四轮独立驱动电动汽车控制策略的研究
四轮独立驱动电动汽车整车控制策略仿真研究
四轮独立驱动电动汽车整车控制策略仿真研究
王灿
【期刊名称】《成都工业学院学报》
【年(卷),期】2024(27)1
【摘要】基于控制器局域网总线(CAN)对四轮独立驱动电动汽车整车控制策略进行优化。
控制策略采用了将比例积分微分(PID)与模糊算法融合的模糊比例积分微分控制算法,使用CAN总线进行整车控制,经模糊PID控制算法可获得车轮滑转率最优比及电机转动转矩,可有效实现驱动的防滑操作。
并基于Matlab软件建立的整车动力学模型进行政策控制策略的仿真,模糊PID控制算法控制仿真结果表明,调节电机信号输出后,及时进行信号反馈,可形成特定闭环控制系统,实现实际滑转率稳定在最优滑转率附近,并且稳定性高,控制较精准。
在模糊PID控制算法控制下,当转向角较小时,4个轮胎纵向力输出基本相等。
在转向工况下,两转向前轮,尤其是外侧转向轮,则会输出较大侧向力,在不转向工况下,两后轮输出具有较小的侧向力。
在转向工况下,仿真结果在可接受范围内,结果验证了控制策略的合理性。
【总页数】7页(P18-24)
【作者】王灿
【作者单位】泉州师范学院交通与航海学院
【正文语种】中文
【中图分类】TP391.9
【相关文献】
1.轮毂驱动电动汽车整车操纵稳定性仿真研究
2.基于模糊控制的后轮独立驱动纯电动汽车驱动控制策略研究
3.前后轴独立驱动的增程式电动汽车整车控制策略
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四轮独立驱动电动汽车的电子系统研究
四轮独立驱动电动汽车的电子系统研究一、概述随着科技的飞速发展和人们对环保出行的日益关注，电动汽车作为新能源汽车的代表，逐渐成为了未来汽车产业的发展趋势。
四轮独立驱动电动汽车，作为一种创新的电动汽车类型，以其独特的驱动方式和优秀的性能表现，受到了广泛的关注和研究。
四轮独立驱动电动汽车的核心在于其电子系统，该系统集成了先进的电机控制、能量管理、车辆稳定性控制等多项技术，为车辆提供了高效、稳定且安全的动力输出。
对四轮独立驱动电动汽车的电子系统进行深入研究，不仅有助于提升车辆的性能和品质，还能够推动电动汽车技术的进一步发展。
本文旨在全面探讨四轮独立驱动电动汽车的电子系统，包括其结构组成、工作原理、控制策略以及在实际应用中的表现等方面。
通过深入研究和分析，以期为解决当前电动汽车领域面临的挑战和问题提供有益的参考和启示。
同时，本文还将关注电子系统的创新点和发展趋势，为电动汽车产业的可持续发展贡献力量。
1. 四轮独立驱动电动汽车的发展背景及意义随着全球能源结构的转型和环境保护意识的日益增强，电动汽车作为新能源汽车的代表，正逐渐取代传统燃油汽车，成为汽车工业发展的主流趋势。
四轮独立驱动电动汽车以其独特的驱动方式和优越的性能，成为了当前研究的热点。
四轮独立驱动电动汽车，即每个车轮都配备独立的驱动电机，通过电子控制系统实现对各车轮的精确控制。
这种驱动方式不仅简化了汽车的传动系统，提高了传动效率，而且能够实时调整各车轮的驱动力和制动力，从而优化车辆的操控性能和行驶稳定性。
从发展背景来看，四轮独立驱动电动汽车的兴起，既是汽车工业技术进步的必然结果，也是应对能源危机和环境污染挑战的重要举措。
随着电池技术的不断进步和电机控制技术的日益成熟，四轮独立驱动电动汽车的续航里程和性能得到了显著提升，使得其商业化应用成为可能。
从意义上看，四轮独立驱动电动汽车的研究和推广，对于推动汽车工业的绿色发展和可持续发展具有重要意义。
它能够有效降低汽车对石油资源的依赖，减少尾气排放，从而缓解能源危机和环境污染问题。
四轮独驱电动汽车操纵稳定性控制策略进展概述
四轮独驱电动汽车操纵稳定性控制策略进展概述作者：文/ 郑萌来源：《时代汽车》 2020年第21期郑萌中国汽车工程研究院股份有限公司重庆市 401120摘要：本文主要针对四轮独驱电动汽车操纵稳定性控制策略的研究进展进行综述，综述内容主要分为两个部分，第一部分是操纵稳定性的简要介绍，第二部分是控制理论及其研究进展，其中包含了目前已得出的研究成果，第三部分是汽车操纵稳定性与其他性能的协同控制策略研究进展。
本文通过综述的方式对研究进展进行简要的概述，希望能对相关研究人员提供参考帮助。
关键词：四轮独驱电动汽车操纵稳定性Overview of the Development of Control Strategies for Handling and Stability of Four-wheel Single-Drive Electric VehiclesZheng MengAbstract：This article mainly summarizes the research progress of the control strategy for the handling and stability of four-wheel single-drive electric vehicles. The review is mainly divided into two parts. The first part is a brief introduction to the handling stability, and the second part is the control theory and its research. Progress includes the research results that have been obtained so far. The third part is the research progress of collaborative control strategiesfor vehicle handling stability and other performance. This article provides a brief overview of the research progress by means of a review, hoping to provide reference for relevant researchers.Key words：four-wheel single-drive, electric vehicle, handling stability因全球变暖的背景原因，我国也开展了有关新能源汽车的研究，目前针对新能源汽车的研究已经取得了相对较为显著的成果，主要体现在电动汽车方面，电动汽车作为全新科技产物不仅代表了新能源汽车的发展进步，同时因电动汽车当中也具备着多种驱动方式能够进行精准有效的控制，比如4WID-EV的类型就简化了原本的传动结构，归类为过驱动系统，以此来确保4WID-EV型号电动汽车的四轮独驱拥有良好的机动性和操作稳定性。
基于CarSim和Matlab四轮独立驱动轮毂电机电动汽车驱动控制系统的研究
基于CarSim和Matlab四轮独立驱动轮毂电机电动汽车驱动控制系统的研究作者：梅鸣来源：《山东工业技术》2016年第21期摘要：针对四轮独立驱动轮毂电机电动汽车驱动控制系统进行了建模与仿真，在传统PID 的基础上引入SOA智能优化算法，最后验证了所建立的CarSim和Matlab车辆模型的合理性。
关键词：电动汽车；驱动控制系统；车辆模型；SOA智能优化算法DOI：10.16640/ki.37-1222/t.2016.21.1630 引言近年来，绿色环保与可持续发展成为日益重要的发展理念。
本文研究的轮毂电机驱动电动汽车在现有商用化电动汽车的基础上省略了减速器、差速器和传动轴等机械零部件部件，直接由整车控制器发出控制信号直接控制车轮，这样节省车内空间，更容易实现电动车的微型化、轻量化[1-2]。
本文将CarSim中的内燃机模型和传动系统模型，修改为毂电机模型，在Matlab/Simulink中搭建电机模型和控制系统模块，在联合CarSim进行联合仿真。
1 四轮轮毂电机电动汽车建模在Matlab/Simulink中搭建轮毂电机模型，去掉CarSim中的传统内燃机汽车模型，通过Matlab/Simulink和CarSim联合仿真，搭建出四轮独立驱动轮毂电机电动汽车整车模型。
1.1 轮毂电机建模轮毂电机无刷直流电机，其主要由电机本体、霍尔位置传感器和电子逆变器构成。
无刷直流电机数学模型形式可表示为：其中ea，eb，ec分别表示定子a，b，c三相生成的梯形反电动势。
电磁转矩方程为：式中：Te为电磁转矩；w为电机角速度；Tl为负载转矩；J为转动惯量；B为黏滞摩擦系数；ua，ub，uc为绕组电压，ia， ib，ic为相电流；ea，eb，ec为相反电势；L为相绕组自感系数；M为相绕组互感系数。
式（1）、式（2）和式（3）共同构成了无刷直流电机的微分方程数学模型。
采用基于SOA的PID控制算法来控制轮毂电机，1.2 整车模型搭建打开CarSim 8.02 软件，选择B-Class， Hatchback选项作为基准车辆，将CarSim中原有的内燃机模型改为 4-wheel drive（四轮驱动），其内容定义为选择No dataset select方式，同时将四轮驱动转矩设置为车辆模型的输入量变量。
四轮独驱电动汽车操纵稳定性控制策略进展概述
0引言随着全球变暖以及石油资源短缺，新能源汽车的呼声日益提升，越来越多的专家学者致力于新能源汽车的研究之中。
电动汽车作为新能源汽车中较为突出的一种，发展势头尤为迅猛。
电动汽车有多种驱动方式，其中4WID-EV 因其各轮均独立，驱动力在理论上可以得到精准地控制，从而将其视为研究对象的人也是最多。
四轮独驱简化了传动结构，是一个过驱动系统，为了保证4WID-EV在具有极佳机动性的情况下同时具有优异的操纵稳定性，需要对其控制策略深入研究。
目前，基于传统车辆操纵稳定性的控制策略有滑模控制、线控转向控制、直接横摆矩控制、差动助力转向控制、电子控制等，这些控制策略也在实车中进行过验证并表明控制效果很好。
但针对4WID-EV来说，其车辆布置结构已发生巨大变化，大部分控制策略效果表现不佳，故此，大量专家学者正致力于开发一些新的控制策略。
1操纵稳定性操纵稳定性是车辆中一种极为重要的性能，操纵稳定性的好坏直接与驾驶安全相关联。
操纵稳定性包括稳定性和操纵性，常用的评价内容有轮胎侧偏特性、转向特性等。
在试验方面，为测试其操纵稳定性性能状况，需对车辆进行线性、非线性和转向盘中间位置操纵稳定性三方面进行测试[1]。
2控制理论及其研究状况4WID-EV作为一种分布式驱动电动汽车，极大地简化传动系统，同时能效方面也十分突出。
目前，越来越多的专家学者致力于4WID-EV控制策略的研究研究当中，其控制策略也在逐步快速更新，控制效果正在不断优化。
2.1直接横摆力矩控制（DYC）DYC是通过对每个车轮的转矩进行合理控制，从而达到车辆稳定行驶的需求，DYC自从20世纪90年代被提出以来，因其控制方式简单、精度高、响应快等诸多优点，已在电动汽车控制领域得到广泛应用[1]。
DYC主要包括车辆状态参数估计、决策需求横摆力矩和横摆力矩分配问题[2]。
目前，大量专家学者在这方面做了巨大贡献。
史培龙[2]等人基于PID和模糊逻辑控制（FLC）设计了一个DYC控制器，并针对FSAE纯电动赛车进行双移线工况的实车试验。
线控四轮独立驱动轮毂电机电动汽车稳定性与节能控制研究
线控四轮独立驱动轮毂电机电动汽车稳定性与节能控制研究一、本文概述随着环保意识的日益增强和新能源汽车技术的迅速发展，电动汽车（EV）在全球范围内正逐步成为新的交通出行选择。
特别是线控四轮独立驱动轮毂电机电动汽车（以下简称轮毂电机电动汽车），其独特的驱动方式和控制策略使得车辆性能优化成为可能。
然而，这类电动汽车在稳定性和节能性方面仍面临诸多挑战。
因此，本文旨在深入研究轮毂电机电动汽车的稳定性和节能控制策略，以提高其运行性能并降低能耗。
本文将首先概述轮毂电机电动汽车的基本原理和特性，包括其驱动方式、控制系统以及与传统电动汽车的差异。
随后，将重点分析轮毂电机电动汽车在稳定性方面面临的挑战，如侧倾、横摆等动态特性问题，以及如何通过先进的控制算法和车辆动力学模型来提高稳定性。
本文还将探讨节能控制策略，包括能量管理、优化驱动和回收制动等方面，以实现更高的能源利用效率和更长的续航里程。
通过本文的研究，我们期望能够为轮毂电机电动汽车的稳定性和节能控制提供有效的理论支持和实践指导，推动电动汽车技术的进一步发展，并为未来的绿色出行贡献力量。
二、线控四轮独立驱动轮毂电机电动汽车概述随着电动汽车技术的不断发展和创新，线控四轮独立驱动轮毂电机电动汽车（Independent Wheel Drive In-Wheel Motor Electric Vehicle, IWD-IWM EV）作为一种新型的电动汽车形式，逐渐展现出其独特的优势和巨大的发展潜力。
这种电动汽车采用轮毂电机直接驱动车轮，取消了传统的传动轴和差速器，实现了车辆的独立四轮驱动。
线控四轮独立驱动轮毂电机电动汽车的核心特点在于其高度集成化和模块化的设计。
每个车轮都配备有独立的轮毂电机，这些电机通过先进的电子控制系统进行精确控制，能够实现车辆在各种路况下的高效、稳定行驶。
由于取消了传统的机械传动系统，这种电动汽车的结构更为紧凑，重量更轻，从而提高了整车的能效和动力性能。
基于CVT的四轮驱动混合动力汽车传动控制策略研究的开题报告
基于CVT的四轮驱动混合动力汽车传动控制策略研
究的开题报告
一、研究背景和意义
随着全球气候变化的加剧和环保意识的增强，传统燃油车逐渐向混
合动力及电动化发展，成为了未来发展方向之一。
而四轮驱动系统有助
于提高汽车的行驶稳定性和通过能力，更是当今市场的热门趋势。
因此，研究基于CVT的四轮驱动混合动力汽车传动控制策略，有助于提高混合
动力汽车的整体性能和效率，满足未来市场的需求，同时符合环保要求。
