件进行有效保护。
With the advantages of high-energy efficiency and no tail emission pollution, an electric vehicle (EV) is an important way of resolving energy crisis and environmental pollution.Inadequate endurance mileage and long charging period are the main restriction factors to hinder the development of EV due to the poor energy density of battery and low efficiency of electric drive system. In order to improve the efficiency of EV drive system and prolong EV endurance mileage, the reasonable match of battery pack, driving motor and transmission system parameters of power-train should be carried out to suit for EV most common driving conditions, and proper control strategy should be developed.
The main works around the matching techniques of EV power-train are described in the following sections.
1. Development of test bench for the performance testing of power-train
A test bench for the performance testing of power train system of EV has been developed. The bench consisted of motor power supply and battery energy consumption system, driving motor and its controller, dynamometer and its control system as well as bench's control and data acquisition system. The characteristic test for key parts of the power-train system, the performance test and matching effect evaluation for the power-train system and electronic control units' validity verification of EV power-train could be conducted using the bench. The communication modes of equipments with that test bench constructed include CAN bus communication, RS-485series communication and RS-232series communication. In order to meet the demands of data concentrated acquisition and equipment remote control, a communication mode changing system has been developed for data sharing between different communication modes using processor infineon XC164CM and the test bench communication network is constructed based on CAN bus. To realize the communication functions of the test bench, the application layer protocol of CAN bus has been designed, the nodes' source address and parameter groups are all defined clearly according to the protocol of SAE J1939. The functions of data acquisition and equipments' control have been achieved by means of communication network. The control program flow of equipment control and data acquisition has been discussed with the example of battery discharge process through intelligent battery discharge unit, and the protection strategy of the test bench is also discribed.
Tests, such as battery pack discharging and power train performance following the basic urban test cycle, are carried out at the bench, and the results show that the test bench works well and satisfies the requirements for EV studying.
2. Characteristic analysis of key components of EV power-train
To guide EV power-train's optimal matching and developing suitable control strategy, the efficiency characteristics of battery pack, driving motor and other key parts of power-train should be understood. The coloumbic efficiency, the open circuit voltage and the voltaic efficiency of a320V/100A·h LiFePO4/C li-ion battery pack are investigated using the test bench. The results indicate that the coloumbic efficiency exceeds99%for different charge current and discharge current, the voltaic efficiency changes with the battery pack's current and its state of charge (SOC). The battery has a high charging efficiency of over92%at small charge current and in the range of20%-80%of SOC, and a high discharging efficiency with small discharge current and high SOC. A model that descibes the relationship of the battery charge and discharge efficiency to current and SOC is established based on the measured data. The comparisons between modeled results and measured values indicate that the model is valid and the maximum relative error is within0.57%at some typical points which are selected according to vehicle opretion conditions.
The efficiency characteristics of a32kW AC asynchronous motor during the high-frequency operation time area and the field-weakening region are tested and analyzed using the test bench. The test results show that the efficiency of a traction motor varies with the motor speed and torque. There is an area in which the efficiency is high and the most efficient region locates in the middle of the speed and torque range, corresponding to the power output between0.3and1.4of rated power(Pe). Contrast to the high efficiency region, lower power output at low-speed or light load leads to low efficiency, and the efficiency will also decrease significantly when the load power is much greater than the rated power. A model to describe the efficiency of the motor system is established using quartic function based on the test results and the model is validated by comparing measured results that are obtained at the rated torque of the motor to the simulation values, and the results show that the maximum relative error is within3.4%.
The efficiency characteristics of electric driving system under driving and braking energy recovering process are tested and results indicate that there is an optimal efficiency region nearby the rated speed and middle torque. Prediction models of energy recovering efficiency and driving efficiency are developed based on measured data and the models' validity are verified by testing results.
