关 键 词：

电机 四轮驱动 小型 电动汽车 参数 匹配 仿真

资源描述：

双电机四轮驱动型小型纯电动汽车的参数匹配和仿真,电机,四轮驱动,小型,电动汽车,参数,匹配,仿真

内容简介：

2001-01-13342001-01-1334混合动力电动汽车混合动力电动汽车在在SIMULINKSIMULINK仿真及其使用的电力管理研究仿真及其使用的电力管理研究Chan-Chiao Lin, Zoran Filipi, Yongsheng Wang, Loucas Louca,Huei Peng, Dennis Assanis, Jeffrey Stein汽车研究中心汽车研究中心密歇根大学密歇根大学版权学会2001年汽车工程师,Inc文摘文摘 混合动力电动汽车仿真工具(HE-VESIM)汽车研究中心密歇根大学已经学习开发了经济潜力的燃料混合动力车的军事和民用卡车。前馈的基础架构,混合动力车辆系统进行了描述,结合动力学方程和基本特征的子系统模块。两电源管理控制算法进行评估,一个基于规则的算法,主要探讨了引擎的效率以直观的方式,和一个动态编程优化算法。 仿真结果在城市驾驶循环说明可能选择的混合动力系统显著提高汽车燃油经济性,改善被大当动态规划电源管理算法。介绍介绍日益增长的环境问题加上全球原油供应的担忧刺激研究针对新型节能汽车技术。 混合动力汽车(HEV)看起来最可行的技术和巨大的潜力来减少燃油消费在现实经济、 基础设施和客户验收的约束。 许多混合动力汽车的原型/概念已经开发出来。 丰田和本田已经开始生产的车辆和许多其他主要汽车制造商有望推出混合动力汽车在未来3 - 5年。 由于存在双重能源,额外的设计自由度的混合动力汽车提供前所未有的可能性在燃油经济性和废气排放,特别是如果并行动力系统架构是使用。同时,新的车辆系统的复杂性要求应用程序模拟的准确分级和匹配的研究,以及发展的控制算法的前面,最终的设计和物理样机。 大多数的控制策略对并行开发混合动力汽车分为三种类别。第一类应用智能控制技术,如规则/模糊逻辑/ NN评估以及控制算法1,2发展。第二种方法是基于静态优化方法。通常,来计算成本的能量,把电能转换成相当数量的燃料3和4)。优化方案然后找出适当的能量和权力划分两个能源在稳态操作。 由于其相对简单的逐点优化自然,它可以扩展优化方案来解决同步的燃油经济性和排放优化问题5。 基本的想法了第三种类型的混合控制算法相似的静态优化;然而,优化进行了动态系统6。 进一步,优化是有关一个时间范围,而不是一个固定的时间点。 一般来说,权力的分离算法从动态优化会更准确瞬态条件下。 通常,动态优化算法不是由于他们的可实现预览性质和沉重的计算需求。 然而,他们有一个很好的基准测试基于前两个类型的算法还可以改进或对比。 这项工作的目的是开发一个集成的混合动力汽车仿真工具,使用它的设计提供能源管理控制算法。我们的混合动力的基础车用发动机仿真(HE-VESIM)是传统汽车的高保真模拟器 VESIM 以前开发的密歇根大学7。VESIM已经验证 VI 类测量了卡车,并被证明是一个非常通用的工具对移动、燃油经济性和动力性的研究。构建一个混合动力车模拟器,一些主要的模块需要修改,如发动机必须缩小,和电动组件模型需要被创建并集成到系统中。 我们努力将专注于混合模拟并行邮报传播配置,电动机机械耦合到出力轴上。 前馈仿真方案将采用,以便控制策略的研究在现实的瞬态条件。集成的混合仿真实现 SIMULINK 以允许方便地重新配置系统,使设计师选择合适的模型根据特定仿真目标。研究了两种控制算法在本文:一个基于规则和动态规划优化算法。摘要安排如下。配置新开发的混合动力电动汽车系统于SIMULINK仿真是首先讨论,紧随其后的是描述的特征主要仿真模块:柴油发动机、动力传动系统、 车辆动力学和电器元件。 接下来,两个电源管理算法:基于规则的算法,并基于动态规划的优化算法引入了。 完整的混合动力汽车模拟随后被用来评估加速能力和燃料经济的混合动力汽车与传统的同行通过比较。 这两个控制策略是通过模拟预测的燃料消耗量超过一个驾驶循环,紧随其后的是总结和结论。混合动力车辆系统混合动力车辆系统 车辆系统认为工作是一个4 X2 VI类卡车配置为并联混合动力的电动马达定位后的传输。图示的车辆和推进系统是图1中给出。发动机是连接到变矩器(TC),然后输出轴的耦合传输(Trns)。 耦合变速器输出端从事或者分离的电动马达根据操作模式的混合。 因此,传输和电动马达可链接到螺旋桨轴(PS)、微分(D)和两个装置(DS)、耦合差动驱动轮的。图1:集成车辆系统的示意图。完整的车辆系统仿真的结构直接像物理系统的布局。为了有一个高度的灵活性,模拟结构中实现了利用MATLAB / SIMULINK图形软件环境,见图之间的联系主要模块代表了物理参数实际上定义了组件之间的交互,比如轴扭矩和角速度,或电流和电压。 混合动力汽车的电源管理逻辑控制器包含并发送控制信号到组件模块根据反馈有关当前操作条件。 最后,一个“司机”模块允许前馈模拟车辆速度按规定的时间表。智能速度控制器(IVS)满足了这些角色和提供司机需求的信号和制动根据指定的速度设置和最近的车辆速度。图2混合动力汽车在SIMULINK仿真引擎引擎发动机模型源自于高保真、 热力发动机系统之前开发的传统汽车7、11。 高保真的发动机模型是由多个缸模块与外部组件模块歧管,压缩机,发电机、 热交换器、 空气过滤器、 和排气系统元素。 为了支持计算密集型的模拟长循环工况和方便简单的伸缩的引擎,热力学的发动机模型替换为一个查找表,提供制动力矩作为函数的瞬时转速和大规模的燃料注入每气缸/周期。 生成的查找表实际上是使用之前确认的高保真引擎系统代码11,因此它是可能的不同大小的物理引擎,或其设计,有一个真实的描述给定变化的影响。 为平行混合应用程序,最初的V8 7.3升柴油是缩减了气缸的数目减少到6,并因此位移到5.5 L种种地图放大以匹配略小的发动机,下面描述的方法在20。 整个过程产生扭矩查找表基于预测一份有效的高保真引擎系统代码,如图3中所示。规范的V8引擎都为传统汽车和混合应用程序的V6引擎表1中给出的附录。为了留住特征的引擎系统关键的瞬态响应,完整的燃料控制逻辑保留在基于查找表的模型,如图4所示。柴油机燃油喷射控制器提供信号质量的燃料注入每个周期基于司机需求,所提供的静脉注射液(司机)模块、环境条件和目前的引擎操作条件,即发动机转速和增加压力。 瞬时转速的输出提供发动机动力学块(图4),而增压的名义价值比例作为函数的速度和加载基于预测的高保真的代码。因此,部分燃料控制逻辑的限制,即燃料在低提高保留在一种有意义的方式。 此外,仔细校准时间延迟是内置的代表涡轮迟滞的影响暂态响应快速增长的引擎“机架的立场”。更详细的引擎子系统和燃油控制器提供了工作Assanis et al。7。图3:产生一个力矩地图伸缩引擎图4:引擎子系统于SIMULINK仿真动力传动系统动力传动系统 动力总成模块由变矩器、 传输、 传动轴、 微分、 传动轴。 它提供了之间的连接引擎,电动机的转速和车辆动力学模块(参见图1)。变矩器输入轴连接到发动机飞轮。另一方面,变速轴和电动马达轴连接到螺旋桨轴,鉴别,并通过传动轴,到车轮。 因此,动力传动系统之间的连接和车辆动力学模型发生在车轮。 传动系统模型利用键合图建模语言(12和13）和实现20SIM 环境14。选择的键合图语言是由于其能力的不劳而获的生成模型的不同的复杂性15。 详细的发展的动力传动系统模型中描述的16;其关键参数值的表2中给出的附录。动力传动系统的动态行为代表了常微分方程描述运动学和动力学行为的真实系统。这些方程是自动生成的,由20个SIM作为标准的C代码。然后他们被转换成一个C-MEX函数。因此,最终的产品是一个功能适合直接集成与SIMULINK仿真模型。车辆动力学车辆动力学车辆子系统包括轮胎、 轴、 悬浮液和身体的车辆。 大量的方法可以用来模拟车辆动力学取决于整体仿真目标。 一个单自由度(自由度),点质量模型,可以被选为最初估计的车辆性能不同的动力系统选择是探索。 该模型的复杂性可以提高更多的自由度数目更严重的激励(路面粗糙度、 转向、制动等)被引入到模型。 这是必要的车辆动力传动交互的调查在这种极端的瞬变,导致显著的俯仰运动。复杂的模型可以系统地调整建议,Loucaet. al。15,以适应一个特定场景的需要。图5:车辆动力学的示意图。在这个工作的研究只考虑加速度的车辆在光滑的道路励磁不产生巨大的俯仰运动。因此,对于这种“温和”场景中,增强点质量模型,如图5所示,充分预测之间的相互作用对动力总成和汽车动力。 该模型由以下两部分组成,描述的动态行为车辆在纵向和隆的方向。 这两个组成部分是通过这条路/轮胎耦合交互。 车辆动力学的发展矩阵模型16及其关键参数表3给出了阑尾的。车辆动力学也建模使用了键合图语言在20 SIM环境。动态方程进行最后变成一个C-MEX函数使用上述过程在传动模块。电气子系统电气子系统 两个子系统是添加到电气路径:一个直流电机,铅酸电池。他们的特征将在下面。DC-MOTOR /发电机,因为传统的汽车的发动机是大约210千瓦,引擎的混合动力车是精减至约157千瓦(8个柱面减少到6缸),49千瓦永磁直流电机被选中。效率数据,得到了从顾问程序9,表单m = f(Tm,m)(请参见图6)。换句话说,效率的汽车是一个函数的电机转矩和电机速度。汽车动力学是一阶近似的滞后。然而,由于电池电量和扭矩限制,最终的汽车动力学假设以下形式: 正扭矩:负扭矩: 在m_ request T是最大扭矩电机可以生成根据现行电机速度、m的最大扭矩T由于电池约束,m T是计算电机转矩和m电机动态特征,是反过来的运动时间常数。负载电流的电池(下面)可以从以下公式计算:tb e 是电池的端电压,m是马达效率和m是电机转速。电池,电池模型8使用本质上是两个RC电路连接在系列(参见图7)。国家依赖内部阻力R和开放电路的电压oc V是函数的电池状态(SOC)的费用。 其他的抵抗(终端欧姆电阻b R)和两个参数(偏振电容p C和初期电容我C),被认为是常数。在这两个电容器的电压和SOC是三个状态变量给电池充电。因为这两个RC-circuit模式是远远快于SOC的动态,在简化模型(用于动态优化)我们将只有一个国家为battery-the SOC。图6:直流电机效率的地图源9图7:电池模型除了引入额外的动力,电池也很重要HE-VESIM整体燃料经济是因为它的非线性和非对称效率特性。图8显示了充放电电池的效率。这图是通过减少动态模型获得一个静态的一张,然后计算瞬时功率效率。 这个效率计算的过程中可以找到18。它可以清楚地看到,总体而言,最优组合得到激效率SOC相关= 0.6。图8:效率铅酸蓄电池的地图:a)放电效率;b)充电效率。电源管理算法电源管理算法 在本节中,我们将介绍两个电源管理算法:基于规则的算法,并基于动态规划的优化算法。基于规则的算法主要是基于工程的直觉。然而,由于自然混合动力汽车的多变量控制问题,这些规则通常不能捕获所有重要的现象。 动态编程算法可以帮助我们了解规则的不足,从而使得我们能够建造改良(和更复杂的)规则。 基于规则的算法基于规则的能量管理策略,建立了基于系统工程的直觉和简单的分析组件图表10。设计过程startsfrom解释司机踏板信号作为一个大国的请求,要求P。 根据电力要求,一个能源管理控制器决定了在混合动力系统功率流。 该控制器的操作可以分为三种模式,每个项目都将在下面: 正常模式:基于引擎效率映射如图9所示,一个预先确定的“引擎”电源线、 e _ on P ,和“汽车协助“电源线、 m_ a P。 如果请求的总功率小于“引擎”力量的水平,电动汽车将提供所需的电力。超越e _ on P ,引擎取代了电机提供总功率的请求。一旦电源请求超过这个引擎可以高效地生成、 m_ P,汽车被激活,供应额外的动力(req P - m_ a P )。 在这种模式下,引擎总是内运行高效地区间(between e _ on P and m_ a P )。充电模式:一个电荷维持策略被实现以确保电池状态的电荷(SOC)保持在预设上界和下界。55 - 60%范围是选择高效有机电池操作。当SOC低于低限制最小SOC,能源管理控制器将切换到电池充电模式(ch Flag =True)。一个预定充电功率、ch P,添加到驱动功率的要求,电机功率的命令是被迫成为负面为电池充电。 一个例外是,当请求总功率小于“引擎”力量的水平(tot e _ on P P ),电机驱动汽车仍将以避免引擎操作在这种低效的地区。 电池充电模式将不会停止,直到SOC支安打上界马克斯SOC(60%)。图9:基于规则的电源管理 制动模式:当司机踩下刹车踏板,它被理解为是一个否定的权力请求( 0) req P 。再生制动系统被激活的制动功率吸收。然而,当制动功率请求超过了再生制动能力m _ min P ,液压制动将会被激活以协助车辆减速。基于规则的算法的逻辑总结如下: 应该指出的是,因为它不是直接来找出是否和如何传播应该转向一个不同的方式(从最初的设计转变为大,地图7.3 L 8-cylinder引擎),我们决定使用相同的转变中的逻辑规则的算法。 基于动态编程算法与基于规则的算法、 动态规划,或类似的优化算法,通常依赖于一个模型来计算最佳的控制策略。 该模型可以分析和数值;换句话说,它可以处理诸如HE-VESIM数值黑匣子。