二、研究内容和方法
本研究旨在针对基于CVT的四轮驱动混合动力汽车传动控制策略进
行系统分析和优化，主要研究内容包括以下方面：
1. 建立基于CVT的四轮驱动混合动力汽车传动控制模型；
2. 设计传动控制策略，包括节能、提升动力性能和行驶稳定性的策略；
3. 优化传动控制参数，提高汽车的整体性能和效率；
4. 进行仿真实验，验证传动控制策略的可行性和有效性。
三、研究预期结果
本研究主要预期结果包括以下几个方面：
1. 建立基于CVT的四轮驱动混合动力汽车传动控制模型，提高汽车性能和效率；
2. 设计出行之有效的传动控制策略，提升汽车的节能和动力性能；
3. 通过优化传动控制参数，提高汽车的整体性能和效率；
4. 通过仿真实验，验证传动控制策略的可行性和有效性。
四、研究意义
本研究有助于提高基于CVT的四轮驱动混合动力汽车的整体性能和效率，符合未来市场的需求和环保要求，有较高的实用性和推广价值。
同时，本研究也可以为混合动力汽车传动控制的研究提供新的思路和方法，具有一定的学术价值。
四轮独立驱动电动汽车最小转弯能耗转矩优化控制研究
四轮独立驱动电动汽车最小转弯能耗转矩优化控制研究与传统内燃机驱动的车辆相比,新能源和混合动力汽车以其低能耗和低污染,成为目前汽车领域的一个重要研究方向。
在新能源汽车的众多构型中,各个车轮分别由电机驱动的四轮独立驱动电动汽车,由于其空间布置灵活,转矩解耦,以及驱动模式多样化而日益受到学者们的关注。
四轮独立驱动电动汽车的一个关键控制技术,就是各个车轮的转矩优化控制,而目前大多数的研究都停留在利用转矩差所产生的直接横摆力矩来提高车辆的侧向稳定性,从而提高车辆的操纵稳定性。
本文主要着眼于转矩优化控制对车辆弯道工况的能耗影响,旨在利用转矩定向分配控制策略实现车辆弯道工况的最小转弯能耗的需求,有效的提高整车经济性。
本文首先利用MATLAB/Simulink仿真软件,搭建了四轮独立驱动电动汽车车辆动力学模型、轮毂电机模型和驾驶员模型等,并利用现有商用软件CarSim对模型的准确度进行了验证,为后文的理论分析及仿真试验提供了可靠的仿真平台。
为了从原理上说明车辆转弯的受力机理,本文利用三自由度车辆动力学模型进行了建立了车辆的运动微分方程,基于转弯降速现象,说明了转弯阻力的产生机理和影响因素,同时提出了通过转矩定向分配控制技术来抑制转弯阻力的控制方法。
本文通过仿真分析,验证了转弯阻力的存在以及其对车辆动力性和能耗的影响。
通过研究发现车速和前轮转角是对转弯阻力影响最大的两个因素。
通过仿真验证,可以清楚的说明采用转矩定向分配控制技术,主动的调节车辆内外侧车轮的驱动转矩,在不改变车辆的行驶状态的同时,可以有效的降低车辆的转弯阻力,从而降低车辆驱动的需求功率,实现节能控制。
本文还对比了车辆不同驱动模式下的能耗情况,明确了车辆转弯工况下的前轮模式受到的转弯阻力小。
本文还通过仿真验证,证明了转矩定向分配控制技术可以改变车辆的转弯特性,有效的改善车辆的转向不足特性,提高车辆的转弯机动性。
为了确定弯道工况以经济性为目标的转矩轴间分配系数k,前轴内外侧车轮转矩分配系数k_f和后轴内外侧车轮转矩分配系数k_r,本文采用遗传粒子群混合优化算法,综合考虑弯道工况经济性和稳定性的影响,构建了最小转弯能耗的转矩优化控制策略,对转矩分配系数进行离线优化,制定出了基于车辆动力学模型的最小转弯能耗转矩分配系数表,同时本文确定出了不同弯道工况的转矩优化控制的最佳节能贡献度。
关于四轮独立驱动电动汽车的电机控制策论
1202IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 20, NO. 5, SEPTEMBER 2012Fast and Global Optimal Energy-Efficient Control Allocation With Applications to Over-Actuated Electric Ground VehiclesYan Chen and Junmin Wang, Member, IEEEAbstract—This paper presents a fast and global optimization algorithm for an energy-efficient control allocation (CA) scheme, which was proposed for improving the operational energy efficiency of over-actuated systems. For a class of realistic actuator power and efficiency functions, a Karush-Kuhn-Tucker (KKT)-based algorithm was devised to find all the local optimal solutions, and consequently the global minimum through a further simple comparison among all the realistic local minima and boundary values for such a non-convex optimization problem. This KKT-based algorithm is also independent on the selections of initial conditions by transferring the standard nonlinear optimization problem into classical eigenvalue problems. Numerical examples for electric vehicles with in-wheel motors were utilized to validate the effectiveness of the proposed global optimization algorithm. Simulation results, based on the parameters of an electric ground vehicle actuated by in-wheel motors (whose energy efficiencies were experimentally calibrated), showed that the proposed global optimization algorithm was at least 20 times faster than the classical active-set optimization method, while achieving better control allocation results for system energy saving. Index Terms—Electrical ground vehicles (EGVs), energy-efficient control allocation (EECA), global optimality, in-wheel motors, Karush-Kuhn-Tucker (KKT) conditions, over-actuated systems.I. INTRODUCTIONOVER-ACTUATED systems, in which the number of actuators is greater than the degrees of freedom, have attracted increasing attention in recent years [1]–[5]. Many existing physical systems, such as marine vessels [6], [7], airplanes [8]–[10], and ground vehicles [11]–[16], [34], can be classified as over-actuated systems because redundant actuators are utilized to improve system performance, reliability, and reconfigurablity. In order to coordinate the redundant actuators and control the over-actuated systems in an elegant configuration, the main challenge is how to handle the actuator redundancy and physical constraints simultaneously. Control allocation (CA),Manuscript received October 06, 2010; revised February 20, 2011; accepted July 05, 2011. Date of publication August 12, 2011; date of current version June 28, 2012. This work was supported by Office of Naval Research (ONR) Young Investigator Award under the Grant N00014-09-1-1018, Honda-OSU Partnership Program, and OSU Transportation Research Endowment Program. Recommended by Associate Editor F. Basile. The authors are with the Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus, OH 43210 USA (e-mail: wang. 1381@). Color versions of one or more of the figures in this paper are available online at . Digital Object Identifier 10.1109/TCST.2011.2161989as a feasible and promising method, is commonly employed in over-actuated systems to optimally allocate the desired virtual (generalized) controls among all the available actuators within their respective constraints, see surveys [6], [9], and the references therein. Several different CA algorithms, such as direct allocation [17], daisy-chain allocation [18], (redistributed) pseudo-inverse allocation [14], [19], optimization-based allocation [8], and adaptive control allocation [20] (which was later applied to vehicle control simulations in [21]) have been proposed based on different methods of distributing the virtual controls to the redundant actuators. Although the aforementioned CA methods have different strengths and limitations, numerical optimization-based algorithms are becoming more and more widely used in CA [7], [8], [13], [22]–[26]. Sørdalen [7] adopted the magnitude of actuation as a secondary optimization term in CA formulation in order to achieve minimum control effort. Bodson [8] summarized and evaluated error minimization, control minimization, and mixed error and control optimization problems for optimization-based CA methods. Härkegård [22] suggested a dynamic CA method by penalizing virtual control at a previous sampling time in the secondary optimization term. Zaccarian [24] described a dynamic CA by inserting a dynamic