3. Test and analysis of power-train running conditions in Jinan's drive cycle
The driving cycle has specific characteristics in different city. To master the characteristics that in Jinan, a driving cycle of Jinan is constructed, and according to that the ordinary EV running conditions are contoured. The running conditions are helpful to optimally match power train and develop proper control strategy. An on-vehicle data acquisition unit is developed to acquire vehicle real-time running data, and the valid data was packed and transmitted to monitoring center based on GPRS network. Typical roads in Jinan are selected considering lane number, road grade, traffic flow density and other factors, and then the running data of pure electric microbuses driving in the roads is acquired for up to15days and2.6million groups data are collected. A driving cycle constructing method is proposed in which vehicle energy consumption status is included. Key factors that affected vehicle's energy consumption, such as road grade, vehicle transient specific power, vehicle speed, and vehicle acceleration rate are analyzed. Taking vehicle's energy consumption and road grade into consideration,27characteristic values of kinematic sequences are proposed. Candidate driving cycles are constructed according to the analysis result of kinematic sequences by using tools such as principal composition analysis and clustering analysis etc. And the actual driving cycle for Jinan is selected from candidate driving cycles by comprehensive consideration of the correlation coefficient, relative error and key parameters probability distribution. By statistical analysis for Jinan's driving cycle, the area of the greatest operation time is within the vehicle speed of10km/h～40km/h, wheel torque demands of-200N·m～300N·m and power demands of-2kW～3.5kW.
4. Research on software in the Loop simulation technology for EV power-train
Software in the Loop simulation system for EV power-train is developed for the purpose of matching the parameters of power-train. And the simulating system is established using MATLAB/simulink, which includ driving cycle model, vehicle kinematics model, vehicle controller model, traction motor model, motor controller model, battery pack model and selection model which used to select key parts of power-train. The simulating system is validated by comparing the modeled value with the measured data and by comparing the modeled value with the result that are obtained from simulation model developed using ADVISOR software. The parameters of a motor, a battery pack and a manual transmission are selected for a power-train by simulation using the simulating system. The efficiency characteristics of the traction motor and the battery pack are measured on the EV test bench. The electrical driving system and the mechanical transmission system are matched using a chassis dynamometer and the results show that the most efficient region of the power train overlaps the greatest operation time area. With a7.5kW AC asynchronous motor, a192V/100Ah LiFePO4/C battery pack and a constant gear ratio6.18, the EV endurance mileage reaches169km under40km/h testing by constant speed metering method, and reaches160km, costing12.01kW·h/100km, according to basic urban driving cycle.
5. Development of controller and control strategy for electric driving system
A motor controller unit is designed using Infineon TC1782F microcontroller and Hybrid PACK1IGBT module, and the motor control strategy is developed based on the vector control. And in the strategy, coordinate transformation module, rotor flux angle calculation module, voltage space vector location calculation module and basic voltage vector acting time calculation module are included. In real-word operation, some factors that affect EV drive performance, such as DC voltage ripple, rotor resistance varying with motor temperature, field weakening control at high-speed range, proportional-integral (PI) parameters tuning in the motor speed control loop, voltage supply and discharge current of the power system are analyzed. Tests about motor torque response to a step change in the torque demand and closed loop speed control performance are carried out and the results show that the motor controller could work effectively.
EV's driving modes are established, mode identifying and mode switching pattern are constructed using Matlab/simulink&stateflow. The driving control strategy of EV's power-train is developed, in which the signal of accelerator pedal is smoothed and filtered; the incremental PID control based on speed deviation and the optimal efficiency path control are adopted in steady mode and transient mode respectively; power output is limited in failure mode. Under transient conditions, in order to further improve power-train's efficiency, an optimizing power-train's control strategy is proposed based on the power-train efficiency model. Simulating results and testing results indicate that the control strategy is valid.
With the tests of AC asynchronous motor and a LiFePO4/C battery pack on the test bench, the relationship of energy recovery efficiency to motor speed, brake torque, battery pack's SOC, battery pack's temperature is studied. The restriction of motor's temperature to the maximum brake torque setting is discussed. For recovering energy effectively, under the premise of driving comfort, the dynamic matrix control (DMC) algorithm is adopted to develop the strategy of coasting condition according to energy recovery efficiency model and conventional vehicle's coasting resistance. To ensure vehicle safety, a braking control strategy is designed that distributes the required braking forces among regenerative braking and frictional braking referring to wheel slip rate.
