(Model Base Design)基于模型的设计

美国大学CS专业十三大研究方向美国大学CS专业的研究分支也超级多，不同分支对学生的要求也会不同，因此，学生们要依照自己的条件选择适合自己的研究方向。

一、体系结构、编译器和并行计算 Architecture, Compilers and Parallel Computing 体系结构和编译器的研究要紧集中在硬件设计，编程语言和下一代编译器。

并行计算研究的包括范围很广，包括并行计算的计算模型，并行算法，并行编译器设计等。

二、系统与网络 Systems and Networking可细分为：(1)网络与散布式系统(Networking and distributed systems)：移动通信系统，无线网络协议(wireless protocols)，Ad-hoc网络，效劳质量治理(Quality of Service management，QoS)，多媒体网络，运算机对等联网(peer-to-peer networking, P2P)，路由，网络模拟，主动队列治理(active queue management, AQM)和传感器网络(sensor networks)。

(2)操作系统(Operating system)：散布式资源治理，普适计算(ubiquitous computing/pervasive computing)环境治理，反射中间件(reflective middleware)，中间件元级操作系统(middleware “meta-operating systems”)，面向对象操作系统设计，许诺单个用户与多运算机、对等操作系统效劳交互的用户设计，上下文灵敏的散布式文件系统，数据中心的电源治理，文件/存储系统，自主计算(autonomic computing)，软件健壮性的系统支持和数据库的系统支持。

(3)平安(Security): 隐私，普适计算，无线传感器(wireless sensors)，移动式和嵌入式运算机，标准，认证，验证策略，QoS保证和拒绝效劳爱惜，下一代通信，操作系统虚拟化和认证，关键基础设施系统，例如SCADA操纵系统和医疗，消息系统，平安网关，可用性平安。