在离散格式,这个模型可以形成x(k + 1)= f(x(k)、 u(k)。 和优化方案的目标是最小化成本函数。在本文中,假设的成本函数只包含燃油消耗率。在未来,当适当的排放模型包括在内,这个同步燃料经济排放优化问题能否得到解决。我们使用的成本函数具有以下形式: 在本文中包含的唯一术语L函数是瞬时油耗率。 该优化问题是解决在适当不等式约束条件,以确保发动机转速、 SOC、 燃料消耗和扭矩均符合相应的界限。同时,等式约束是强加的,这样车辆总是按指定的驾驶循环速度,以及加速驾驶循环规定。加快计算,预计算表可构造网格点到所有可能的状态和控制信号。详细的程序,基于look-up-table动态编程算法已报告在19,因此这里不再重复。应该指出的是,一个简化的模型(只有2状态变量、 SOC和齿轮号码)的生成HEVESIM动态编程算法,因为计算时间变得不可接受渴望高阶系统。在最优控制策略是发现,我们然后应用控制信号到原始HE-VESIM模型(参见图2),以确保性能得到数量从相同的(复杂的)模型。整合和验证整合和验证 混合车系统仿真(HE-VESIM)由五个主要功能模块:引擎,动力传动系统、 电动汽车、 电池和汽车结构。 推进模块之间的交互的形式是“积极”和“电阻式”扭矩,以及轴角的速度。仿真中配置方式前馈,所有的一切都始于司机行动和“踏板位置”信号被送到了喷射系统控制器。发动机仿真提供了作为输出瞬时值的发动机转矩和转速。经过多个转换的扭矩传输通过变矩器和传输。 在方向盘上的最终值取决于操作模式,即传动齿轮比和贡献的电动马达和发电机,以及对伸缩螺旋桨的和传动轴。 车轮上的扭矩转换成胶轮力量,它会同其他信息的车辆和地形决定汽车动态行为。因此,车辆动力学模块返回瞬时车速和车轮角速度。这个信息是通过系统传回到TC输出轴,从而可以确定变矩器水轮机调速器和TC速比。 后者决定了TC泵扭矩,后者又提供给引擎模块电阻扭矩。 解决方案的引擎动态方程确定了发动机转速值为下一个集成的一步。 这些动态模块的集成进行SIMULINK环境。它允许简单的图形化编程功能的耦合模块,只要每个人都有自己的所需的设置的输入/输出链接。然而,这是已知的(例如,17),灵活性SIMULINK带有一个特定计算效率方面的开销,实际规模被强烈依赖级别的系统分解和数量的组件模块和链接。为了提高计算效率,一些更复杂的模块是用C(动力传动系统、车辆动力学),以及被配置为独立的SIMULINK模块使用墨西哥函数的标准。 之前的研究该混合动力系统,模拟传统的VI类卡车通过比较,验证了测量结果的VESIM预测与上一个真实的车辆在国际的试验场,7。 对比计算结果与实验测量的两种加速度测试(0到60英里/小时,30到50英里/小时)展示了很好的协议7,因此得出仿真可以用于进一步研究来源于最初的配置之一。仿真结果仿真结果 汽车研究是国际4700系列。 柴油引擎从V8的萎缩(7.3升)到V6和49千瓦的电动机被选中,以协助内燃机。 传动齿轮速比匹配根据要求新配置的混合动力系统的并行与邮报传播电机位置。 机动车总质量是7258公斤。基本引擎,动力传动系统和车辆规范中给出了阑尾。 混合动力汽车的性能在发射和硬加速从0加速到60英里每小时是评估第一。 那么虚拟混合动力电动卡车与电源管理控制器是通过模拟测试在联邦城市驾驶的时间表中,以评估其潜能,提高燃油经济性。加速度0 60英里每小时0 - 60英里加速试验进行了模拟,以便核实并联混合动力与规模引擎保留相同的加速性能基线车辆常规。车辆速度的比较概要如图10所示。混合达到60英里/小时略早于传统的卡车,主要是由于更好的性能之后立即启动。揭示了更详细的系统响应在这快速瞬态。一个良好的价值高扭矩低速(参见图11 b)补偿反应变慢的柴油发动机由于涡轮滞后(参见图11 c),因此合并后的值导致更高的加速启动。因为司机电力需求是100%,电机运行在其最高可用不断转矩协助引擎,直到达到希望的速度。图11演示了累积的燃料消耗量的传统的汽车和混合动力电动汽车在加速运行。显然,规模显著减少引擎消耗燃料,而应该牢记这部分的能量来自电池。图10:速度剖面对比传统vs。在0 60英里/小时的加速度混合动力卡车 燃料经济在一个驾驶循环联邦城市驾驶的计划(这类,见图12)是用来评估了卡车的燃料经济性进行了研究工作。一旦速度剖面的驾驶循环是指定的,相应的扭力轮胎必要遵循计算的速度剖面(图12)和使用所需的输出和基于规则的和最优算法。有趣的是更严密地检查的一个控制算法,比如基于规则的控制器、 开关在不同的模式之间的混合操作在驾驶时间表。 为了这一目的,一个特写镜头的430年到540年第二期的驾驶循环中给出了图13。 这部分的周期包括两个加速度巡航减速概要文件(参见图13),第一个要求更高的速度加速更难。 电池SOC,发动机功率和电机功率给出数字13 b、c和d,分别。卡车从停止使用只发射了电机避免效率低下的发动机运行在低功率需求。然而,引擎是打开非常迅速,因为电力需求需要输出从这两个发动机和电动马达。 从450年到477秒,需要的能量巡航速度的35英里每小时少于“引擎”力量的水平,因此引擎,电机电流供应所有的扭矩需要在轮子。当卡车减速,再生制动系统是应用;因此电机运转如同生成器来恢复精力,否则会分散掉刹车(477到490秒,530到537秒间隔)。 应该指出的是,当电池SOC击中下限(55%),在523秒到时间表,引擎是立刻打开了权力的卡车,以及为电池充电。因此,电机切换到生成器模式及其扭矩变得消极。Figure11:关键系统变量在0 60英里加速:传统的汽车(VESIM)和混合动力电动卡车(HE-VESIM)图12:联邦城市驾驶的计划此外,近距离的检查行为两辆汽车以不同控制算法在690到765年之间的第二个驾驶循环中给出了图14。 两辆汽车的行为在减速(从714到726秒,749到763秒)都是相同的。 这不足为奇,因为制动策略的两种算法都是相同的。 另外,两种算法使用电机在发射过程中避免效率低下的发动机运行。 因为基于规则的算法是在充电模式在这个时期,电池需要充电每当权力要求超越了“引擎”力量的水平直到电池SOC到达马克斯SOC(60%)。 电池698到714秒之间发生充电秒,以及728到740秒。因此,基于规则的引擎算法工作更努力地动态规划的案例,以提供附加的恒功率ch P为电池充电。 燃料经济可能遭受。 相反,由于动态规划优化过程在整个驾驶循环,一个更好的放电/收费安排部署。图13:的特写,430 - 540年的第二次间隔在城市驾驶循环:车辆速度的一个);b)电池状态;c)柴油发动机功率;d)电机功率。燃料经济的成果完成城市驾驶的时间表,获得了两个控制算法与传统的柴油引擎的卡车在表1。 这些结果获得了电荷维持策略,以SOC循环的末尾是和它一开始。 可以看到,提高燃油经济性(传统的汽车)大概是22%,基于规则的算法和33%的动态编程算法。实例的基于规则的算法,其中很大一部分的改善是由于再生制动。 进一步改进,得到通过更好的策划动态规划之间的协调操作的发动机、传动和电机/电池。图15显示了引擎操作点计算BSFC地图开车时安排三车辆配置(传统的,基于HEVrule和HEV-dynamic编程)。 最大的集群的位置点的混合动力汽车都地图是更多有利相对传统的车辆,即引擎被迫工作在相对比较高的负载和点靠近了效率高地区。 详细的审查地图获得了两个混合版本表明,基于规则的算法已经达到更一致的发动机运行岛附近的优化效率,但相当多的点是位于最大扭矩线。动态编程算法,会产生更高的整体的燃油效率的探索整个系统,而不是只盯着发动机的效率。 换句话说,看来,在某些情况下,这是更有效的牺牲一些引擎效率和获得在其他方面,通过更有效的马达操作和更好的优化充电/放电时间表。 这个观察目前正在进行更详细的分析以设计一种改进的基于规则的HE-VESIM算法。表1燃料消耗的比较:传统的、动态的编程(DP),基于规则(RB)燃料消耗(公斤/ kWhr)发动机转速(rpm)a)燃料消耗(公斤/ kWhr)发动机转速(rpm)b)发动机转速(rpm)c)图15:发动机运行在一个比较驾驶循环:a)传统的汽车;b)混合,总结和结论总结和结论本文介绍了开发的一种前馈、平行、混合动力电动汽车系统仿真器,其用于评估的电源管理策略旨在最大化燃料经济。模拟器是基于之前确认的模拟器的传统发动机车辆系统,包括模块的柴油引擎,动力系统和车辆动力学的适当的忠诚。 电力系统中的组件集成在SIMULINK编程环境中,这样的电动马达和发电机位于传播后,与输出轴通过机电耦合。 柴油引擎萎缩自电动机能够提供援助在高功率需求操作。两个电源管理算法分析了paper-a动态编程算法和基于规则的算法。在这两种情况下,策略的目的是在保持电池状态的电荷。混合动力汽车的加速性能的VI类卡车被证实是相当传统的汽车。良好的扭矩特性电动机的弥补了延迟响应导致柴油机的由涡轮滞后。 仿真的车辆超过一个完整的城市驾驶循环显示,可以改善燃油经济性的20 - 30%在传统的(没有混合动力)的卡车。 结果表明,动态编程算法达到更高的整体燃料经济,尽管其引擎可能经常运行在一个低效率的地区比一个基于规则的控制算法。这一事实说明了协调多个输入的重要性,这可能不是被简单的工程的直觉。换句话说,在某些情况下,这是更有效的牺牲一些引擎效率、 效益通过更有效的马达操作和优化充电/放电时间表,全面建设更好的妥协。应答应答 作者想承认的技术和财政支持的汽车研究中心(弧)由国家汽车中心(NAC)位于在美国陆军坦克自动化研究、 发展与工程中心(TARDEC)。 ARC是美国陆军卓越中心的汽车研究在密歇根大学,目前在阿拉斯加大学的伙伴关系,克莱姆森大学,爱荷华大学,奥克兰大学,田纳西大学韦恩州立大学和威斯康辛-麦迪逊大学的。动态编程方案所作贡献的Jungmo Kang和Jessy Grizzle的密歇根大学的部门也欣然承认。引用引用1。 Baumann, Bernd M.; Washington, G.; Glenn, Bradley C.; Rizzoni, G.,“机电设计和控制混合动力车,“IEEE / ASME事务对机电一体化,v5 n1 2000。p 58 - 722。 Farrall, S. D. and Jones, R. P.,”能源管理是在一家汽车电动/听到引擎混合动力系统采用模糊决策。1993年国际研讨会上智能控制、 芝加哥、IL,1993。c)图15:发动机运行在一个驾驶循环比较:1)传统的汽车;b)混合,123。Kim, C., NamGoong, E., and Lee, S.,油耗优化并联混合动力汽车无级变速。“SAE纸,编号9 - 1999-01-1148 -。4。Paganelli, G., Ercole, G., Brahma, A., Guezennec, Y. and Rizzoni,G.,如,“通用的计算公式瞬时功率分流的控制负责维持混合动力电动汽车。 学报,5日郭旃一起2000年研讨会上先进车辆控制、 Ann Arbor, MI,2000。5.Johnson, V.H., Wipke, K.B. and Rausen, D.J.、“混合动力汽车控制策略,并对实时优化的燃油经济性和排放,”程序的未来汽车的国会,2000年4月,SAE纸# 2000-01-1543。6。Brahma, A., Guezennec, Y. and Rizzoni, G.,如,“动态优化机械/电子能量流向并联混合动力电动汽车学报2000年一起,5日郭旃研讨会上先进车辆控制、Ann Arbor, MI,2000。7 。 ssanis, D.N., Z.S. Filipi, S. Gravante, D. Grohnke X. Gui, L.S.Louca, G.D. Rideout, J.L. Stein, Y. Wang出版社,2000年。 验证和使用SIMULINK集成、高保真度,Engine-In-Vehicle仿真的国际VI类的卡车。SAE论文2000-01-0288,SAE 2000世界大会。8。Powell, B.K. and Pilutti, T.E.、“增程型混合动力汽车的动态模型”,诉讼的第33 IEEE会议上决定和控制,伟湖FL,1994年12月。9。 Burch, Steve, Cuddy, Matt, et. al.,”顾问2.1文档,”国家可再生能源实验室,1999年3月。10。 Bowles, P. D.,建模和能量管理一个并联混合动力电动汽车(PHEV)与变速(CVT),“论文女士,密歇根大学安娜堡分校,Ann Arbor, MI,1999。11。 Assanis D.N., and Heywood J.B.、开发和使用的计算机模拟的Turbocompounded 柴 油 发 动 机 性 能 和 系 统 组 件 传 热 研 究 ,” 论 文860329,1986 SAE。12。Karnopp, D. C., Margolis, D. L., and Rosenberg, R. C.,系统动力学:一个统一的方法。Wiley-Interscience,纽约,纽约,1990年。13。Rosenberg, R. C., and Karnopp, D. C.,介绍物理系统动力学。麦格劳-希尔,纽约,纽约,1983年14。 20 SIM,20个SIM Pro用户手册。 特文特大学的Controllab产品B.V.恩斯赫德,荷兰,199915。 