system as an allocator between the high-level controller and low-level actuators. Moreover, Johansen et al. [25] resolved a CA problem on ship control by decomposing a non-convex thrust region into multiple convex sets, solved by mixed-integer-like convex quadratic programming. Within the aforementioned optimization-based allocation methods, the optimal algorithms for solving various CA problems were similar although the problem formulations were different. Standard numerical algorithms, such as quadratic programming, active-set, and fixed-point, were applied to solve various nonlinear programming problems of CA. However, local minima are often obtained by adopting these standard numerical algorithms for general nonlinear programming CA problems. On the other hand, the selections of the initial conditions of these standard algorithms also strongly influence the acquisition of the global minimum. Although there are no uniform optimization algorithms for general nonlinear programming problems, this paper proposes a global and initial-condition-independent algorithm for energy-efficient CA [27] based on the Karush-Kuhn-Tucker (KKT) conditions. In the expression of energy-efficient CA problem, a special nonlinear programming is considered since the secondary optimization term, standing for the system power consumption,1063-6536/$26.00 © 2011 IEEECHEN AND WANG: FAST AND GLOBAL OPTIMAL ENERGY-EFFICIENT CONTROL ALLOCATION1203consists of polynomial and/or fractional functions. By utilizing this kind of characteristics, the nonlinear programming procedure for energy-efficient CA can be transferred into classical eigenvalue problems, which are initial-condition-independent, based on the KKT conditions. Thus, all the physically meaningful eigenvalues are obtained as local minima of the energy-efficient CA problem. Through simple comparisons and exclusions, a global minimum can be obtained. The main contributions of this paper are: 1) a novel energy-efficient control allocation scheme which can explicitly incorporate actuator efficiencies and actuator operating modes into the control allocation for over-actuated systems and 2) a fast and global algorithm for solving the non-convex optimization problems associated with the proposed energy-efficient CA scheme. The remainder of this paper is organized as follows. In Section II, both single-mode and dual-mode energy-efficient CA schemes are described. The KKT condition-based algorithm is proposed to find the global minimum for the energy-efficient CA in Section III. In Section IV, numerical examples are given to verify the effectiveness of the proposed global optimization methods. In Section V, simulations based on longitudinal motion control of an electric ground vehicle actuated by in-wheel motors are presented to show the effectiveness of the proposed optimization algorithm in comparison to the standard active-set optimization algorithm. Conclusive remarks are presented in Section VI. II. ENERGY-EFFICIENT CONTROL ALLOCATION In this section, the main ideas and formulations of singlemode and dual-mode energy-efficient CA are briefly described. For more details and discussions on the energy-efficient CA, the reader can refer to the authors’ work [27]. A. Single-Mode Energy-Efficient CA A general over-actuated dynamic system is described as follows: (1) , where the system state vector is represented by is the system output vector, is the virtual control is the control effectiveness matrix, and vector, is the control input vector. Note that when the relaand is nonlinear, the control effectivetionship between ness matrix can be obtained by local approximation with an affine mapping through linearization at each sampling instant holds as the number [28]. For over-actuated systems, of actuators is greater than the number of virtual control signals and the number of the controlled system outputs. Thus, there is no unique solution for in general and CA is often utilized to optimal mapping problem. address the For over-actuated systems in which each actuator has only one actuation mode and one corresponding energy efficiency function, the energy-efficient control allocation is formulated as (2) s.t.where is the total instantaneous power consumption by all the actuators. The actuator amplitudes’ lower and upper bound and in a component-wise vectors are represented by fashion, respectively. Such magnitude bounds can also incorporate the actuator rate limitations based on the given system sampling period and difference formulation, see [22] and [27] for details. A small positive parameter is used to balance the efforts between reducing CA errors and the power consumptions. Note that smaller parameter will yield less CA errors [8]. Small CA errors may not noticeably affect the overall system control performance. Moreover, a high-level robust controller can help to overcome/mitigate the influence of the limited CA errors in the mixed optimization formulation. The system power consumption can be a function of actuator force or torque values based on the corresponding actuator efficiency functions, which can be expanded as follows: (3) Within (3), and are the output power function and the efficiency function of the th actuator, respectively. and represents Therefore, the division between . Thus, the formulathe th actuator’s power consumption tion of single-mode energy-efficient CA is developed by (2) and (3). B. Dual-Mode Energy-Efficient CA When actuators in physical systems have dual operating modes, such as consuming and gaining energy, and different associated efficiency functions, the energy-efficient CA becomes more challenging. For such dual-mode actuators, their effects on the virtual control, magnitude/rate constraints, efficiencies, power consumption, or gain characteristics may vary with operating modes. Consequently, to achieve