The test results indicate that driver's driving characteristics have great influence on EV energy consumption. The difference of energy consumption driving by different drivers is tested and driving characters of affecting the difference are discussed according to test results of electric vans running in Jinan city. Factors influencing EV energy consumption, such as vehicle acceleration rate, speed, braking deceleration rate and motor power overload are tested and analyzed. By adding a control mode, in which motor torque and over-speed are limited, and therefore the driving parameters to that energy consumption is sensitive are optimized. The test of optimized electric vans is carried out, and results indicated that energy consumption of optimized vans could be decreased as more as34.9%when compared with the original vehicle.
6. Performance examination of matched EV
Chassis dynamometer test and vehicle road test are carried out using the matched EV. Results of chassis dynamometer tests show that the vehicle maximum speed satisfies the EV's design index requirement of umax>80km/h, and the endurance mileage reaches177km, costing10.71kW·h/100km, over the urban driving cycle. The vehicle test indicates that time used to accelerate the vehicle from zero speed to60km/h is10.88s when the motor torque is limited to the range less than120N-m, and the time also meet the demands of vehicle acceleration performance. The test results about driving modes'management strategy show that the operation mode can identify operation status accurately, operation mode can switch smoothly and the control strategy works well. The safety protection function for power-train is tested on chassis dynamometer and the results indicate that the battery management system and the motor controller can provide effective protection for the key components of power-train according to user's settings.
围绕纯电动汽车动力总成系统的匹配技术,本文开展了以下研究工作：
1.纯电动汽车动力总成系统性能测试试验台开发
为测试纯电动汽车动力总成关键部件及动力总成系统的性能,评价动力总成系统的匹配效果,验证电力驱动系统各控制单元的有效性,建立了由电源系统、驱动电机系统、测功机系统及数据采集控制系统构成的纯电动汽车动力总成系统性能测试试验台；试验台集成的各设备分别采用了CAN总线、485总线或232总线等不同的通信方式,为实现试验台数据的集中采集及对试验台各设备的远程控制,以英飞凌XC164CM单片机为核心,开发了基于CAN总线的信息采集及通信方式转换信息单元,将各设备通信方式统一转化为CAN总线通信方式,构建了试验台CAN总线通信网络；根据试验台所要实现的功能,参考SAEJ1939协议,对试验台各CAN节点源地址进行了分配,并定义了各节点的CAN报文内容,制订了试验台CAN通信网络应用层协议,构建了试验台数据采集及控制系统的基本结构框架,实现了所需的通讯、控制功能。论文以智能型放电仪为例,对数据采集及控制过程的实现方法进行了详细描述,并讨论了试验台的报警及保护机制。动力电池组的放电试验和基本城市循环工况下动力总成系统性能的测试结果表明,开发的试验台实现了纯电动汽车动力总成系统测试所需的功能,达到了设计要求。
2.纯电动汽车动力总成关键部件特性分析