Louca, L. S., Stein, J. L., Hulbert, G. M.一个Physical-Based模型降指标应用程序车辆动力学”。 第四研究非线性控制系统设计研讨会(NOLCOS 98)。恩斯赫德,荷兰,1998。16。 Louca, L.S., J.L. Stein and D.G.,2001。 动态模型生成适当的整合为车辆移动使用键合图配方。 2001年国际会议论文集对键合图模型的建立,一月份,凤凰城,阿兹。出版的社会过程计算机模拟。17。 Liu, H., Chalhoub, N. G., Henein, N.,“模拟的单缸柴油机冷起动条件下用SIMULINK论文集,春天的ASME-ICE技术会议,卷。 28-1、 柯林斯堡、CO、4月研究,1997。18。 Wiegman,h . l . n,Vandenput,aja .,电池状态控制技术应用程序,负责维持“机械研究所硕士论文,981129。19。 Jun-Mo Kang, Ilya Kolmanovsky and J.W.“近似动态编程解决方案燃烧引擎后处理精益,”进行的IEEE会议上决定和控制、菲尼克斯、阿兹,12月7 - 10,1999。20。Assanis D., Delagrammatikas, G, Fellini, R., Filipi,Z., Liedtke, J.,Michelena, N., Papalambros, P.,Reyes, D., Rosenbaum, D., Sales, A.,Sasena,M. ,“一种优化方法对混合动力ElectricPropulsion系统设计”,力学Structuresand的机器,27卷,第4期,1999年,页393 -4212001-01-1334Integrated, Feed-Forward Hybrid ElectricVehicleSimulation in SIMULINK and its Use forPowerManagement StudiesChan-Chiao Lin, Zoran Filipi, Yongsheng Wang, Loucas Louca,Huei Peng, Dennis Assanis, Jeffrey SteinAutomotive Research CenterThe University of MichiganCopyright 2001 Society of Automotive Engineers, Inc.ABSTRACTA hybrid electric vehicle simulation tool (HE-VESIM) has been developedat the Automotive Research Center of the University of Michigan to study thefuel economy potential of hybrid military/civilian trucks. In this paper, thefundamental architecture of the feed-forward parallel hybrid-electric vehiclesystem is described, together with dynamic equations and basic features ofsub-system modules. Two vehicle-level power management controlalgorithms are assessed, a rule-based algorithm, which mainly exploresengine efficiency in an intuitive manner, and a dynamic-programmingoptimization algorithm. Simulation results over the urban driving cycledemonstrate the potential of the selected hybrid system to significantlyimprove vehicle fuel economy, the improvement being greater when thedynamicprogramming power management algorithm is applied.INTRODUCTIONGrowing environmental concerns coupled with concerns about globalcrude oil supplies stimulate research aimed at new, fuel-efficient vehicletechnologies. Hybrid-electric vehicles (HEV) appear to be one of the mostviable technologies with significant potential to reduce fuel consumption withinrealistic economical, infrastructural and customer acceptance constraints.Dozens of prototype/concept hybrid vehicles have been developed. Toyotaand Honda have already launched production vehicles and many other majorautomakers are expected to launch hybrid vehicles in the next 3-5 years. Dueto the existence of dual power-sources, the additional design degrees offreedom of HEV offer unprecedented possibilities in fuel economy andexhaust emissions, particularly if parallel powertrain architectures areemployed. At the same time, the complexity of the new vehicle systemrequires the application of simulations for accurate sizing and matchingstudies, as well as for development of control algorithms well ahead of thefinal design and physical prototyping.Most of the control strategies developed for parallel HEV fall into threecategories. The first type applies intelligent control techniques such asrules/fuzzy logic/NN for estimation as well as control algorithm development 1and 2. The second type of approach is based on static optimization methods.Commonly, to calculate the cost of energy, the electric energy is translatedinto an equivalent amount of fuel 3 and 4. The optimization scheme thenfigures out proper energy and/or power split between the two energy sourcesunder steady-state operations. Due to its relatively simple point-wiseoptimization nature, it is possible to extend the optimization scheme to solvethe simultaneous fuel economy and emission optimization problem 5. Thebasic idea of the third type of HEV control algorithm is similar to that of staticoptimization; however, the optimization was performed for dynamic systems6. Further, the optimization is with respect to a time horizon, rather than for afixed point in time. In general, the power split algorithm from the dynamicoptimization will be more accurate under transient conditions. Usually, thedynamic optimization algorithms are not implementable due to their previewnature and heavy computation requirement. They are, however, a goodbenchmark based on which the first two types of algorithms can be improvedor compared against.The objective of this work is to develop an integrated hybrid vehiclesimulation tool and use it for the design of energy management controlalgorithms. The basis for our Hybrid Vehicle-Engine SIMulation (HE-VESIM) isthe high-fidelity conventional vehicle simulator VESIM previously developed atthe University of Michigan 7. VESIM has been validated againstmeasurements for a Class VI truck, and proven to be a very versatile tool formobility, fuel economy and drivability studies. To construct a hybrid-vehiclesimulator, some of the main modules require modifications, e.g. the engineneeds to be reduced in size/power, and the electric component models needto be created and integrated into the system. Our HEV simulation effort willfocus on parallel post-transmission configurations, where the electric motor ismechanically coupled to the output shaft. A feed-forward simulation schemewill be employed so as to enable studies of control strategies under realistictransient conditions. The integrated HEV simulation will be implemented inSIMULINK to allow for easy reconfiguration of the system and to enable thedesigner to select proper models depending on specific simulation goals. Twocontrol algorithms are investigated in this paper: a rule-based and a dynamicprogramming optimization