基于模型的设计基于模型的设计 (Model-Based Design, MBD) 是一种软件开发方法，通过使用模型来设计、构建和验证系统。

这些模型可以是数学模型、物理模型或计算机模型，用于描述和预测系统的行为。

基于模型的设计可以应用于各种领域，包括航天、汽车、医疗设备和工业自动化等。

基于模型的设计方法的核心思想是使用模型来代替传统的手动编程方法。

通过使用模型，工程师可以更容易地描述系统的功能和行为，并可以通过仿真和验证来检查设计的正确性。

这减少了错误，加快了开发周期，并提高了系统的可靠性。

2.模型验证：一旦模型创建完成，就可以使用仿真来验证模型的正确性。

通过在模型中输入不同的输入和参数，可以模拟系统的行为，并观察系统的响应。

这允许工程师在实际系统构建之前检查模型的正确性和性能。

3.代码生成：一旦模型验证通过，就可以通过代码生成工具将模型转换成可执行的代码。

这些代码可以是C、C++或其他编程语言。

这些代码可以直接用于系统的实现和部署，减少了手动编程的工作量和错误。

4.部署和测试：生成的代码可以在目标硬件上进行部署和测试。

这包括将代码编译和链接到目标硬件上，然后在硬件上进行测试。

通过在系统的实际硬件上进行测试，可以验证系统的功能和性能，并及时发现和修复问题。

1.提高开发效率：通过使用模型，开发人员可以更快地设计、构建和验证系统。

模型的可视化表示方式使得系统的开发更直观和易于理解。

此外，模型的重复使用性使得开发人员可以更快地修改和更新系统。

2.提高系统可靠性：通过使用模型进行验证和测试，可以减少系统中的错误和缺陷。

模型的仿真和验证功能可以帮助工程师在实际系统构建之前发现和解决问题。

这减少了开发过程中的返工和修复工作。

3.简化系统维护：基于模型的设计方法使得系统的维护更加简单。

通过模型的重复使用性，开发人员可以更容易地理解和修改系统。

此外，生成的代码是自动生成的，减少了手动编程的错误和困难。

基于模型的设计方法在多个领域得到了广泛应用。

mbd的名词解释MBD，即Model-Based Design（基于模型的设计），是一种在工程领域广泛应用的设计方法论。

它以模型为中心，通过建立和分析系统的数学模型，实现复杂系统的设计、开发和验证。

MBD集成了建模、仿真、代码生成和自动化测试等环节，帮助工程师在系统设计过程中提高生产力和质量。

本文将对MBD的定义、特点以及应用领域进行解释和分析。

一、MBD的定义MBD可被描述为一种综合利用计算机模型来进行设计和开发的方法。

传统的设计方法往往需要用户手动编写代码，并在实际系统中进行验证。

而MBD则通过使用数学模型来描述系统，代替了繁琐的手写代码过程，从而提高了设计效率和准确性。

MBD常常在各种工程领域中使用，如电子、汽车、航空航天等，以及工业自动化和控制系统等领域。

二、MBD的特点1. 模型驱动：MBD建立在数学模型的基础上，通过建模和仿真来实现系统设计的目标。

用户可以使用各种建模工具，如Simulink等，来构建系统的数学模型，然后进行仿真和验证。

这种模型驱动的设计方法使得工程师能够更加直观地理解系统的行为。

2. 自动化代码生成：MBD不仅可以用于系统设计和仿真，还可以通过自动化代码生成实现系统的实际部署。

通过将数学模型转换为可执行代码，MBD能够大大减少手动编写代码的工作量，确保代码的正确性和一致性。

此外，MBD还可以生成可嵌入式系统使用的代码，如控制器、传感器等，进一步简化系统开发和集成。

3. 紧密集成的工具链：MBD包含了建模、仿真、代码生成和测试等环节，这些环节在一个统一的开发环境中紧密集成。

工程师可以在同一个界面下完成整个设计过程，无需在不同工具之间频繁切换，从而提高了工作效率和可行性。

此外，MBD还支持多人协同工作，使得团队成员之间能够更好地进行沟通和合作。

三、MBD的应用领域1. 汽车工程：MBD在汽车工程领域的应用非常广泛。

通过建立车辆动力学模型，设计师可以对整车性能进行分析和优化，如加速度、转向和刹车等。

依据MBD技术的船舶数据集定义与标注方法MBD技术（Model-Based Design，基于模型的设计）是一种通过使用实时仿真模型进行系统设计、开发和验证的方法。

在船舶领域，MBD技术可以用于船舶数据集的定义和标注方法。

船舶数据集的定义是指确定需要收集和记录的船舶相关数据的过程。

在MBD技术中，船舶数据集的定义是基于船舶设计和性能要求的。

这些数据可以包括船舶的尺寸、重量、稳性参数、推进系统特性、船舶航行性能等。

通过定义船舶数据集，可以为船舶设计和仿真提供必要的输入和验证。

船舶数据集的标注方法是指对收集到的船舶数据进行注释和标记的过程。

在MBD技术中，船舶数据集的标注方法可以包括以下几个方面：1.标注船舶性能参数：船舶性能参数是船舶数据集中最关键的部分之一、这包括标注船舶的速度、加速度、舵角、推力等信息。

通过标注这些参数，可以准确地模拟船舶在不同情况下的性能。

2.标注船舶航行状态：船舶航行状态包括船舶的位置、航向和姿态等信息。

这些信息是进行船舶运动模拟和控制的基础。

通过标注船舶航行状态，可以为仿真模型提供准确的输入和实时的反馈。

3.标注船舶传感器数据：船舶传感器数据是指通过安装在船舶上的传感器收集到的物理量数据，如温度、压力、湿度等。

通过标注船舶传感器数据，可以为船舶的监测和控制提供实时的信息。

4.标注船舶环境数据：船舶环境数据是指船舶所处环境的相关数据，如海洋环境参数、风速、海浪等。

通过标注船舶环境数据，可以为船舶的设计和运行提供参考和验证。

5.标注船舶系统数据：船舶系统数据是指与船舶相关的系统数据，如推进系统的功率、燃料消耗等。

通过标注船舶系统数据，可以对船舶的性能和效率进行评估和优化。

在使用MBD技术进行船舶数据集的定义和标注时，需要注意以下几个方面：1.数据质量：确保数据的准确性和可靠性，避免数据的误差和偏差对模型和仿真结果的影响。

2.数据一致性：保持数据的一致性，确保各种数据的衔接和关联，以提高模型的准确性和可靠性。

Software Development •软件开发Electronic Technology & Software Engineering 电子技术与软件工程• 57【关键词】基于模型的设计 嵌入式软件 控制策略 开发1 引言MBD ，全称为Model Based Design ，即基于模型的设计。