对车辆动力总成系统进行优化匹配和控制策略开发时,需充分了解动力电池、驱动电机等关键部件的效率特性。为此,在试验台上,以320V/100A·h磷酸铁锂电池组为研究对象,对电池组开路电压、容量效率及电压性效率等特性进行了测试研究,结果表明磷酸铁锂电池组在不同充、放电电流下的容量效率达99%以上；电压性效率随电池组工作电流和SOC而变化,电池组在充电电流较小和SOC处于20%-80%时充电效率较高,达92%以上；在放电电流较低且SOC较高时,电池组放电效率较高。基于试验数据构建了电池组充、放电效率模型,用以描述电池组效率与充、放电电流及SOC之间的关系,利用实车测试的电池组工作电流对建立的电池组效率模型进行了验证,结果表明,模型计算值与实测值的最大相对误差为0.57%,表明建立的模型是有效的。
以32kW交流异步电机为研究对象,在试验台上对驱动电机系统常用工况范围和高速弱磁范围内的效率特性进行了测试分析。指出,在不同工况点,电机系统效率相差很大,在低速或低负荷时电机系统效率很低；在电机输出功率0.3Pe≤P≤1.4Pe的中等转速及中等转矩区域内效率较高,维持适当的电机负荷率可显著提高电机系统运行效率；在电机输出功率存在较大过载时,电机系统效率急剧降低。基于实测数据构建了驱动电机系统效率模型；利用驱动电机额定转矩下部分工况点的实测数据对模型进行了验证,结果表明：模型计算值与实测值的最大相对误差为3.4%,建立的模型是有效的。
对电力驱动系统的能量回馈效率特性和驱动效率特性进行了测试分析,结果表明,电力驱动系统高效区域主要集中在电机额定转速附近的中等负荷区域。基于实测数据构建了电力驱动系统能量回馈和驱动效率模型,并通过台架试验验证了模型的有效性。
3.济南市道路工况下车辆动力系统运行区域测试分析
不同城市的车辆行驶工况具有不同的特点,通过构建济南市车辆行驶工况,统计得到车辆实际行驶过程中电力驱动系统常用工作区域,可为动力总成系统匹配设计以及控制策略的优化开发提供依据。本文开发了车载信息单元,通过车辆CAN总线获取车辆实时运行数据,并将有效数据打包,通过GPRS远程无线通信网络发送至监控中心,实现车辆运行信息的实时采集。考虑车道数量、道路坡度及车流密度等因素,选择了济南市典型道路,利用纯电动微型客车连续进行了15天的数据采集,获得了260万条有效数据。本文提出了基于车辆能耗状态构建济南市道路行驶工况的思路,对道路坡度、瞬时比功率、车速及车辆加速度等反映车辆能耗状态的关键因素进行了分析,定义了27个参数反映运动学片段特征；运用主成分分析、快速聚类分析等方法,构建出候选工况,并综合考虑相关系数、相对误差及关键参数概率分布,选出了代表性行驶工况,即济南市车辆行驶工况。通过对济南市车辆行驶工况的统计分析,得到车辆行驶工况点主要集中在车速为10km/h～40km/h、车轮转矩为-200N·m～300N.m、需求功率为-2kW～3.5kW的区域内。
4.动力总成系统软件在环仿真分析
开发了纯电动汽车动力总成系统软件在环仿真系统,用于进行动力总成系统参数匹配研究。以MATLAB/Simulink为基础,搭建了包括道路工况描述模块、车辆行驶动力学模块、整车控制器模块、动力总成关键部件选型模块、驱动电机模块、电机控制器模块以及动力电池组模块在内的动力总成系统在环仿真系统。仿真结果与试验结果以及与ADVISOR仿真结果的对比表明,建立的软件在环仿真系统是有效的。基于所建立的仿真系统,结合台架试验和底盘测功机试验,对一辆纯电动轿车动力总成系统中电池组、电机及传动系统参数进行了选型匹配,实现了电力驱动系统高效区域与车辆实际道路行驶工况点密集区域相吻合。对匹配额定功率7.5kW电机,192V/100A-h磷酸铁锂电池组,传动比6.18的车辆实测结果表明,车辆40km/h匀速行驶时的续驶里程达169km；在基本城市循环工况下百公里能耗为12.01kW.h,续驶里程达160km。
5.电力驱动系统控制单元及控制策略开发
基于Infineon TC1782F微控制器和Hybrid PACK1功率模块开发了电机控制单元,并基于矢量控制算法开发了电机控制策略,控制策略包括坐标变换、转子磁通角计算、电压空间矢量扇区定位、电压矢量作用时间计算等模块。针对纯电动汽车用驱动电机的特点,分析了电机控制器直流母线电压波动、电机温升引起的转子电阻变化、电机高速弱磁控制、转速控制环的PI参数整定及供电电源电压和放电电流对电机系统性能的影响规律,并在试验台上通过转矩动态响应试验和电机转速闭环控制试验,验证了电机控制系统的有效性。
对车辆运行模式进行了划分,并利用Matlab软件中的Simulink、Stateflow建立了驱动模式识别和转换控制模型。设计开发了纯电动汽车驱动控制策略,对加速踏板信号进行了抗干扰、防抖动及滤波处理；车辆在稳态模式下,采用基于车速偏差的增量式PID控制；在瞬态模式下,按照效率最优路径进行控制；在失效模式下,限制电机输出功率。为了最大限度地提高驱动系统效率,提出了基于动力总成系统效率模型实现车辆变工况下转矩轨迹最优的控制策略。模型仿真分析和实车测试结果表明,开发的驱动控制策略是有效的。
在试验台上,以交流异步驱动电机及LiFePO4/C锂离子电池组为研究对象,测试分析了电机转速、制动转矩、电池组SOC及电池组温度对能量回馈效率的影响规律；讨论了电机温度对能量回馈最大制动转矩的限制：针对滑行能量回馈,开发了基于动态矩阵预测控制算法的滑行能量回馈控制策略,参考传统车辆滑行时发动机产生的阻力和电动汽车能量回馈效率模型,确定滑行能量回馈时电机制动转矩参考轨迹,在确保司机驾驶舒适性的前提下,有效回收车辆滑行时的能量；制动能量回馈时,考虑驱动电机最大制动转矩的限制,基于滑动率合理分配机械制动力和电机制动力,确保车辆制动安全性。
实测结果表明,纯电动汽车行驶过程中,驾驶特性对车辆能耗的影响很大。利用济南市区实际运行的纯电动物流用车,对比分析了不同司机驾车行驶时的能耗及其影响因素；对车辆加速度、车速、制动减速度及电机过载特性等对车辆能耗的影响进行了测试分析；在保证车辆性能指标的前提下,通过增加电机极限参数控制模式降低了车辆能耗对驾驶特性的敏感度。试验结果表明,优化后车辆的能耗较原车最高可降低34.9%。
6.匹配车辆性能的试验验证