algorithm.The paper is arranged as follows. The configuration of the newlydeveloped hybrid electric vehicle system in SIMULINK is discussed first,followed by the description of features of the main simulation modules: dieselengine, drivetrain, vehicle dynamics and electric components. Next, twopower management algorithms: a rule-based algorithm, and a dynamicprogramming based optimization algorithm are introduced. The completehybrid vehicle simulation is then used to assess the acceleration ability andthe fuel economy of the hybrid vehicle through comparisons with itsconventional counterpart. The two control strategies are evaluated throughsimulation predictions of fuel consumption over a driving cycle, followed bythe summary and conclusions.HYBRID-ELECTRIC VEHICLE SYSTEMThe vehicle system considered in this work is a 4X2 Class VI truck configuredas a parallel hybrid with the electric motor positioned after the transmission.The schematic of the vehicle and the propulsion system is given in Figure 1.The engine is connected to the torque converter (TC), whose output shaft isthen coupled to the transmission (Trns).Figure 1: Schematic of the integrated vehicle system.The coupling at the transmission output side engages or disengages theelectric motor depending on the operation mode of the hybrid. Hence,thetransmission and/or electric motor can be linked to the propeller shaft (PS),differential (D) and two driveshafts (DS), coupling the differential with thedriven wheels.The complete vehicle system simulation is structured to directly resemblethe layout of the physical system. In order to have a high degree of flexibility,the simulation structure is implemented in the MATLAB/SIMULINK graphicalsoftware environment, as shown in Figure Links between main modulesrepresent the physical parameters that actually define the interaction betweenthe components, such as shaft torque and angular velocity, or electricalcurrent and voltage. The HEV controller contains the power managementlogic and sends control signals to the components modules based on thefeedback about current operating conditions. Finally, a “driver” module allowsthe feed-forward simulation to follow a prescribed vehicle speed schedule.The Intelligent Speed Controller (IVS) fulfills that role and provides the driverdemand signal and braking based on the specified speed setting and thecurrent vehicle speed.Figure 2 Hybrid-electric vehicle simulation in SIMULINKENGINEThe engine model is derived from the high fidelity, thermodynamic enginesystem previously developed for the conventional vehicle 7 and 11. The highfidelity engine model was comprised of multiple cylinder modules linked withexternal component modules for manifolds, compressors and turbines, heatexchangers, air filters, and exhaust system elements. In order to support thecomputationally intensive simulations over long driving cycles and facilitateeasy scaling of the engine, the thermodynamic engine model is replaced by alook-up table that provides brake torque as a function of instantaneous enginespeed and mass of fuel injected per cylinder/cycle. The look-up table isactually generated using a previously validated high fidelity engine systemcode 11, hence it is possible to physically vary the size of the engine, or itsdesign, and have a realistic representation of the effect of a given change. Forthe parallel hybrid application, the original V8 7.3 L diesel is downsized byreducing the number of cylinders to 6, and hence the displacement to 5.5 L.The turbomachinery maps are scaled to match the smaller engine, followingthe methodology described in 20. The whole procedure for generating torquelook-up tables based on predictions of a validated high fidelity engine systemcode is illustrated in Figure 3. The specifications of both the V8 engine for theconventional vehicle and the V6 engine for the hybrid application are given inTable 1 of the Appendix.In order to retain features of the engine system critical for the transientresponse, the complete fuel control logic is retained in the look-up table basedmodel, as shown in Figure 4. The diesel engine fuel injection controllerprovides the signal for the mass of fuel injected per cycle based on driverdemand, supplied by the IVS (driver) module, environmental conditions andcurrent engine operating conditions, i.e. engine speed and boost pressure.The instantaneous engine speed is provided as the output of the enginedynamics block (Figure 4), while the nominal value of boost pressure istabulated as the function of speed and load based on predictions of the highfidelity code. Hence, the part of the fuel control logic that limits the fuel at lowboost is retained in a meaningful way. In addition, a carefully calibrated timedelay is built-in to represent the effect of turbo-lag on transient response torapid increases of engine “rack positions”. More details about the enginesubsystem and the fuel controller are provided in the work by Assanis et al.7.Figure 3: Generating a torque map for a scaled engineFigure 4: Engine subsystem in SIMULINKDRIVETRAINThe driveline module consists of the torque converter, transmission,propshafts, differential, and drive shafts. It provides the connection betweenthe engine, the electric motor and the vehicle dynamics module (see Figure1). The torque converter input shaft connects to the engine flywheel. On theother end, the transmission-out shaft and/or the electric motor shaft areconnected to the propeller shaft, the differential and, via driveshafts, to thewheels. Hence, the connection between the driveline and the vehicledynamics model occurs at the wheel. The