在汽车电子所属的嵌入式软件开发领域，MBD 技术通常指采用图形化建模并仿真，进而将模型自动生成代码的技术。

相比传统嵌入式软件开发方法，由于MBD 技术具有需求可追溯、建模图形化、自动生成代码、快开发周期、方便并行开发等诸多特点，其近年来受到了越来多越多的重视。

特别是基于Matlab/Simulink 平台的MBD 嵌入式软件开发方法，由于其应用广泛，逐渐成为一种趋势。

本文基于MBD 开发方法与传统开发方法对比，分析和研究MBD 技术在汽车嵌入式软件开发领域中的导入方法，以及要解决的一些关键问题。

2 基于MBD技术的嵌入式软件开发方法与传统方法对比2.1 传统嵌入式软件开发方法及其存在的问题如图1所示，传统嵌入式软件开采用自上而下的瀑布式流程，即下阶段工作开展依赖于上阶段工作的完成情况，上阶段工作为下阶段工作提供基础。

这种开发流程一个显而易见的问题，就是不允许并行开发，并且如果在某阶段发现错误或需求变更，极有可能引起耗时较长的大规模软件更新甚至整个软件重写，从而拉长项目周期，并为项目本身带来诸多的不确定性。

其次，在传统的开发手段中，在不同阶段彼此之间传递的信息需要依赖文档，例如需求分析报告、系统详细设计规范、设计任务书、设计报告等。

由于工程人员总会存在针对文字基于模型的电控嵌入式软件开发方法文/宋炳雨 陈娜娜 何晓明 李峰理解的二义性问题，因此即便是文档本身没有错误，可能也会由于理解上的误差而引起系统设计、实现的错误。

2.2 MBD开发方法的优势基于MBD 技术的嵌入式软件开发采用V 形开发流程。

mbd测试验证流程下载温馨提示:该文档是我店铺精心编制而成，希望大家下载以后，能够帮助大家解决实际的问题。

文档下载后可定制随意修改，请根据实际需要进行相应的调整和使用，谢谢!并且，本店铺为大家提供各种各样类型的实用资料，如教育随笔、日记赏析、句子摘抄、古诗大全、经典美文、话题作文、工作总结、词语解析、文案摘录、其他资料等等，如想了解不同资料格式和写法，敬请关注!Download tips: This document is carefully compiled by theeditor. I hope that after you download them,they can help yousolve practical problems. The document can be customized andmodified after downloading,please adjust and use it according toactual needs, thank you!In addition, our shop provides you with various types ofpractical materials,such as educational essays, diaryappreciation,sentence excerpts,ancient poems,classic articles,topic composition,work summary,word parsing,copy excerpts,other materials and so on,want to know different data formats andwriting methods,please pay attention!MBD（Model-Based Design，基于模型的设计）测试验证流程是一种基于模型的软件开发方法，它将系统设计和测试验证紧密结合，通过对模型的验证和测试来确保系统的正确性和可靠性。