对匹配开发的车辆进行了底盘测功机试验和实车道路验证试验。在底盘测功机上的测试结果表明,车辆最高车速满足设计指标ua>80km/h,城市工况下的百公里能耗为10.71kW.h,续驶里程为177km。实车道路试验表明,转矩限值为120N-m时,车辆0-60km/h加速时间为10.88s,满足车辆设计指标要求。对驱动模式管理系统功能测试结果表明,车辆运行模式识别准确,模式间切换平稳,整车控制策略达到了预期的效果。在底盘测功机上对动力总成系统安全保护功能进行了测试,结果表明,电池管理系统和电机控制器能根据设定的极限参数对动力总成系统关键部件进行有效保护。
With the advantages of high-energy efficiency and no tail emission pollution, an electric vehicle (EV) is an important way of resolving energy crisis and environmental pollution.Inadequate endurance mileage and long charging period are the main restriction factors to hinder the development of EV due to the poor energy density of battery and low efficiency of electric drive system. In order to improve the efficiency of EV drive system and prolong EV endurance mileage, the reasonable match of battery pack, driving motor and transmission system parameters of power-train should be carried out to suit for EV most common driving conditions, and proper control strategy should be developed.
The main works around the matching techniques of EV power-train are described in the following sections.
1. Development of test bench for the performance testing of power-train
A test bench for the performance testing of power train system of EV has been developed. The bench consisted of motor power supply and battery energy consumption system, driving motor and its controller, dynamometer and its control system as well as bench's control and data acquisition system. The characteristic test for key parts of the power-train system, the performance test and matching effect evaluation for the power-train system and electronic control units' validity verification of EV power-train could be conducted using the bench. The communication modes of equipments with that test bench constructed include CAN bus communication, RS-485series communication and RS-232series communication. In order to meet the demands of data concentrated acquisition and equipment remote control, a communication mode changing system has been developed for data sharing between different communication modes using processor infineon XC164CM and the test bench communication network is constructed based on CAN bus. To realize the communication functions of the test bench, the application layer protocol of CAN bus has been designed, the nodes' source address and parameter groups are all defined clearly according to the protocol of SAE J1939. The functions of data acquisition and equipments' control have been achieved by means of communication network. The control program flow of equipment control and data acquisition has been discussed with the example of battery discharge process through intelligent battery discharge unit, and the protection strategy of the test bench is also discribed.