energy-efficient CA for systems involving dual-mode actuators, the actuator mode selections need to be seamlessly integrated into the CA scheme as well since the dual-mode actuators with different modes/amplitudes may construct the same virtual control signal with different energy consumptions. Thus, the energy-efficient CA scheme needs to dictate not only the magnitude but also the operating mode for each of the redundant actuators in an optimal way. Such a requirement cannot be satisfied by the existing standard CA schemes and thus represents a key challenge for extending the foregoing energy-efficient CA to physical systems whose actuators have dual actuation modes. Here, a virtual actuator concept is introduced for tackling such a challenge. The expression of over-actuated dynamic systems described in (1) is correspondingly augmented as (4) A virtual actuator control input vector , is introduced to represent the of actuators who have dual operating modes in the system. This virtual actuator augment systematically incorporates the dual-mode actuators into the energy-efficient CA scheme. Such a method enables the CA to seamlessly dictate both actuators’ magnitudes and operating1204IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 20, NO. 5, SEPTEMBER 2012modes, resolving the aforementioned challenge. The augis the new control mented matrix effectiveness matrix for the new system. Note that the matrix are determined by the dual-mode actuators. The energy-efficient CA scheme (2) is modified for the new augmented system (4) with dual-mode actuators as (5) s.t. Since a particular dual-mode actuator can only operate in one of the two operating modes at a given time instant, the added third term in the constraints ascertains that only one operating mode is assigned to a physical actuator by the energy-efficient CA. Electric motors such as the in-wheel motors equipped on electric ground vehicles can be classified as dual-mode actuators. In electric ground vehicles, the electric motors can either work in motor (driving) mode, in which motor consumes onboard electric energy or generator (regenerative braking) mode, in which motor produces electric energy from the vehicle kinetic energy. thus can include Within (5), the total power consumption the instantaneous power consumption and gain with respect to the dual operating modes for each of the physical actuators. For example, if denotes the actuator energy consuming mode and represents the actuator energy gaining mode, then the total power consumption of all the actuators in different modes is formulated as (6) Within (6), and denote the actuator output power and and efficiency at the energy consuming mode while represent the actuator input power and efficiency at the energy gaining mode, respectively. The energy gaining mode is inferred by the minus sign in front of virtual actuator power consumption. Therefore, the formulation of the dual-mode energy-efficient CA is built by (5) and (6). III. KKT-BASED GLOBAL OPTIMIZATION ALGORITHMS FOR ENERGY-EFFICIENT CONTROL ALLOCATION For both single-mode and dual-mode energy-efficient CA, the authors adopted a standard nonlinear optimization method, active-set algorithm, to obtain the solutions [27]. The adopted active-set algorithm, however, cannot guarantee the global optimization and is sensitive to the selections of initial conditions. Based on the KKT conditions, a global and initial-condition-independent optimization algorithm for the energy-efficient CA is proposed as follows. A. KKT Conditions and Algorithm for the Single-Mode Energy-Efficient CA In order to develop the optimization algorithm for the singlemode energy-efficient CA, the expressions (2) and (3) are combined and modified as (7)s.t. The square modification for CA errors is convenient for derivatives of the Lagrangian function defined later. are the vector forms of output power functions and efficiencies, respectively. Define the following Lagrangian function: (8) Within (8), nonnegative vectors and are the Lagrangian multipliers. Based on the KKT conditions [29], the optimal solution with certain Lagrangian multipliers and satisfy the following conditions:(9)Remark 1: Generally, the KKT conditions are necessary conditions for local minima. They can also be utilized to sufficiently characterize the global optimal solution when the objective/cost function and the constraint set are convex. Although the cost function in (7) is not in a convex form, further examinations can be fulfilled to exclude the maximum and the local minima. The calculations on optimal control are categorized by the values of Lagrangian multipliers and . When the Lagrangian mul, the control can be calculated within tipliers and/or boundaries by solving the algebraic equations (9). For the special form of energy-efficient CA, the secondary optimization term is a sum of fractions, whose denominators and numerators can be characterized by low-order polynomial functions of power and efficiency, making (9) be easily solvable. When the and/or , the control Lagrangian multipliers has to be equal to the boundary values and/or based on the KKT conditions. Thus, a few local minima including the boundary values can be obtained from the above two categorized steps. At last, further comparisons among these local minima based on the criteria of the least power consumption can give the global minimum. In sum, the algorithm is written in a pseudo-code form as follows. Inputs: and are from the system model and high-level • controller. , , , and are from actuator models • and experimental calibrations. and are adjustable parameters for optimization. • Steps: 1) Set , solve algebraic equations (9) and obtain all the nontrivial local minima .CHEN AND WANG: FAST AND GLOBAL OPTIMAL ENERGY-EFFICIENT CONTROL ALLOCATION12052) Set , consequently obtain and , can be obtained by solving the other the remaining algebraic equations in (9), like the process in step 1. , consequently obtain 3) Similar to step 2, set and , the other can be obtained by solving the left algebraic equations in (9), like the process in step 1. 