2008-01-0085Model-Based Design for Hybrid Electric Vehicle SystemsSaurabh Mahapatra, Tom Egel, Raahul Hassan, Rohit Shenoy, Michael Carone Copyright © 2008 The MathWorks, Inc.ABSTRACTIn this paper, we show how Model-Based Design can be applied in the development of a hybrid electric vehicle system. The paper explains how Model-Based Design begins with defining the design requirements that can be traced throughout the development process. This leads to the development of component models of the physical system, such as the power distribution system and mechanical driveline. We also show the development of an energy management strategy for several modes of operation including the full electric, hybrid, and combustion engine modes. Finally, we show how an integrated environment facilitates the combination of various subsystems and enables engineers to verify that overall performance meets the desired requirements. 1. INTRODUCTIONIn recent years, research in hybrid electric vehicle (HEV) development has focused on various aspects of design, such as component architecture, engine efficiency, reduced fuel emissions, materials for lighter components, power electronics, efficient motors, and high-power density batteries. Increasing fuel economy and minimizing the harmful effects of the automobile on the environment have been the primary motivations driving innovation in these areas.Governmental regulation around the world has become more stringent, requiring lower emissions for automobiles (particularly U.S. EPA Tier 2 Bin 5, followed by Euro 5). Engineers now must create designs that meet those requirements without incurring significant increases in cost. According to the 2007 SAE’s DuPont Engineering survey, automotive engineers feel that cost reduction and fuel efficiency pressures dominate their work life [1] and will continue to play an important role in their future development work.In this paper, we explore key aspects of hybrid electric vehicle design and outline how Model-Based Design can offer an efficient solution to some of the key issues. Due to the limited scope of the paper, we do not expect to solve the problem in totality or offer an optimal design solution. Instead, we offer examples that will illustrateThe MathWorks, Inc.the potential benefits of using Model-Based Design in the engineering workflow. Traditionally, Model-Based Design has been used primarily for controller development.One of the goals of this paper is to show how Model-Based Design can be used throughout the entire system design process.In section 2, we offer a short primer on HEVs and the various aspects of the design. Section 3 is devoted to Model-Based Design and the applicability of the approach to HEV development. Sections 4, 5, and 6 will focus on examples of using Model-Based Design in a typical HEV design.2. HYBRID ELECTRIC VEHICLE DESIGNCONCEPTA block diagram of one possible hybrid electric vehicle architecture is shown in Figure1. The arrows represent possible power flows. Designs can also include a generator that is placed between the power splitter and the battery allowing excess energy to flow back into thebattery.Figure 1: The main components of a hybrid electric vehicle.Conceptually, the hybrid electric vehicle has characteristics of both the electric vehicle and the ICE (Internal Combustion Engine) vehicle. At low speeds, it operates as an electric vehicle with the battery supplying the drive power. At higher speeds, the engine and the battery work together to meet the drive power demand. The sharing and the distribution of power between thesetwo sources are key determinants of fuel efficiency. Note that there are many other possible designs given the many ways that power sources can work together to meet total demand.DESIGN CONSIDERATIONSThe key issues in HEV design [2] are typical of classical engineering problems that involve multilayer, multidomain complexity with tradeoffs. Here, we discuss briefly the key aspects of the component design: very similar to those of a traditional ICE. Engines used in an HEV are typically smaller than that of a conventional vehicle of the same size and the size selected will depend on the total power needs of the vehicle.design are capacity, discharge characteristics and safety. Traditionally, a higher capacity is associated with increase in size and weight. Discharge characteristics determine the dynamic response of electrical components to extract or supply energy to the battery. motors, AC induction motors, or Permanent Magnet Synchronous Motors (PMSM). Each motor has advantages and disadvantages that determine its suitability for a particular application. In this list, the PMSM has the highest power density and the DC motor has the lowest. [3].splitter that allows power flows from the two power sources to the driveshaft. The engine is typically connected to the sun gear while the motor is connected to the ring gear.aerodynamic drag interactions with weight and gradability factors accounted for in the equations.process of the hybrid powertrain is to study the maximum torque demand of the vehicle as a function of the vehicle speed. A typical graph is shown in Figure 2. Ratings of the motor and the engine are determined iteratively to satisfy performance criteria and constraints. The acceleration capabilities are determined by the peak power output of the motor while the engine delivers the power for cruising at rated velocity, assuming that the battery energy is limited. Power sources are coupled to supply power by the power-splitter, and the gear ratio of the power-splitter is