Tests, such as battery pack discharging and power train performance following the basic urban test cycle, are carried out at the bench, and the results show that the test bench works well and satisfies the requirements for EV studying.
2. Characteristic analysis of key components of EV power-train
To guide EV power-train's optimal matching and developing suitable control strategy, the efficiency characteristics of battery pack, driving motor and other key parts of power-train should be understood. The coloumbic efficiency, the open circuit voltage and the voltaic efficiency of a320V/100A·h LiFePO4/C li-ion battery pack are investigated using the test bench. The results indicate that the coloumbic efficiency exceeds99%for different charge current and discharge current, the voltaic efficiency changes with the battery pack's current and its state of charge (SOC). The battery has a high charging efficiency of over92%at small charge current and in the range of20%-80%of SOC, and a high discharging efficiency with small discharge current and high SOC. A model that descibes the relationship of the battery charge and discharge efficiency to current and SOC is established based on the measured data. The comparisons between modeled results and measured values indicate that the model is valid and the maximum relative error is within0.57%at some typical points which are selected according to vehicle opretion conditions.
The efficiency characteristics of a32kW AC asynchronous motor during the high-frequency operation time area and the field-weakening region are tested and analyzed using the test bench. The test results show that the efficiency of a traction motor varies with the motor speed and torque. There is an area in which the efficiency is high and the most efficient region locates in the middle of the speed and torque range, corresponding to the power output between0.3and1.4of rated power(Pe). Contrast to the high efficiency region, lower power output at low-speed or light load leads to low efficiency, and the efficiency will also decrease significantly when the load power is much greater than the rated power. A model to describe the efficiency of the motor system is established using quartic function based on the test results and the model is validated by comparing measured results that are obtained at the rated torque of the motor to the simulation values, and the results show that the maximum relative error is within3.4%.
The efficiency characteristics of electric driving system under driving and braking energy recovering process are tested and results indicate that there is an optimal efficiency region nearby the rated speed and middle torque. Prediction models of energy recovering efficiency and driving efficiency are developed based on measured data and the models' validity are verified by testing results.
3. Test and analysis of power-train running conditions in Jinan's drive cycle
The driving cycle has specific characteristics in different city. To master the characteristics that in Jinan, a driving cycle of Jinan is constructed, and according to that the ordinary EV running conditions are contoured. The running conditions are helpful to optimally match power train and develop proper control strategy. An on-vehicle data acquisition unit is developed to acquire vehicle real-time running data, and the valid data was packed and transmitted to monitoring center based on GPRS network. Typical roads in Jinan are selected considering lane number, road grade, traffic flow density and other factors, and then the running data of pure electric microbuses driving in the roads is acquired for up to15days and2.6million groups data are collected. A driving cycle constructing method is proposed in which vehicle energy consumption status is included. Key factors that affected vehicle's energy consumption, such as road grade, vehicle transient specific power, vehicle speed, and vehicle acceleration rate are analyzed. Taking vehicle's energy consumption and road grade into consideration,27characteristic values of kinematic sequences are proposed. Candidate driving cycles are constructed according to the analysis result of kinematic sequences by using tools such as principal composition analysis and clustering analysis etc. And the actual driving cycle for Jinan is selected from candidate driving cycles by comprehensive consideration of the correlation coefficient, relative error and key parameters probability distribution. By statistical analysis for Jinan's driving cycle, the area of the greatest operation time is within the vehicle speed of10km/h～40km/h, wheel torque demands of-200N·m～300N·m and power demands of-2kW～3.5kW.