drivetrain model is constructedusing the bond graph modeling language 12 and 13 and implemented in the20SIM system-modeling environment 14. The bond graph language isselected due to its capability of effortless generation of models with differentcomplexity 15. The detailed development of the drivetrain model is describedin 16; its key parameter values are given in Table 2 of the Appendix.The dynamic behavior of the drivetrain is represented by ordinarydifferential equations that describe the kinematic and dynamic behavior of thereal system. These equations are automatically generated by 20SIM asstandard C code. They are then converted into a C-MEX function. Hence, thefinal product is an Sfunction suitable for direct integration with the SIMULINKmodel.VEHICLE DYNAMICSThe vehicle subsystem includes the wheels/tires, axles, suspensions andbody of the vehicle. A number of approaches can be used to model vehicledynamics depending on the overall simulation objectives. A single Degree ofFreedom (DOF), point mass model, can be selected for an initial estimate ofvehicle performance as different powertrain options are explored. The modelcomplexity can be enhanced with more DOFs as more severe excitations(road roughness, steering, braking, etc.) are introduced into the model. This isnecessary for the investigation of vehicle-powertrain interactions during suchextreme transients that induce significant pitch motion. The complexity of themodel can be systematically adjusted, as proposed by Louca et. al.15, toaccommodate the needs of a specific scenario.Figure 5: Schematic of vehicle dynamics.The studies in this work consider only the acceleration of the vehicle on asmooth road where the excitation does not generate significant pitch motion.Therefore, for this “mild” scenario, the enhanced point mass model, shown inFigure 5, adequately predicts the interactions between the powertrain andvehicle dynamics. The model is composed of two components that describethe dynamic behavior of the vehicle in the longitudinal and heave directions.The two components are coupled through the road/tire interaction. Thedevelopment of the vehicle dynamics model is given in 16 and its keyparameter values are given in Table 3 of the Appendix. The vehicle dynamicsare also modeled using the bond graph language within the 20SIMenvironment. The dynamic equations are finally onverted into a C-MEXfunction using the same procedure as in the drivetrain module.ELECTRIC SUB-SYSTEMS Two sub-systems were added to the electric path: a DC motor, and alead-acid battery. Their characteristics are described in the following.DC-MOTOR/GENERATOR - Because the engine of the conventionaltruck is roughly 210 KW and the engine for the hybrid is downsized to about157 KW (8 cylinders reduced to 6 cylinders), a 49 KW permanent magnet DCmotor is selected. The efficiency/loss data, obtained from the Advisor program9, have the form m = f (Tm ,m ) (see Figure 6). In other words, theefficiency of the motor is a function of motor torque and motor speed. Themotor dynamics are approximated by a first-order lag. However, due to thebattery power and motor torque limit, the final motor dynamics assume thefollowing form: Positive Motor Torque:Negative Motor Torque:where m_ request T is the requested motor torque, m_ max T is themaximum torque the motor can generate under current motor speed, m_ batT is the maximum motor torque due to battery constraint, m T is the calculatedmotor torque, and m characterizes the motor dynamics, and is the inverse ofthe motor time constant. The load current for the battery (to be presentedbelow) can then be calculated from the following equation:where tb e is the battery terminal voltage, m is the motor efficiency, and m is the motor speed.BATTERY - The battery model 8 used is essentially two RC circuitsconnected in series (see Figure 7). The state dependent internal resistance Rand the open circuit voltage oc V are functions of the battery State Of Charge(SOC). The other resistance (the terminal ohmic resistance b R ) and the twocapacitances (the polarization capacitance p C and incipient capacitance i C )are assumed to be constant. The voltages across the two capacitors and theSOC are the three state variables for the battery. Because the two RC-circuitmodes are much faster than the dynamics of SOC, in the simplified model(used for dynamic optimizations) we will only have one state for the batterythe SOC.Figure 6: Efficiency map of the DC motor source 9Figure 7: Battery modelIn addition to introducing extra dynamics, the battery is also important inthe overall HE-VESIM fuel economy because of its nonlinear and non-symmetric efficiency characteristics. Figure 8 shows the charging anddischarging efficiency of the battery. This figure was obtained by reducing thedynamic model to a static one and then computing the instantaneous powerefficiency. The process of this efficiency calculation can be found in 18. It canbe clearly seen that, overall, the optimal combined charging-dischargingefficiency is obtained around SOC=0.6.Figure 8: Efficiency maps of the lead acid battery: a) discharging efficiency; b) charging efficiency.POWER MANAGEMENT ALGORITHMS In this section, we will present two power management algorithms: a rule-based algorithm, and a dynamic programming based optimization algorithm.The rule-based algorithm is mainly based on engineering intuition. However,due to the multi-variable nature of HEV control problems, these rules usuallyfail to capture all the important phenomena. The dynamic programmingalgorithm can help us understand the deficiency of the rules, and