determined in tandem. The next steps include developing efficient management strategies for these power sources to optimize fuel economy and designing the controllers. The final steps focus on optimizing the performance of this system under a variety of operating conditions.Figure 2: Maximum torque demand as a function of vehicle tire speed.3. MODEL-BASED DESIGN OF AN HEVMOTIVATIONIn this section, we outline some of the challenges associated with HEV design and explain the motivation for using Model-Based Design as a viable approach for solving this problem.of an HEV design problem is reflected in the large number of variables involved and the complex nonlinear relationships between them. Analytical solutions to this problem require advanced modeling capabilities and robust computational methods.set of requirements to meet the vehicle performance and functionality goals. Requirements refinement proceeds iteratively and depends on implementation costs and equipment availability.conceptualize the operation of the system’s various components and understand the complex interactions between them. This often requires experimentation with various system topologies. For example, studies may include comparing a series configuration with a parallel configuration. Because the goal is a better understanding of the overallsystem behavior, the models must include the appropriate level of detail. system level to a more detailed implementation, engineers elaborate the subsystem models to realize the complete detailed system model. This can be accomplished by replacing each initial model of a component with the detailed model and observing the effects on performance. Completing this process andrealizing a detailed model of the system requires robust algorithms for solving complex mathematics in a timely fashion.and mechanical components. Typically these components are designed by domain specialists. To speed development, these engineers need to effectively communicate and exchange design ideas with a minimum of ambiguity.typical HEV design is to increase the fuel efficiency of the vehicle while maintaining performance demands. Intuitively, one can look at this problem as finding the optimal use of the power sources as a function of the vehicle internal states, inputs, and outputs satisfying various constraints. This translates to the requirement for switching between various operational “power modes” of the vehicle as a func tion of the states, inputs, and measured outputs [4]. In a true environment for Model-Based Design the power management algorithms co-exist with the physical system models.complexity of the various subsystems, HEV controller design is typically a complex task. A variety of control algorithms specific to each subsystem may be required. For example, the controller that manages the frequency of the input voltage to the synchronous motor will be different from the simple control used for torque control of the same motor. Typically, this will manifest itself as a multistage, multiloop control problem. Successful implementation of the controllers requires deployment of these algorithms on processors that are integrated while interfacing with the physical plant. testing ensures that it continues to meet requirements. Detection of errors early in the process helps reduce costs associated with faulty designs. As design errors trickle down the various workflow stages the costs associated with correcting them increase rapidly[5]. The ability to continually verify and validate that requirements are being satisfied isa key aspect of Model-Based Design.A software development environment for Model-Based Design must be able to address the aforementioned challenges. Additionally, a single integrated environment facilitates model sharing between team members. The ability to create models at various levels of abstraction is needed to optimize the simulation time. A mechanism for accelerating the simulation as the complexity increases will also be important. PROCESS OF MODEL-BASED DESIGNModel-Based Design can be thought of as a process of continually elaborating simulation models in order to verify the system performance. The overall goal is to ensure first pass success when building the physicalprototype. Figure 3 shows the key elements of Model-Based Design.The system model forms the “executable specification” that is used to communicate the desired system performance. This model is handed over to the various specialists who use simulation to design and further elaborate the subsystem models. These specialists refine the requirements further by adding details or modifying them. The detailed models are then integrated back into the system level realization piece by piece and verified through simulation. This goes on iteratively until a convergence to an optimal design that best meets the requirements results. During Model-Based Design, C-code generation becomes an essential tool for verifying the system model. The control algorithm model can be automatically converted to code and quickly deployed to the target processor for immediate testing. Code can also be generated for the physical system to accelerate the simulation and/or to test the controller with Hardwarein the Loop simulation.Figure 3: The key elements of Model-Based Design.4. SYSTEM LEVEL MODELING OF AN HEVIn the first stage of the HEV design, the system-level description of the system is realized. Experimentation enables the system designer to explore innovative solutions in the design space resulting in optimal architectures. Our approach has been inspired by