4. Research on software in the Loop simulation technology for EV power-train
Software in the Loop simulation system for EV power-train is developed for the purpose of matching the parameters of power-train. And the simulating system is established using MATLAB/simulink, which includ driving cycle model, vehicle kinematics model, vehicle controller model, traction motor model, motor controller model, battery pack model and selection model which used to select key parts of power-train. The simulating system is validated by comparing the modeled value with the measured data and by comparing the modeled value with the result that are obtained from simulation model developed using ADVISOR software. The parameters of a motor, a battery pack and a manual transmission are selected for a power-train by simulation using the simulating system. The efficiency characteristics of the traction motor and the battery pack are measured on the EV test bench. The electrical driving system and the mechanical transmission system are matched using a chassis dynamometer and the results show that the most efficient region of the power train overlaps the greatest operation time area. With a7.5kW AC asynchronous motor, a192V/100Ah LiFePO4/C battery pack and a constant gear ratio6.18, the EV endurance mileage reaches169km under40km/h testing by constant speed metering method, and reaches160km, costing12.01kW·h/100km, according to basic urban driving cycle.
5. Development of controller and control strategy for electric driving system
A motor controller unit is designed using Infineon TC1782F microcontroller and Hybrid PACK1IGBT module, and the motor control strategy is developed based on the vector control. And in the strategy, coordinate transformation module, rotor flux angle calculation module, voltage space vector location calculation module and basic voltage vector acting time calculation module are included. In real-word operation, some factors that affect EV drive performance, such as DC voltage ripple, rotor resistance varying with motor temperature, field weakening control at high-speed range, proportional-integral (PI) parameters tuning in the motor speed control loop, voltage supply and discharge current of the power system are analyzed. Tests about motor torque response to a step change in the torque demand and closed loop speed control performance are carried out and the results show that the motor controller could work effectively.
EV's driving modes are established, mode identifying and mode switching pattern are constructed using Matlab/simulink&stateflow. The driving control strategy of EV's power-train is developed, in which the signal of accelerator pedal is smoothed and filtered; the incremental PID control based on speed deviation and the optimal efficiency path control are adopted in steady mode and transient mode respectively; power output is limited in failure mode. Under transient conditions, in order to further improve power-train's efficiency, an optimizing power-train's control strategy is proposed based on the power-train efficiency model. Simulating results and testing results indicate that the control strategy is valid.
With the tests of AC asynchronous motor and a LiFePO4/C battery pack on the test bench, the relationship of energy recovery efficiency to motor speed, brake torque, battery pack's SOC, battery pack's temperature is studied. The restriction of motor's temperature to the maximum brake torque setting is discussed. For recovering energy effectively, under the premise of driving comfort, the dynamic matrix control (DMC) algorithm is adopted to develop the strategy of coasting condition according to energy recovery efficiency model and conventional vehicle's coasting resistance. To ensure vehicle safety, a braking control strategy is designed that distributes the required braking forces among regenerative braking and frictional braking referring to wheel slip rate.
The test results indicate that driver's driving characteristics have great influence on EV energy consumption. The difference of energy consumption driving by different drivers is tested and driving characters of affecting the difference are discussed according to test results of electric vans running in Jinan city. Factors influencing EV energy consumption, such as vehicle acceleration rate, speed, braking deceleration rate and motor power overload are tested and analyzed. By adding a control mode, in which motor torque and over-speed are limited, and therefore the driving parameters to that energy consumption is sensitive are optimized. The test of optimized electric vans is carried out, and results indicated that energy consumption of optimized vans could be decreased as more as34.9%when compared with the original vehicle.
6. Performance examination of matched EV
Chassis dynamometer test and vehicle road test are carried out using the matched EV. Results of chassis dynamometer tests show that the vehicle maximum speed satisfies the EV's design index requirement of umax>80km/h, and the endurance mileage reaches177km, costing10.71kW·h/100km, over the urban driving cycle. The vehicle test indicates that time used to accelerate the vehicle from zero speed to60km/h is10.88s when the motor torque is limited to the range less than120N-m, and the time also meet the demands of vehicle acceleration performance. The test results about driving modes'management strategy show that the operation mode can identify operation status accurately, operation mode can switch smoothly and the control strategy works well. The safety protection function for power-train is tested on chassis dynamometer and the results indicate that the battery management system and the motor controller can provide effective protection for the key components of power-train according to user's settings.
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