4) Calculate the total power consumptions (the second term obtained from the above three steps, in (7)) for all the by comparison. and obtain the global optimal B. KKT Conditions and Algorithm for the Dual-Mode Energy-Efficient CA Similar to the single-mode energy-efficient CA case, the expressions (5) and (6) are combined and modified so as to develop the optimization algorithm for the dual-mode energy-efficient CA(10)s.t.Remark 2: Again, although the KKT conditions, which are necessary for local minima, generally cannot guarantee the global optimal solution of the non-convex cost function (10), further examinations can be fulfilled to exclude the maximum and and the local minima. The calculations on control are categorized by the values of Lagrangian multipliers , , , and . When the Lagrangian multipliers and/or ( and/or ), the control can be calculated within boundaries by solving the algebraic (12). The special secondary optimization term in (10) consists of a sum of polynomials and fractions, whose denominators and numerators can also be characterized by low-order polynomial functions of power and efficiency, making (12) be readily solvable. Moreover, the complementary condition determines that and cannot be nonzero simultaneously, which can be further applied to reduce the computational cost and . When the Lagrangian of (12) by setting multipliers and/or ( and/or ), has to be equal to the boundary values. the control Thus, a few local minima including the boundary values can be obtained from the above two categorized steps. At last, further comparisons among these local minima based on the criteria of the least power consumption can give the global minimum. Similar to the single-mode CA case, the algorithm for dual-mode CA is given in a pseudo-code form as follows: Inputs: • and are from the system model and high-level controller. , , , , , , and • are from actuator models and experimental calibrations. • and are adjustable parameters for optimization. Steps: , solve algebraic equations 1) Set and . (12) and obtain all the nontrivial local minima can be Note that the complementary condition applied to simplify the calculation of (12) by categorizing , and , the whole process into ( are trivial cases without solving (12)). 2) Set , consequently obtain and , through the complementary and furthermore . The other and condition can be obtained by solving the remaining algebraic equations in (12), like the process in step 1. , consequently obtain 3) Similar to step 2, set and , and furthermore through . The other the complementary condition and can be obtained by solving the left algebraic equations in (12), like the process in step 1. and , obtain 4) Repeat steps 2 and 3 for and . 5) Calculate the total power consumptions (the second term and obtained from the above in (10)) for all the and by steps, and find out the global optimal comparison.where are the vector forms of input power functions and efficiencies, respectively. Define the corresponding Lagrangian function as(11) Within (11), nonnegative vectors and , and , are the Lagrangian multipliers. Based on the KKT conditions and with certain Lagrangian [29], the optimal solutions , and satisfy the following conditions: multipliers , ,(12)1206IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 20, NO. 5, SEPTEMBER 2012Fig. 1. Experimentally measured efficiency curve of an in-wheel BLDC motor and its controller. Fig. 2. Driving efficiency map of an in-wheel BLDC motor and its controller based on experimental data. TABLE I IN-WHEEL MOTOR EFFICIENCY FUNCTION PARAMETERSmotor speed also slightly affects the motor efficiency as can be seen from the measured experimental data shown in Fig. 2, the efficiency curves are similar within a large range of motor rotational speeds [31]. Moreover, motor rotational speed can be reasonably assumed to be a constant at each instantaneous time for CA due to the short sampling period. The power consumption of the in-wheel motor is given by It should be noted that as the number of the system redundant actuators increases, the computational effort and complexity of the preceding algorithms will grow as well. In addition, the actuator efficiency function characteristics also influence the complexity of the KKT-based energy-efficient CA algorithm. IV. NUMERICAL EXAMPLES AND DISCUSSIONS In this section, the aforementioned KKT-based algorithms for both single-mode and dual-mode energy-efficient CA are applied to numerical examples. These examples, generated from in-wheel motor models and experimental data, verify the assumption and validation of the aforementioned formulation. of an in-wheel brushless The output efficiency function DC (BLDC) motor and its controller is expressed by fitting the experimental data [31] shown in Fig. 1. Two linear functions are adopted to approximate the rising and falling potions of the entire experimentally measured data (13) , , , and are coefficients, listed in Table I. where Multiple reasons make the piece-wise linear function (13) as the efficiency fitting function. The first one is the consideration of computational effort. From either (9) or (12), the KKT conditions offer algebra equations or eigenvalue problems to obtain the optimal values. The simpler efficiency fitting function makes the computational cost less. It can be seen later that this piece-wise division actually makes the global optima obtainable. The second reason is due to the DC characteristics of the brushless DC motors. The piece-wise linear function (13) can sufficiently describe the rising and falling trends along with the increase of motor