an earlier SAE paper [6]. REQUIREMENTSIn the initial stages of the project, it is not uncommon for the specifications of subsystem components to shift. The requirements are in a preliminary form, and are based on previous designs, if available, or best engineering judgment. Requirements are refined when each of the component models is delivered to component designers for additional refinement. There are, however, certain requirements that the system architect understands fully, and can lock down. As the project moves from requirements gathering to specification, the concepts of the system architects can be included in the model. Collaboration between architects and designers leads to a much better and more complete specification. The system can be expressed as a series of separate models that are to be aggregated into an overall system model for testing. Breaking down the model into components facilitates component-based development and allows several teams to work on individual components in parallel. This kind of concurrent development was facilitated by the parallel configuration we chose for our example, in which the electrical and mechanical power sources supply power in parallel. The broad design goals were:Improve fuel efficiency to consume less than 6.5liters per 100 km (L/100 km) for the driver input profile shown in Figure 4.Cover a quarter mile in 25 seconds from rest. Attain a top speed of 193 kph.Figure 4: Driver input profile as outlined in the requirements document.These and other such requirements are typically captured in a requirements document that engineers can associate with the design models. This provides the ability to trace the requirements throughout the model, a key component of Model-Based Design. VEHICLE DYNAMICSModeling the vehicle dynamics can be a challenging task. When creating any simulation model it is important to consider only the effects that are needed to solve the problem at hand. Superfluous details in a model will only slow down the simulation while providing little or noadditional benefit. Because we are primarily interested in the drive cycle performance, we will limit our vehicle model to longitudinal dynamics only. For example, the vehicle was initially modeled as a simple inertial load on a rotating shaft connected to the drive train. ENGINEA complete engine model with a full combustion cycle is also too detailed for this application. Instead, we need a simpler model that provides the torque output for a given throttle command. Using Simulink® and SimDriveLine™, we modeled a 57kW engine with maximum power delivery at 523 radians per second, as shown in Figure5.Figure 5: Engine modeled using blocks from the SimDriveline™ library. SYNCHRONOUS MOTOR/GENERATORThe synchronous motor and generator present an interesting example of electromechanical system modeling. Standard techniques for modeling synchronous machines typically require complex analysis of equations involving electrical and mechanical domains. Because the input source to this machine drive is a DC battery and the output is AC, this would require the creation of complex machine drive and controller designs – often a significant challenge at this stage.An averaged model that mathematically relates the control voltage input with the output torque and resulting speed is a useful alternative. This simplification allows us to focus on the overall behavior of this subsystem without having to worry about the inner workings. Furthermore, we can eliminate the machine drive by simply feeding the DC voltage directly to this subsystem. With this averaged model, we only need a simple Proportional-Integral (PI) controller to ensure effective torque control. TheMotor/Generator subsystem design will be explored in more detail in the next section. POWER-SPLITTERThe power-splitter component is modeled as a simple planetary gear, as shown in Figure 6. With these building blocks, more complex gear topologies can easily be constructed and tested within the overall system model.Figure 6: Power-splitter modeled as a planetary gear with connections.POWER MANAGEMENTThe power management subsystem plays a critical role in fuel efficiency.The subsystem has three main components:• Mode logic that manages the various operatingmodes of the vehicle.• An energy computation block that computes theenergy required to be delivered by the engine, the motor, or both in response to gas pedal input at any given speed.• An engine controller that ensures the engine is theprimary source of power and provides most of the torque. The motor and generator controllers provide torque and speed control.MODE LOGICFor efficient power management, an understanding of the economics of managing the power flow in the system is required. For example, during deceleration, the kinetic energy of the wheels can be partially converted to electrical energy and stored in the batteries. This implies that the system must be able to operate in different modes to allow the most efficient use of the power sources.We used the conceptual framework shown in Figure 7 to visualize the various power management modes.Algorithm design starts with a broad understanding of the various possible operating modes of the system. In our example, we identified four modes—low speed/start, acceleration, cruising, and braking modes. For each of these modes, we determined which of the power sources should be on and which should be off.The conceptual framework of the mode logic is easily implemented as statechart. Statecharts enable the algorithm designer to communicate the logic in an