【作者】 王业琴；

【导师】 刘一星；

【作者基本信息】 东北林业大学 ， 林业工程自动化， 2013， 博士

【摘要】 电动汽车续驶里程不足是制约电动汽车产业化发展的主要瓶颈,因此在有限车载能源情况下,提高电动汽车运行能效具重要有意义。尤其是电动城市客车运行在低速、制动频繁的城市工况,能量利用率提升空间更为可观。电动城市客车运行能效关键技术主要涉及：电池SOC的准确估算、驱动电机效率优化控制和再生制动能量回收三方面内容。本文以JXK6121BEV型号电动城市客车为研究对象,在现有电池、电机技术水平基础上,围绕上述三方面内容展开研究,从而实现电动城市客车有限能源的开源节流,旨在提高电动城市客车能量利用率和一次充电续驶里程,主要研究内容如下：实车测量计算得出驱动系统能耗比例为70.8%,通过驱动系统能耗实验,确定采用驱动电机效率优化和再生制动能量回馈两种途径来提高电动城市客车的能量利用效率。在系统分析电动城市客车从电网到车轮的能量传递效率基础上,进行电动城市客车电池组能效分析、驱动系统效率和再生制动能量回馈效率研究,通过动力总成实验台架,对实车运行工况进行驱动电机效率测试,为驱动系统电机效率优化控制提供依据。为了有效管理车载能源,进行了电池管理系统设计,包括结构设计、电控系统设计和软件系统设计。准确估算电池SOC,可以提高动力电池的能量利用效率、延长电池使用寿命,是电池能量管理和制动力分配的关键所在。在电池性能测试基础上,提出采用PSO-BP方法,利用电池电压、充放电倍率和温度作为神经网络输入特征参数,估算电池SOC。仿真实验结果表明,该方法能够实现单体电池SOC10%-90%范围内的准确估算,中国典型城市公交循环工况电池组SOC估算精度为0.7%,实车电池组的SOC估算精度为3.5%,说明该方法能够满足实际需求。为了提高电动城市客车驱动系统运行效率,在异步电机矢量控制基础上,建立基于损耗模型的效率优化控制系统的仿真模型。针对损耗模型法在电机参数变化时效率优化控制精度下降的问题,提出一种混合法电机效率优化控制策略：利用损耗模型确定最优磁链搜索范围,缩短搜索时间,采用广义回归神经网络搜索最优磁链,从而消除电机参数对效率优化的影响,提高控制精度。在MATLAB/Simulink中搭建了两种效率优化控制的仿真模型并进行了仿真实验,给定转速／转矩1003r/min/200N.m情况下,效率优化以前电机效率为62.95%,基于损耗模型优化和混合法优化电机效率分别为84.23%和86.72%。给定转速／转矩1691r/min/619N.m情况下,效率优化以前电机效率为75.86%,基于损耗模型优化和混合法优化电机效率分别为80.44%和81.56%。为了验证效率优化控制策略的实用性,进行了实车运行工况仿真实验,优化以前电机平均效率为59.93%,基于损耗模型优化和混合法优化电机平均效率分别为82.45%和84.92%。仿真实验结果表明,混合法效率优化控制的鲁棒性、实用性和准确性要优于基于损耗模型的效率优化控制。为了回收电动城市客车频繁制动的再生制动能量,进行了制动力分配控制研究。考虑到制动力分配受到车速、制动强度和电池SOC等多因素影响,很难建立准确的数学模型,提出了模糊控制制动力分配策略并利用Simlink搭建了制动力分配仿真模型。利用Cruise软件搭建了电动城市客车模型与MATLAB进行联合仿真。中国典型城市公交循环工况,一个循环行驶5.8km,模糊控制制动力分配策略电池组SOC降低0.376%,电池组90%DOD续驶里程增加10.2km,续驶里程提高7.8%；美国FTP工况(最高车速改为70km/h)一个循环行驶16.84km,电池组SOC降低1.487%,电池组90%DOD续驶里程增加9.8km,续驶里程提高8.84%。实验结果表明,该方法能够有效地提高能量利用效率,最大限度地回收再生制动能量,延长电动城市客车的续驶里程,同时能够有效地防止电池过充电。更多还原