torque. Last but not the least, although the (14) is a given rotational speed and stands for the motor where torque. Without loss of generality, two in-wheel BLDC motors are considered for the longitudinal speed control of a bicycle vehicle model under straight line driving condition. Different scaling ratios were applied to the efficiency curve in Fig. 1 to generate different efficiency functions for two motors and two operating modes. In the case of single-mode actuation (both in-wheel motors drive), the control effectiveness matrix is . In the case of dual-mode actuation (both in-wheel motors can perform driving and regenerative braking), the control . The boundaries of the effectiveness matrix is 0 Nm and 100 Nm for driving, and actuator are 100 Nm and 0 Nm for regenerative braking. A. Single Mode Energy-Efficient CA In the numerical example for single-mode energy-efficient 400 rpm, CA, the motor rotational speed was selected as which is about 50 km/h for a passage car with a common tire effective radius around 0.3 m. The penalty coefficient was set was set to be an idento be 0.001 and the weighting matrix tity matrix for the optimization problem. The scaling ratio for efficiency of the second in-wheel motor was 0.9. for nonSubstituting (14) into (9) and letting trivial solutions, the following equations are obtained:(15)CHEN AND WANG: FAST AND GLOBAL OPTIMAL ENERGY-EFFICIENT CONTROL ALLOCATION1207Fig. 3. Space of the total power consumption for single-mode energy-efficient CA with two in-wheel motors.Fig. 4. Power consumption of single-mode energy-efficient CA with two in-wheel motors with different given virtual control values.Since the efficiency function (13) is piece-wise linear, the equations in (15) have to be solved by combining different efficiency functions. Basically, four pairs of two algebraic equations are obtained. In order to solve two algebraic equations with two variables, like (15), variable cancellation can obtain the equivalent problem of finding the roots of a polynomial due to the simple efficiency and power expressions (13) and (14). The obtained polynomial can be taken as a characteristic polynomial of a classical eigenvalue problem. Thus, the optimization problem are transferred into four (two pairs of) eigenvalue problems to and . Compared with the trivial solutions find optimal (boundary values of actuators), the global minimum can be finally obtained. In order to show the real global minimum is achieved, the space/surface of the total power consumption, the secondary optimization term in (7), is plotted in Fig. 3. As shown in Fig. 3, the total power consumption generally becomes larger with the increase of motor torque. However, due to the non-convex characteristics of the surface, it is hard to find the global minimum by using standard optimization algorithms. Through the piece-wise fitting for the efficiency functions of the actuators, the non-convex space/surface is actually , each of which is convex divided into four portions within its own definitional area. Thus, in each area, the corresponding global minimum can be obtained through KKT conditions, which is equivalent to an eigenvalue problem. Then, a simple comparison among these global minima and boundary values of different areas can suggest the true global minimum in the entire non-convex space of the power consumption. The five lines on the surface stand for different virtual control values, which are the intersection curves between the non-convex power consumption surface and vertical planes, corresponding to dif. These curves define the solution sets of ferent problem (7) under different virtual control signals without considering the power minimization. Given different virtual control , we can clearly observe the global optimization of the distribution torques and from the plot in Fig. 4. Fig. 4 shows the power consumption of optimization problem (7) by inserting efficiency and power expressions in (13) and (14). Each curve in Fig. 4 represents different virtual controlsignals from 10, 20, 40, 60, to 80 Nm. On each curve, the corresponding virtual control is equal to the sum of distributions onto the two motors. All the labels in Fig. 4 represent the global optimization points. Although these global minimum points vary on the non-convex curves, the proposed KKT-based algorithm accurately found all of them from the equivalent eigenvalue problems and simple comparisons with boundary values. If a standard active-set algorithm is applied to solve the nonlinear optimization problem, the global minimum points may not be found by inappropriate choices of initial conditions and only local minima can be obtained. It is interesting to observe the 40 Nm. The global optimum dictates that the equal case of torque distribution gives the lowest power consumption, which happens to be the distribution result of a standard CA method [27]. This is because the most efficient operating point lies on 20 Nm, which can be seen from Fig. 1 and the efficiency scaling factor. B. Dual-Mode Energy-Efficient CA The same values for motor rotational speed , weighting maand the penalty coefficient as those in the single-mode trix case were adopted for the numerical example in dual-mode energy-efficient CA. Assuming that two in-wheel motors (front and rear) have similar efficiency profiles, the scaling ratio for the driving efficiency of the rear motor was 0.9. Moreover, the motor regenerative braking efficiency is usually less than the driving efficiency. Thus, the scaling ratio for the regenerative braking efficiency of each motor was set as 0.9 of the corresponding driving efficiency. Substituting (14) into (12) and letting for nontrivial solutions, the following expressions are obtained:1208IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 20, NO. 5, SEPTEMBER 2012Fig. 5. Space of the total power consumption for dual-mode energy-efficient CA with two in-wheel motors.Fig. 6. Power consumption of dual-mode energy-efficient CA with two in-wheel motors with different given virtual control values.