intuitive, readable form.Figure 7: Mode logic conceptualized for the hybrid vehicle.The Stateflow® chart shown in Figure 8 is a realization of the conceptual framework shown in Figure 7. While very similar to the conceptual framework, the Stateflow chart has two notable differences. The “acceleration” and “cruise” states have been grouped to form the “normal” superstate, and the “low speed/start” and “normal” states have been grouped together to form the “motion” superstate. This grou ping helps organize the mode logic into a hierarchical structure that is simpler to visualize and debug.Figure 8: Mode logic modeled with Stateflow®. SYSTEM REALIZATIONAfter the HEV components have been designed, they can be assembled to form the parallel hybrid system shown in Figure9.Figure 9: System-level model of the parallel HEV.This system model can then be simulated to determine if the vehicle meets the desired performance criteria over different drive cycles. As an example, for the input to the system shown in Figure 4, the corresponding speed and the liters per 100 km (L/100 km) outputs are shown in Figure 10. Once the baseline system performance has been evaluated using the system model, we begin the process of model elaboration. In this process, we add more details to the subsystems models to make them more closely represent the actual implementation. During this process, design alternatives can be explored and decisions made based on the analysis results. This is a highly iterative process that is accelerated using Model-Based Design.5. MODEL ELABORATIONIn the model elaboration stage, the subsystem components undergo elaboration in parallel with requirements refinement.A subsystem block is an executable specification because it can be used to verify that the detailed model meets the original set of requirements.As an example, we show how the generator machine drive undergoes requirements refinement and model elaboration. We assume that the engineer responsible for themachine drive design will carry out the model elaboration of the plant and the associated controller.REQUIREMENTS REFINEMENTThe machine drive is an aggregated model of the machine and the power electronics drive. In the system level modeling phase, the key specification is the torque-speed relationship and the power loss. This information was sufficient to define an abstract model to meet the high-level conceptual requirements.Figure 10: Output speed and L/100 km metric for the averaged model.As additional design details are specified, the model must become more detailed to satisfy the subsystem requirements. For example, the generator model will need parameters such as the machine circuit equivalent values for resistance and inductance. Engineers can use this specification as the starting point towards the construction of an electric machine customized for this HEV application.In the case of the generator drive, as the machine model is elaborated from an averaged model to a full three phase synchronous machine implementation, the controller must also be elaborated. PLANT ELABORATIONThe machine model for the synchronous generator is elaborated using SimPowerSystems™ blocks that represent detailed models of power system components and drives. For this model, the electrical and mechanical parts of the machine are each represented by a second-order state-space model. Additionally, the internal flux distribution can be either sinusoidal or trapezoidal. This level of modeling detail is needed to make design decisions as the elaboration process progresses.Figure 11: Detailed PMSM model parameters.The details of this model are captured in the model parameters shown in Figure 11, which specify the effects of internal electrical and magnetic structures.CONTROL ELABORATIONThe controller used in the averaged model of the AC machine drive is a simple PI controller. In model elaboration of the synchronous machine plant, a DC battery source supplies energy to the AC synchronous machine via an inverter circuit that converts DC to AC. These changes in plant model structure and detail require appropriate changes to the controller model to handle different control inputs and implement a new strategy. For example, the power flow to the synchronous machine is controlled by the switching control of the three phase inverter circuit. This added complexity was not present in the initial model of the machine drive because we focused on its behavior rather than its structure. We implemented a sophisticated control strategy, shown in Figure 12, that included cascaded speed and vector controllers [7]. The controllers were developed using Simulink® Control Design™ to satisfy stability and performance requirements.VERIFICATION AND VALIDATIONAt every step of the model elaboration process, the model is verified and validated. Figure 13 shows the averaged and detailed models as they are tested in parallel.Figure 12: Controller elaboration as we move from averaged (top) to detailed (below) model.The test case is a 110 radians per second step input to the machine. The response, shown in Figure 14, reveals comparable performance of both models. This serves as a visual validation that the detailed model is performing as desired. More elaborate testing schemes and formal methods can be devised with test case generation and error detection using assertion blocks from Simulink® Verification and Validation™ [8].Figure 13: Testing of the averaged and the detailed models for speed control with a 1000 rpm step input.SYSTEM INTEGRATIONAfter the component model elaboration and testing is complete, the