【Abstract】 The endurance mileage deficiency of EV is still the main obstacle which restricts the development of electric vehicle industry, so it is significant to improve the operation energy efficiency of electric vehicle in the case of limited vehicle energy. Especially when the electric city bus run at the city conditions of low speed and frequent brake, energy efficiency promotion space is more impressive.The key technology about operation energy efficiency of electric city bus mainly includes three parts:accurate estimation of battery SOC, efficiency optimization of driving motor and regenerative braking energy, JXK6121BEV type electric city bus is taken as study objects in this thesis, on the present level of battery and driving motor, the research is carried out around the above three contents, so as to realize the limited energy resources tapping and saving, in order to improve the energy utilization efficiency and prolong the endurance mileage of electric city bus, the main research contents are as follows:Calculate from substantial vehicle tests show that energy consumption ratio of the driving system is70.8%, base on the experimental of the energy loss in driving system, two methods including the efficiency optimization of motor drive system and regenerative braking energy are used to improve the energy utilization efficiency of electric city bus. The energy transfer efficiency from the grid to the wheels of electric city bus is analyzed, on this basis, the researches of efficiency in battery energy, efficiency of drive system and regenerative braking are studied. Efficiency test of driving motor is done under real measurement environment on an experiment bench for power train, which provide theoretical basis for efficiency optimization control of driving motor.In order to manage the vehicle power supply effectively, the battery management system including the structure, control system and software system was designed. Accuracy estimation of the battery SOC can improve the energy utilization efficiency of battery and extend the battery life, which is the key to energy management and braking force distribution. PSO-BP method was presented based on the testing of the battery performance, which used the battery voltage, charge-discharge rate and temperature as the input features parameters of neural network to estimate the battery SOC. The simulation results show that the presented method may estimate accurately the single battery SOC in the range of10%～90%. The estimation accuracy of battery packs SOC of the driving cycles of Chinese city buses was0.7%, and the estimation accuracy of the real vehicle battery packs was3.5%, which show that this method can meet the actual requirement.In order to improve the operation efficiency of driving system in electric city bus, the loss model of motor is established based on the vector control of induction motor, and simulation model of efficiency optimization control system based on loss model is established in MATLAB/Simulink. Aiming at drawbacks that efficiency optimization control precision is being dropped while motor parameters change in loss model control strategy, a hybrid method in motor efficiency optimization control is proposed, that is the loss model is used to determine the optimal flux search range, which can shorten search time, and then generalized regression neural network is used to search the optimal flux, which decrease the effect of motor parameters on efficiency optimization and improve the control precision. Comparison simulation experiments of the two efficiency optimization methods are carried out in MATLAB/Simulink, the motor efficiency with non-optimum is62.95%, and the optimized efficiency are respectively84.23%and86.72%based on the loss model optimization and the hybrid method when the given speed and torque are1003r/min/200N.m. and in the condition of1691r/min/619N.m, the non-optimum efficiency is75.86%, and the optimized efficiency are respectively80.44%and81.56%. In order to validate the practicality of efficiency optimization control strategy, the real vehicle condition simulation experiment is done, and the non-optimum efficiency is59.93%, the optimized efficiency are respectively82.45%and84.92%. The simulation experiment results show that the robustness, practicality and accuracy of hybrid method is better than the method based on loss model.In order to recover the energy regenerated in frequent braking, the braking force distribution control is researched. It is difficult to build the actual mathematical model for the brake force distribution which is affected by the speed, braking intensity and battery SOC, The fuzzy control strategy for braking force distribution is developed, and the simulation model of brake force distribution is established in Simulink. The electric city buses model is established by cruise and co-simulated with MATLAB. For the typical driving cycles of city buses in China, the driving distance within a cycle is5.8km, the battery SOC decreased0.376%by the fuzzy control strategy for braking force distribution, the endurance mileage of the90%DOD increased10.2km and it improved by7.8%. For the FTP cycles in American, the driving distance within a cycle was16.84km, the battery SOC decreased by1.487%based on the fuzzy control strategy for braking force distribution, the endurance mileage of the90%DOD increased9.8km and it improved by8.84%. The experiment results show that the method can improve the energy efficiency effectively, recover the regenerative braking energy in maximum scale, extend the endurance mileage of electric city bus and avoid overcharge effectively.更多还原