thus makesit possible to construct improved (and more complicated) rules. RULE BASED ALGORITHM - The rule-based energy managementstrategy was developed based on engineering intuition and simple analysis ofcomponent efficiency tables/charts 10. The design process startsfrominterpreting the driver pedal signal as a power request, req P . According tothe power request, an energy management controller determines the powerflow in the hybrid powertrain. The operation of this controller can be dividedinto three modes, each of which is described in the following:Normal Mode: Based on the engine efficiency map shown in Figure 9, apre-selected “engine on” power line, e _ on P , and “motor assist” power line,m_ a P , are chosen. If the total power request is less than the “engine on”power level, the electric motor will supply the requested power. Beyond e _ onP , the engine replaces the motor to provide the total power request. Once thepower request exceeds what the engine can efficiently generate, m_ a P , themotor is activated to supply the additional power ( req P - m_ a P ). In thismode, the engine always operates within the high efficient region (between e_ on P and m_ a P ). Charging Mode: a charge-sustaining strategy is implemented to assurethat the battery State Of Charge (SOC) stays within preset upper and lowerbounds. The 55-60% SOC range is chosen for efficient battery operation.When the SOC drops below the low limit min SOC , the energy managementcontroller will switch to the battery recharge mode ( ch Flag = True ). Apreselected recharge power level, ch P , is added to the driver power request,and the motor power command is forced to become negative to recharge thebattery. One exception is that when the total power request is less than the“engine on” power level ( tot e _ on P P ), the motor will still propel thevehicle to avoid the engine operating in this inefficient region. The batteryrecharge mode will not stop until the SOC hits the upper bound max SOC(60%).Figure 9: Rule based power management Braking Mode: When the driver steps on the brake pedal, it is interpretedas a negative power request ( 0) req P . The regenerative braking isactivated to absorb the braking power. However, when the braking powerrequest exceeds the regenerative braking capacity m _ min P , the hydraulicbraking will be activated to assist the vehicle deceleration.The logic of the rule-based algorithm is summarized in the following:It should be noted that because it is not straightforward to figure outwhether and how the transmission should be shifted in a different manner(from the original shift map designed for the larger, 7.3L 8-cylinder engine), wedecided to use the same shift logic in the rule-based algorithm. DYNAMIC PROGRAMMING BASED ALGORITHM As opposed to therule-based algorithm, the dynamic programming, or similar optimizationalgorithms, usually rely on a model to compute the best control strategy. Themodel can be either analytical or numerical; in other words, it can work withnumerical black boxes such as HE-VESIM. In the discrete-time format, themodel could have the form x(k +1) = f (x(k),u(k) . And the goal of theoptimization scheme is to minimize a cost function. In this paper, the costfunction is assumed to consist of only fuel consumption rate. In the future,when proper emission models are included, the simultaneous fuel economy-emission optimization problem can be solved.The cost function we used has the following form: where in this paper the only term included in the L function is theinstantaneous fuel consumption rate. The optimization problem is solvedunder proper inequality constraints to ensure that the engine speed, SOC, fuelconsumption and motor torque are all within their corresponding bounds. Also,equality constraints are imposed so that the vehicle always follows thespecified driving cycle speed, as well as the acceleration prescribed by thedriving cycle. To accelerate computations, pre-computed tables areconstructed for grid points for all possible states and control signals. Thedetailed procedures of the look-up-table based dynamic programmingalgorithm have been reported in 19 and thus are not repeated here. It should be noted that a simplified model (with only 2 state variables,SOC and gear number) of the HEVESIM was generated for the dynamicprogramming algorithm, because the computation time becomesunacceptably long for higher order systems. After the optimal control strategyis found, we then apply the control signals to the original HE-VESIM model(see Figure 2) to ensure that the performance number is obtained from thesame (complex) model.INTEGRATION AND VALIDATIONThe hybrid vehicle system simulation (HE-VESIM) consists of five mainmodules: engine, driveline, electric motor, battery, and vehicle structure. Theinteraction between the propulsion modules is in the form of “active” and“resistive” torques, as well as shaft angular speeds. The simulation isconfigured in a feed-forward manner, where everything starts with the driveraction and the “pedal position” signal being sent to the injection systemcontroller. The engine simulation provides as outputs the instantaneous valueof engine torque and the rotational speed. The torque undergoes multipletransformations as it is transmitted through the torque converter and thetransmission. The final value at the wheel depends on the operating mode,i.e. transmission gear ratio and the contribution of the electricmotor/generator, as well as on flexing of the propeller and drive shafts. Thetorque on the wheels is converted into tractive forces, which in conjunctionwith other information