subsystem containing the averaged model is replaced with the detailed model and the overall system is simulated again.Figure 14: Comparison between the averaged andthe detailed models of the machine drive. This integration will proceed, one component at a time, until the overall system level model contains all the detailed models for each component. This ensures each component is tested and verified in the system model. A single modeling environment for multidomain modeling facilitates the integration. In our example, we used Simulink for this purpose. In Figure 15, we compare the results of the averaged and the detailed models for the driver input profile shown in Figure 4. The detailed model shows deterioration in the speed and L/100 km performance metrics, which can be attributed to the additional detail incorporated into the model.4. CONTROLLER DEPLOYMENTThe electronic control unit (ECU) layout, deployment, and implementation are challenging problems that require innovative thinking. Typically, this requires exploration of the design space to optimize various criteria.Once the design of the system controllers is complete, ECU layout strategy must be considered. In a typical vehicle, we would likely keep some of the controllers inside a centralized ECU, while distributing the others throughout the car.One potential layout would implement the controller for the synchronous motor on a dedicated floating point microcontroller situated closer to the machine, instead of incorporating the controller as part of the centralized ECU. Such a strategy would allow for faster response times from the motor controller for efficient control. If a mix of centralized and distributed controller architecture is under consideration, then the extra layer of complexity introduced by the communication networkshould be accounted for in the modeling.Figure 15: Speed and L/100 km metric comparisons for averaged and detailed models for the HEV. Cost and performance considerations will drive design decisions regarding the selection of floating point or fixed point implementation of each controller. For example, one may consider implementing the controller for the synchronous generator on a fixed-point processor to lower the cost of the overall architecture.6. SIMULATION PERFORMANCEThe final system-level model of the HEV will contain detailed lower-level models of the various components. As model complexity increases, it will take longer to simulate the model in the software environment. This behavior is expected because the model contains more variables, equations, and added components which incur an additional computational cost. Intuitively, this can be visualized as an inverse relationship between simulation performance and complexity of the model as shown in Figure 16.Running the simulations in a high-performance computing environment can offset the increase in simulation times that comes with increased complexity. . With the advent of faster, multicore processors, it is possible to run large simulations without having to investin supercomputer technology.Figure 16: Simulation performance deteriorates with increasing model complexity. We used Simulink simulation modes that employ code generation technology [9] to accelerate the simulation of our model. The improvements in the simulation performance are shown in Figure 17.Figure 17: Comparison of Simulink® simulation modes for the detailed HEV model.CONCLUSIONIn this paper, we first described a typical HEV design and gave an overview of the key challenges. We discussed how the multidomain complications arise from the complex interaction between various mechanical and electrical components—engine, battery, electric machines, controllers, and vehicle mechanics. This complexity, combined with the large number of subsystem parameters, makes HEV design a formidable engineering problem.We chose Model-Based Design as a viable approach for solving the problem because of its numerous advantages, including the use of a single environment for managing multidomain complexity, the facilitation of iterative modeling, and design elaboration. Continuous validation and verification of requirements throughout the design process reduced errors and development time.Our first step in the development process was the realization of a system-level model of the entire HEV. The subsystem components were averaged models, which underwent model elaboration with requirements refinement and modifications in parallel. We showed how statecharts can be used to visualize the operating modes of the vehicle. After each component model was elaborated, we integrated it into the system-level model, compared simulation results of the averaged and detailed models, and noted the effect of model elaboration on the outputs. When simulation times grew long as we moved towards a fully detailed model, we introduced techniques to alleviate this issue. ACKNOWLEDGMENTSThe authors would like to acknowledge the following fellow MathWorks staff who contributed towards the development of the HEV models used in this paper and the writing of this paper. In alphabetical order—Bill Chou, Craig Buhr, Jason Ghidella, Jeff Wendlandt, Jon Friedman, Rebecca Porter, Rick Hyde, and Steve Miller. REFERENCES1. L. Brooke, “Cost remains the boss”, AutomotiveEngineering International, April 2007, SAE International.2. Iqbal Husain, “Electric and Hybrid Vehicles—DesignFundamentals”, 1st E dition, © 2003 CRC Press.3. S J. Chapman, “Electric Machinery Fundamentals”,4th Edition, © 2004 McGraw-Hill Inc.4. 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