(16)Fig. 7. EGV longitudinal speed tracking control structure.Note that although four algebra equations and two complemenand tary equations are listed in (16), the four equations for or and cannot be held simultaneously due to two complementary equations. Thus, the two complementary equations can be logically applied to reduce the computational cost since only two algebra equations are solved together. Like the process in the single-mode case, similar steps are implemented to solve (16) for local minima by plugging in the piece-wise linear efficiency function (13). Then, comparisons among local minima and trivial solutions (boundary values) will give the globally optimal solutions. In order to show the real global minimum is achieved, the space/surface of the total power consumption, the secondary optimization term in (10), is plotted in Fig. 5. As shown in Fig. 5, the power consumption space becomes more non-convex than that in the single-mode case. Since each virtual actuator has two piece-wise linear efficiency potions, the total non-convex power space consists of sixteen convex . However, for a certain virtual control value, areas only parts of the convex areas are meaningfully involved in. Fig. 5 shows that seven convex surfaces are physically achiev20 Nm. Thus, similar to the single-mode case, a able when simple comparison among global minima and boundary values of different areas can suggest the true global minimum in the entire non-convex space. The four lines on the surface stand for different virtual control values, which are plotted in Fig. 6 to clearly show the global minimum for the torque distribution and . among Fig. 6 shows the power consumption of the optimization problem (10) by inserting efficiency and power expressions in (13) and (14). Each curve in Fig. 6 represents different virtual control values from 4, 8, 10, to 20 Nm. On each curve, the corresponding virtual control is equal to the sum of distributions on the two motors. All the labels in Fig. 6 represent the global optimal points. Although these global minimum points vary on the non-convex curves, the proposed KKT-based algorithmaccurately finds all of them from the equivalent eigenvalue problems and simple comparisons with the boundary values. V. SIMULATIONS OF ELECTRIC GROUND VEHICLES In this section, the proposed KKT-based algorithm for both single- and dual-mode energy-efficient CA are applied to an electrical ground vehicle (EGV) model equipped with front/rear in-wheel motor sets. Electric concept cars with independently actuated in-wheel motors such as the GM Geo Storm and Volvo ReCharge have already proceeded into the prototyping and/or pre-market phases. Thus, in-wheel motors have potential applications in automotive industry, see [13], [27], and reference therein. Compared with the conventional vehicle drivetrain architectures where driving and braking of different wheels are coupled, EGVs with independently actuated in-wheel motors can offer higher control flexibility and many potential advantages. Consequently, the efficiency and control allocation problem are meaningful to be investigated. Note that the proposed energy-efficient CA schemes are applicable for many over-actuated systems, well beyond the electrical/hybrid vehicles. Moreover, such general energy-efficient CA methods are different from the power management methods for hybrid vehicles [32], [33] because of the distinctions in number and characteristics of actuators as well as the control purposes for over-actuated systems. The structure for vehicle longitudinal speed tracking control is shown in Fig. 7 for comparison between the common active-set method and the proposed KKT-based algorithm. The and , desired and measured vehicle speed are denoted as respectively. The high-level speed tracking controller is a proportional plus integral (PI) controller, which generates the virtual control for the energy-efficient control allocator. Note that other high-level speed tracking controllers can be employed as。
四轮独立驱动电动汽车纵向动力学控制研究
摘要由于石油等燃料属于不可再生能源，而如今汽车的保有量一直呈现增长趋势，因此电动汽车技术成为解决能源与环境危机的必然发展趋势。
相对于集中式驱动电动汽车，分布式驱动的传动方式可以明显体现出更加良好的动力学操控性，高传动效率以及简化的系统结构，于是分布式驱动电动汽车逐渐开始变成研究热点。
本文以四轮独立驱动电动汽车为研究对象，对纵向动力学控制进行研究。
利用分布式驱动汽车四轮转矩可独立控制的特点，考虑轮胎的动态特性和制动系统执行器的动态特性，基于分层控制理念，利用先进控制分配技术，实现车辆的稳定性控制并提高控制性能。
主要完成了以下研究工作：（1）建立了整车动力学模型，并搭建了CarSim/Simulink联合仿真平台。
利用CarSim软件搭建了模块化的整车动力学模型，并根据控制模型需求，配置了CarSim与MATLAB/Simulink软件之间的I/O口，完成整车模型与控制器模型的连接。
（2）基于逆轮胎模型设计了稳态车轮滑移率控制策略。
首先基于带约束的优化分配方法将目标纵向轮胎力进行分配，然后通过Dugoff逆轮胎模型求出目标滑移率，再利用滑模控制（Sliding Mode Control，SMC）对目标滑移率进行跟踪控制。
最后对基于逆轮胎模型的轮胎力控制分配效果与不考虑轮胎动态特性的轮胎力控制分配效果进行了仿真对比。
结果表明，本文所提出的考虑轮胎动态特性，基于逆轮胎模型，通过滑移率控制进行轮胎力控制分配的策略，有效地提高了轮胎力的控制精度，轮胎力绝对误差至少降低了51.10%。
（3）基于执行器动态控制分配方法设计了极限工况下的滑移率控制策略。
首先在上层控制器中通过滑模控制跟踪滑移率，防止车轮出现滑转和抱死，得到驱动防滑控制(Acceleration Slip Regulation，ASR)转矩和制动防抱死控制（Anti-lock Braking System，ABS）转矩。
然后在制动工况下，下层控制器考虑电机和液压制动系统的动态特性，基于模型预测控制（Model Predictive Control，MPC）动态分配的方法，对电机和机械制动进行转矩分配，实现复合制动。
四轮独立线控电动汽车试验平台搭建与集成控制策略研究
四轮独立线控电动汽车试验平台搭建与集成控制策略研究一、本文概述随着电动汽车技术的快速发展，线控技术在汽车领域的应用越来越广泛。
四轮独立线控电动汽车作为新一代电动汽车的代表，其独特的线控系统结构和集成控制策略成为了研究热点。
本文旨在搭建一套四轮独立线控电动汽车的试验平台，并研究其集成控制策略，为电动汽车的发展提供理论支持和实践指导。
本文将详细介绍四轮独立线控电动汽车的试验平台搭建过程。
这包括硬件设备的选择、系统架构的设计、控制系统的编程以及平台的调试与测试等方面。
通过搭建这样一个试验平台，可以模拟真实道路环境，为后续的集成控制策略研究提供有力的实验基础。
本文将重点研究四轮独立线控电动汽车的集成控制策略。
这涉及到车辆动力学建模、稳定性分析、能量管理以及多目标优化等多个方面。
通过深入研究这些控制策略，可以提高车辆的性能表现，如稳定性、操控性、能效等，从而满足用户对电动汽车的多样化需求。
本文将总结研究成果，并对未来研究方向进行展望。
通过本文的研究，不仅可以为四轮独立线控电动汽车的实际应用提供理论支持和实践指导，还可以为电动汽车领域的其他研究提供参考和借鉴。
本文旨在搭建一套四轮独立线控电动汽车的试验平台，并研究其集成控制策略，以期为电动汽车的发展做出积极贡献。
二、四轮独立线控电动汽车技术概述四轮独立线控电动汽车（4WIS，Four-Wheel Independent Steering）是一种采用独立线控转向系统的电动汽车技术。
这种技术允许车辆的前轮和后轮在转向时拥有独立的角度调整，从而极大地提高了车辆的操控性和稳定性。