about the vehicle and the terrain determines vehicledynamic behavior. Hence, the vehicle dynamics module returns theinstantaneous vehicle speed and the wheel angular velocity. This informationis propagated back through the system, all the way to the TC output shaft,thus determining the torque converter turbine speed and the speed ratio of theTC. The latter determines the TC pump torque, which is in turn supplied to theengine module as resistive torque. The solution of the engine dynamicequations determines the engine speed value for the next integration step. The integration of these dynamic modules is performed in the SIMULINKenvironment. Its graphical programming capabilities allow easy coupling of themodules, as long as each one of them has a desired set of input/output links.However it was known (e.g., 17) that the flexibility of SIMULINK comes witha certain overhead in terms of computational efficiency, the actual magnitudebeing strongly dependent on the level of system decomposition and thenumber of component modules and links. In order to enhance computationalefficiency, some of the more complex modules are programmed in C(drivetrain, vehicle dynamics), and configured as self-contained SIMULINKblocks using the MEX function standard.Prior to the studies of the hybrid-electric system, the simulation of theconventional Class VI truck was validated through comparisons of VESIMpredictions with measurements obtained on a real vehicle on Internationalsproving grounds 7. The comparison of calculated results and measurementsfor two acceleration tests (0 to 60 mph and 30 to 50 mph) demonstrated verygood agreement 7, hence it was concluded that the simulation could be usedfor further studies of configurations derived from the original one.SIMULATION RESULTS The vehicle studied is the International 4700 series. The diesel engine isdownsized from the V8 (7.3L) to a V6 and a 49 KW electric motor is selectedto assist the internal combustion engine. The transmission gear ratios arematched according to the demands of the newly-configured parallel hybridpowertrain with the post-transmission motor location. Total vehicle mass is7258 kg. Basic engine, drivetrain and vehicle specifications are given in theAppendix. The performance of the hybrid vehicle during launch and hardacceleration from 0 to 60 mph is assessed first. Then the virtual hybrid electrictruck with the power management controller is tested through simulation overthe Federal Urban Driving Schedule in order to evaluate its potential for fueleconomy improvement.ACCELERATION 0-60 mph The 0 to 60 mph acceleration test wassimulated in order to verify that the parallel hybrid with the downsized engineretains the same acceleration performance as the conventional baselinevehicle. The comparison of vehicle speed profiles is shown in Figure 10. Thehybrid achieved 60 mph slightly earlier than the conventional truck, primarilydue to better performance immediately after launch. reveals more detailsabout the system response during this rapid transient. A favorably high valueof motor torque at very low speeds (see Figure 11b) compensates for theslower response of the diesel engine due to turbo lag (see Figure 11c), hencethe combined value results in a higher acceleration at launch. Since the driverpower demand is 100%, the motor continuously operates at its highestavailable torque to assist the engine until the desired speed is achieved.Figure 11a illustrates the cumulative fuel consumption of the conventionaltruck and the hybrid electric vehicle during the acceleration run. Obviously, thedownsized engine consumes significantly less fuel, while it should be kept inmind that part of the energy comes from the battery.Figure 10: Speed profile comparison of conventional vs.hybrid truck during 0-60 mph accelerationFUEL ECONOMY OVER A DRIVING CYCLE The Federal UrbanDriving Schedule (FUDS, see Figure 12) was used to evaluate the fueleconomy of the delivery truck studied in this work. Once the speed profile ofthe driving cycle is specified, the corresponding torque at the tires necessaryto follow the speed profile is calculated (Figure 12) and used as the desiredoutput for both the rule-based and the optimal algorithms. It is interesting toexamine more closely how one of the control algorithms, e.g. the rule-basedcontroller, switches between different modes of hybrid operation during adriving schedule. For that purpose, a close-up of the 430 to 540 secondperiod of the driving cycle is given in Figure 13. This segment of the cycleincludes two accelerationcruisingdeceleration profiles (see Figure 13a), thefirst one requiring harder acceleration to higher speed. The battery SOC,engine power and electric motor power are given in Figures 13b, c and d,respectively. The truck launches from stop using only the motor to avoidinefficient engine operation under low power demands. However, the engineis turned on very quickly, since the power demand requires the output fromboth the engine and the electric motor. From 450 to 477 sec, the powerrequired to cruise at the speed of 35 mph is less than the “engine on” powerlevel, hence the engine is disengaged and the motor supplies all the torquerequired at the wheels. When the truck decelerates, the regenerative brakingis applied; hence the motor operates as a generator to recover the energy thatwould otherwise be dissipated in brakes (477 to 490 sec and 530 to 537 secinterval). It should be noted that when the battery SOC hits the lower bound(55%), at 523 seconds into the schedule, the engine is immediately turned onto power the truck, as well as to recharge the battery. Hence, the electricmotor is switched to the generator mode and its torque becomes negative.Figure11: Critical system variables during 0-60 mph acceleration: conventional truck (VESIM) vs. the hybrid electric truck (HE-VESIM)Figure 12: Federal Urban Driving Schedule (FUDS) Furthermore, a close-up examination of the behavior of two vehicles withdifferent control algorithm between 690 to 765 second of the driving cycle isgiven in Figure 14. The behaviors of the two vehicles under deceleration (from714 to 726 sec, and 749 to 763 sec) are identical. This is not surprising sincethe braking strategies of the two algorithms are identical. Besides, bothalgorithms use the motor during launch to avoid inefficient engine operation.Since the rule-based algorithm is in the charging mode within this timeperiod,the battery needs to be recharged whenever the power request isbeyond the “engine on” power level until the battery SOC reaches the maxSOC (60%). Battery Figure 13: The close-up of the 430 540 second interval during the urbandriving cycle: a) vehicle speed; b) battery state of charge; c) diesel engine power; and d) electric motor power.recharge occurs between 698 to 714 sec, and 728 to 740 sec. Hence, theengine for the rule-based algorithm works harder than that of the dynamic-programming case in order to provide additional constant power ch P torecharge the battery. The fuel economy could suffer. On the contrary, sincethe dynamic programming optimizes the process over the whole driving cycle,a better discharging/charging schedule has been deployed. The fuel economy results over the complete urban driving schedule,obtained for the two control algorithms are compared with a conventionaldiesel engine truck in Table 1. These results are obtained for the chargesustaining strategy, with the SOC at the end of the cycle being the same as itwas at the beginning. It can be seen that the fuel economy improvement (overthe conventional truck) is about 22% for the rule-based algorithm and 33% forthe dynamic-programming algorithm. For the case of rule-based algorithm, alarge portion of its improvement is due to regenerative braking. Furtherimprovement was obtained by dynamic programming through better-orchestrated coordination between the operation of engine/transmission andmotor/battery. Figure 15 shows engine operating points on the BSFC mapcalculated during the driving schedule for all three vehicle configurations(conventional, HEVrule based and HEV-dynamic programming). The positionof the largest clusters of points on both HEV maps is much more favorablecompared to the conventional vehicle, i.e. the engine is forced to operate atrelatively higher loads and points are moved closer to the high efficiencyregion. Closer examination of the maps obtained for the two HEV versionsindicates that, the rule-based algorithm has achieved a more consistentengine operation near the island of optimum efficiency, but quite a few pointsare located on the maximum torque line. The dynamic programming algorithmproduces higher overall fuel economy by exploring the efficiency of the wholesystem, instead of focusing just on the engine efficiency. In other words, itappears that in some instances it is more effective to sacrifice some of theengine efficiency and gain on other fronts, through more efficient motoroperation and better-optimized charging/discharging schedule. Thisobservation is currently being analyzed in more detail in order to devise animproved rule-based algorithm for the HE-VESIM.Table 1 Fuel consumption comparison: conventional, dynamicprogramming (DP), rule-based (RB)Fuel Consumption (kg/kWhr)Engine speed (rpm)a)Fuel Consumption (kg/kWhr)Engine speed (rpm)b)Engine Speed (rpm)c)Figure 15: Engine operation comparison over adriving cycle: a) conventional truck; b) hybrid,SUMMARY AND CONCLUSIONS This paper presents the development of a feedforward, parallel, hybridelectric vehicle system simulator, and its use for evaluation of powermanagement strategies aimed at maximizing fuel economy. The simulator isbased on a previously validated simulator of conventional engine-vehiclesystems that includes modules of the diesel engine, driveline and vehicledynamics of the appropriate fidelity. The electric components are integrated inthe system in the SIMULINK programming environment so that the electricmotor/generator is located after the transmission and linked to the output shaftvia an electro-mechanical coupling. The diesel engine is downsized since theelectric motor is able to provide assistance during the high power demandoperation. Two power management algorithms were analyzed in this paperadynamic programming algorithm and a rule-based algorithm. In both casesthe strategy aims at sustaining the battery state of charge. The accelerationperformance of the HEV Class VI truck was shown to be comparable to theconventional truck. The favorable torque characteristic of the electric motorcompensates for the delay in the diesel engine response caused by turbo lag.Simulation of the vehicle over a complete urban driving cycle showed that thefuel economy could be improved by 20-30% over traditional (non-hybrid)trucks. It was found that the dynamic programming algorithm achieves higheroverall fuel economy despite the fact that its engine may often operate in aless efficient region than the one controlled by the rule-based algorithm. Thisfact illustrates the importance of coordinating multiple inputs, which may notbe captured by simple engineering intuition. In other words, in some instancesit was more effective to sacrifice some of the engine efficiency, and benefit