与传统的固定转向比或者通过差速器实现的转向系统相比，四轮独立线控转向系统能够实现更为复杂和精细的操控策略，使得车辆在高速行驶、紧急避让、泊车入库等多种场景下都能展现出更优越的性能。
四轮独立线控电动汽车技术的核心在于其线控转向系统。
线控转向系统由转向传感器、电子控制单元（ECU）和执行机构等部分组成。
四轮独立线控电动汽车驱动系统主动容错控制策略研究
四轮独立线控电动汽车驱动系统主动容错控制策略研究相比于四轮独立线控电动汽车其他底盘系统,线控驱动系统具有如下特点:其一,驱动执行器的数量众多,各类可控执行机构在空间上大量应用及分布,各子系统之间的机械耦合相对弱化,电气化特征更加明显,其故障更具隐蔽性和突发性。
其二,轮毂电机需频繁启动以应对车辆时变的行驶工况,使得线控驱动系统经常处于非稳态的工作状态。
其三,多执行器冗余结构,往往使得多个控制系统共享同一传感器、执行机构或控制变量,这种行为耦合会产生各执行系统间的干扰或冲突。
鉴于线控驱动系统自身的特殊性,有必要针对这种新型结构电动汽车的线控驱动系统,建立完善的容错控制机制,对提高车辆行驶安全性具有重大的实际意义。
目前,针对四轮独立线控电动汽车驱动失效问题,多采用基于规则分配的被动容错控制策略,应对预先设定的驱动失效模式,仅对特定故障具有鲁棒性。
考虑到线控驱动系统故障的不可预知性,采用主动容错控制的效果更佳。
四轮独立线控电动汽车驱动系统主动容错控制尚处于研究阶段,许多问题亟待解决,大体可概括为以下几点:其一,基于重构控制分配的主动容错控制策略,通过对驱动失效的电动汽车动力学控制问题进行解耦,在集成控制架构下,将其转化为控制分配问题,但是仅从整车控制角度出发,缺乏对底层驱动执行器特性的综合考虑。
其二,基于重构控制分配的主动容错控制策略,在进行重构控制分配性能指标设计时,多采用基于约束的单目标优化,考虑到驱动系统失效会影响到整车动力学特性,无法保证在重构控制分配中始终得到优化可行解。
其三,将线控驱动系统的各职能单元统一为一个功能整体,未对各驱动执行单元之间关联做出定性分析,对失效的线控驱动系统采用输入直接置零的处理方式,忽略各驱动单元自身的故障补偿能力,降低了执行器效率。
本文依托国家自然科学基金资助项目“分布式全线控电动汽车可重构集成控制策略研究”(编号:51505178)、“多智能体线控转向系统分层容错控制方法研究”(编号:51505179)及“基于驾驶员特性的新型线控转向系统控制机理和评价方法”(编号:51575223),面向智能电控底盘关键技术,从车辆系统动力学和自动控制理论出发,在分析四轮独立线控电动汽车驱动执行器工作特性和失效机理基础上,以保证车辆行驶安全性为前提,以最大化满足整车动力学需求为目标,提出了一种基于双层架构的线控驱动系统容错控制策略。
后轮独立驱动电动汽车四轮转向系统的研究
后轮独立驱动电动汽车四轮转向系统的研究后轮独立驱动电动汽车再研究后可以实现四轮转向，其中前轮和后轮转向可以经过差速转向和常规转向实现。
确立了动力学模型，将常规的四轮转向汽车用作参考模型，并且将PID控制器设计为捕获驱动后轮转向所需要的的差动转矩，仿真效果表明，差速转向可以实现后轮转向。
标签：后轮独立驱动;四轮转向系统;电动汽车根据电机的类型，独立驱动器可以分为轮侧电机驱动器和轮毂电机驱动器。
驱动和传动都集成到轮毂电机驱动汽车的轮毂中，这大大简化了车辆的机械结构并提升了车辆的空间使用效率。
差动转向技术是使车辆能够通过控制两侧独立驱动车轮的驱动力矩来实现转向的新技术。
独立的后轮驱动与差动转向技术相结合，不仅有效提高了低速行驶时的机动性，而且还有效地降低了高速行驶时的甩尾和侧滑现象，此外，可以主动提升汽车的操纵稳定性和安全性。
一、车辆模型目前国内外大学和研究机构研究和开发的热点是四轮独立驱动电动汽车。
四轮独立驱动电动汽车可独立控制四轮驱动力矩，转矩和速度可轻松测量，这对车辆控制非常有利。
汽车的四轮转向控制是主动安全控制的重要研究内容之一，因为它可以有效地减小车辆的转向半径并提高转向稳定性，四轮独立驱动电动汽车具有四轮转向功能是车辆主动安全控制的理想载体。
本文研究的传统转向后轮独立驱动差速转向车辆可以被认为是一个具有一个车身和两个后轮的系统，仅忽略车辆沿y轴的横向运动和绕z轴的横摆运动，而不考虑悬架对车身运动的影响，偏侧特性在线性范围内。
还研究了四轮转向控制策略，使用CarSim与Matlab/Simulink建立了车辆模型和控制策略，并进行了仿真测试。
内燃机模型是CarSim中的车辆模型，并且该模型已修改为四轮独立驱动电动汽车模型。
Simulink用于构建四轮驱动力分配器和四轮角分配器，四轮驱动力分配装置通过基于驾驶员的目标速度和实际车辆的反馈速度来计算车辆所需的总目标驱动转矩来分配四轮驱动转矩，四轮角分配器根据驾驶员的方向盘角度计算并分配四轮角大小，四轮转角控制基于阿克曼的转角控制原理。
全线控四轮独立转向驱动制动电动汽车动力学集成控制研究
全线控四轮独立转向驱动制动电动汽车动力学集成控制研究一、本文概述随着电动汽车技术的快速发展，车辆动力学集成控制已成为提高车辆行驶稳定性、安全性及能效的关键。
特别是针对全线控四轮独立转向驱动制动电动汽车，其动力学特性与传统车辆存在显著差异，开展对其动力学集成控制的研究具有重要意义。
本文旨在深入探讨全线控四轮独立转向驱动制动电动汽车的动力学特性，分析其转向、驱动和制动系统的协同工作机理，研究相应的集成控制策略，以实现车辆在各种工况下的最优性能。
本文将对全线控四轮独立转向驱动制动电动汽车的基本结构和工作原理进行介绍，阐述其与传统车辆在动力学方面的主要差异。
分析车辆在转向、驱动和制动过程中的动力学行为，建立相应的数学模型，为后续控制策略的研究提供理论基础。
接着，本文将重点研究车辆的动力学集成控制策略，包括转向、驱动和制动系统之间的协同优化，以及基于多传感器信息融合的车辆状态感知与决策算法。
通过仿真分析和实车试验验证所提控制策略的有效性，为全线控四轮独立转向驱动制动电动汽车的实际应用提供理论支撑和技术指导。
本文的研究不仅对提升电动汽车的动力学性能具有重要意义，同时也为车辆动力学集成控制领域的发展提供新的思路和方法。
二、全线控四轮独立转向驱动制动电动汽车概述随着科技的进步和新能源汽车的快速发展，全线控四轮独立转向驱动制动电动汽车（Fully Actuated Four-Wheel Independent Steering, Driving, and Braking Electric Vehicle，简称FAFWIDBEV）作为一种新型的电动汽车技术，正受到越来越多的关注和研究。
这种电动汽车突破了传统汽车的设计框架，通过先进的控制系统和算法，实现了四轮独立转向、驱动和制动，从而极大地提高了车辆的动力性、操纵稳定性和安全性。
全线控四轮独立转向技术允许车辆的四个车轮各自独立地进行转向操作，不再局限于传统的前轮转向或四轮同向转向的模式。
四轮独立驱动电动汽车稳定性仿真研究
应该有什么样的滑移 率／ 滑转 率 ， 以期使 车辆受 到地 面施加 的 期望的附加 横摆力矩 △ ｆ 。 第 三步 ， 通过控制驱动电机 ， 将 车轮的滑移率／ 滑转率控 制 在 期望值 。 完 整的稳定性控制策略总体方案如图 １ 所示 。
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对汽车操纵稳定性影响因素进行分析设计适合于四轮独立驱动汽车的稳定性控制策略利用四轮独立驱动汽车每个轮的驱动力可以单独控制的优点在传统车只能靠制动来改变车轮心侧偏角联合作为表征汽车操纵稳定性的变量进行控制且较内燃机汽车的机械传动效悬架技术与驱动以后掌握采用主开发能力具有指导意义的车辆的特点以及稳定性控制策略的总体车辆受到地面施加的期望的附加横摆力矩通过控制驱动电机将车轮的滑移定性控制理念是选取汽车的横摆角速度和纵向速度与侧向速度确定汽车质心相关文献也指出质心侧偏角对于车辆稳定性控可以得到以下结论
四轮驱动车辆的动力学控制及优化策略
四轮驱动车辆的动力学控制及优化策略一、引言近年来，越来越多的豪华车以及运动型车辆采用四轮驱动系统，优异的车辆性能得到了广泛认可。
四轮驱动系统相比于传统的二轮驱动系统拥有更好的操控性、加速性能、抓地性以及越野能力等方面，逐渐成为了现代汽车发展的趋势。
在四轮驱动车辆动力学控制及优化策略方面，已经有了一定的研究进展。
本文将从四轮驱动车辆的动力系统、四轮驱动模式选取以及动力分配策略等方面对四轮驱动车辆的动力学控制及优化策略进行讨论。
二、四轮驱动车辆的动力系统四轮驱动车辆的动力系统由发动机、变速器、传动系统、差速器以及方向盘等部件组成。
其中对于四轮驱动车辆，差速器的设计尤为重要。
差速器的作用是实现驱动轮的不同转速，以达到差速同步。
在四轮驱动车辆中，前、后轴差速器的作用相当于分别控制车辆前面轮胎和后面轮胎的转速，以克服车辆转弯时轮胎的自然不同步现象。
为了提高汽车的通过性和操控性，通常四轮驱动车辆会包括两个以上的差速器，比如中央差速器以及前、后差速器。
通过中央差速器可以实现前后轮的动力分配，从而实现更加优化的行驶。
三、四轮驱动车辆的四轮驱动模式选取常见的四轮驱动模式包括普通四驱、自动四驱、全时四驱和主动式四驱等。
其中普通四驱是指固定的前、后轮驱动模式，适用于复杂路况的情况，但是在好路况下的行驶就会导致油耗增加。
自动四驱是指在正常路况下前轮和后轮进行分配，但是当出现车辆滑动时，后轮会进行额外的驱动以增加车辆抓地力。
全时四驱是指车辆全时驱动四个轮子，适用于较为复杂的路况下行驶，但在好的路况下，油耗可能较高。
主动式四驱是指根据车辆行驶路况，动态进行前后轮驱动模式的调节，从而实现最佳的动力分配和行驶性能。
四、四轮驱动车辆的动力分配策略动力分配策略是保证车辆在复杂路况下能够快速响应的一个重要环节。
常见的动力分配策略有恒定的动力分配策略和变化的动力分配策略。
恒定的动力分配策略是指在不同路况下，保持一定比例的前后轮驱动模式，从而获得平衡的性能表现。
四轮独立驱动电动汽车轮毂电机控制策略的研究
四轮独立驱动电动汽车轮毂电机控制策略的研究随着科学技术的不断进步，新能源汽车已经逐渐成为一种趋势。
电动汽车具有零排放、环保、低噪音等特点，成为了未来汽车的主流。
而四轮独立驱动电动汽车轮毂电机控制策略也得到了广泛关注和研究。
四轮独立驱动是一种将每一个车轮都配备电动机的新型汽车驱动形式，它可以实现前、后、左、右四轮独立控制，增强了汽车的操控性能，同时也可以有效提高汽车的能效。
轮毂电机则是将电机直接安装在车轮中，代替传统的发动机和变速箱结构，从而实现汽车驱动方式的变革。
掌握四轮独立驱动电动汽车轮毂电机控制策略，可以有效提高汽车的性能和驾驶体验。
其中，电机控制系统是十分重要的部分。
该系统的控制方法多种多样，包括PID控制、变结构控制、自适应控制、SLIDING MODE 控制等。
其中，PID控制是一种较为常见的控制方法，主要用于控制电机转速和转矩，并且具有结构简单、调节方便等优点。
在实际运行过程中，电机也会面临很多不同的负载状况，包括急启动、急刹车、上下坡等。
在控制策略中，也需要考虑这些因素。
针对电机的负载状况，可采用速度矢量控制、磁场定向控制、直接扭矩控制等多种控制方法实现电机转矩和转速的控制。
在电机控制方面，还需要考虑不同的能量回收方式。
例如利用制动能量回收（Regenerative Braking，简称ReB）就可以将制动损失的能量全部收回，充电电池的电量也可以得到有效的补充。
同时，还可以利用光伏充电等方式为汽车充电，降低汽车的能量消耗，抵消环境污染。
总之，四轮独立驱动电动汽车轮毂电机控制策略是未来汽车产业中的一个重要方向，可以为汽车的性能提升、能效提高、环保节能等方面做出贡献。
未来，随着新技术的研发和应用，相信这种驱动方式会逐渐普及，成为新能源汽车发展的一个新趋势。
四轮独立电驱动车辆实验平台及驱动力控制系统研究共3篇
四轮独立电驱动车辆实验平台及驱动力控制系统研究共3篇四轮独立电驱动车辆实验平台及驱动力控制系统研究1四轮独立电驱动车辆实验平台及驱动力控制系统研究随着电动汽车市场的不断发展，电驱动技术也被广泛应用于各种类型的车辆中。
其中，四轮独立电驱动技术因其优异的性能和灵活的控制方式在汽车领域中备受瞩目。
为了更好地研究四轮独立电驱动技术，我们设计了一套实验平台，并研究了不同的驱动力控制系统。
一、实验平台的设计实验平台主要由电机、转向系统、悬挂系统、传感器、控制器和数据采集系统等组成。
其中，电机是实验平台的核心部件，它们负责驱动车辆的四个车轮。
为了实现四轮独立控制，我们使用了四个相同的电机，并通过能够实现精确控制的驱动器进行控制。
转向系统由电动转向机构和电子稳定系统等组成，它们能够精准控制车辆的转向和稳定性，从而满足不同的实验需求。
悬挂系统则通过四个独立悬挂系统进行控制，可以模拟不同路况下的行驶情况，从而更好地评估车辆性能。
传感器是实验平台的重要组成部分，可以实现车辆状态的实时监测和数据采集。
其中，轮速传感器、转向传感器、陀螺仪和加速度计等传感器可以提供详细的车辆状态信息，以便进行车辆控制和性能分析。
控制器是实验平台的大脑，能够接收传感器数据并实现车辆控制。
基于德州仪器的C2000系列DSP芯片，我们设计了一款高性能的控制器，其频率可高达100kHz，能够快速响应车辆状态的变化并实现精确的控制。
数据采集系统是实验平台的另一个重要部分，可以对车辆性能进行全面评估。
我们使用了高精度的数据采集卡和LabVIEW软件进行数据采集和分析，以实现对车辆性能的定量评估。
二、驱动力控制系统的研究驱动力控制是四轮独立电驱动技术的核心之一，它可以实现四个轮子的独立控制，从而为车辆带来更好的动力性能和稳定性能。
我们研究了基于模型预测控制(MPC)和基于反馈线性化控制的驱动力控制系统，并进行了实验验证。
基于MPC的控制系统是以实时预测为基础，通过对车辆状态和驱动力的优化预测，实现了对车辆的精准控制。