基于驾驶员驾驶习惯的增程式电动汽车参数匹配研究

纯电动汽车由于电池低能量密度导致续驶里程短,高价格导致用户购买力低,增程式电动汽车(EREV)通过增加增程器(发动机-发电机组)克服了这一缺陷,基于驾驶员的驾驶习惯分析了增程式电动汽车的性能,并对动力传动系统参数进行匹配,基于Cruise仿真软件对增程式电动汽车进行仿真分析,仿真表明,匹配的动力系统参数完全满足要求。     

基于可进化PID的含电动汽车孤岛微电网负荷频率控制策略

电动汽车（electric vehicle,EV）作为具有车网互动（vehicle-to-grid,V2G）能力的移动储能组件,为微电网的频率稳定提供了更为灵活的控制方案。但是,分布式电源与电动汽车所具有的强非线性,也给微电网频率控制带来了新的挑战。为此,提出了一种基于可进化PID的含电动汽车微电网频率控制策略。首先,考虑电动汽车移动性和用户充电行为随机性,以及电动汽车充电站容量在日内的阶段性突变,建立了由电动汽车充电站、微型燃气轮机与分布式电源组成的微电网系统。其次,将PID与深度确定性策略梯度（DDPG）算法进行互补,形成可进化PID控制器,根据微电网系统实际情况完成状态空间、动作空间与奖励函数的设计,从而对PID控制器实现自适应权重参数的调整,有效解决了电动汽车与随机扰动所带来的强非线性影响。最后,仿真结果表明：与传统PID算法相比,可进化PID控制器不仅能够更有效地抑制强随机扰动所引起的频率波动,还可以更好地适用于系统参数和结构随时间发生变化的复杂工况。     

储能飞轮径向磁轴承的H∞鲁棒控制器

磁轴承已广泛应用于飞轮储能技术中,但磁轴承建模时存在不确定性问题,采用常规方法整定的控制器难以保证其优良的稳定性和抑制扰动能力。研究基于状态空间的径向磁轴承参数不确定H∞控制,运用于磁轴承状态反馈控制器设计中,并借助于MATLAB仿真工具给出了不同情况下H∞控制的仿真波形。研究结果表明:在所考虑的刚度参数不确定性范围内,基于H∞理论的控制器具有较好的稳定性、鲁棒性和抑制扰动能力。     

光伏并网逆变器鲁棒控制器设计

针对光伏并网逆变器模型参数未知和外界干扰问题,文章运用自适应Backstepping与耗散性理论相结合的方法进行非线性控制器设计,文章设计的每个环节都符合耗散性要求,所得控制器既能保证闭环系统稳定,又具有一定的扰动抑制能力,同时自适应环节对未知参数进行实时估计。通过自控理论可知,文章的控制方法能保证系统内所有状态变量一致有界且渐近稳定;仿真结果表明,所设计的控制器能够增强系统的抗干扰能力,提高系统的鲁棒性。     

基于线性自抗扰光伏逆变器的并网电流控制研究

针对光伏并网逆变器存在内部不确定性和外部扰动的问题,文章提出了线性自抗扰控制器对并网电流进行跟踪控制。首先,基于全桥光伏逆变器的并网拓扑建立了二阶线性自抗扰控制模型,并对其控制参数进行分析、整定。然后,利用二阶线性自抗扰控制器提高系统的鲁棒性。最后在Matlab/Simulink搭建模型,仿真结果表明:控制策略能对并网电流快速跟踪,并有效抑制扰动。     

基于微分器的燃料-汽压系统直接神经网络控制

针对燃料-汽压系统中锅炉蒸汽压力的控制问题,首先设计了一种基于高增益微分器的滑模控制器,实现了无需速度和加速度测量的滑模控制,在此基础上,设计了一种神经网络控制器,实现了无需模型信息的自适应控制.通过Lyapunov稳定性分析方法证明闭环系统的稳定性.仿真结果表明,采用微分器与神经网络相结合的控制方法,可实现锅炉蒸汽压力的高性能控制,具有很好的跟踪效果.     

基于神经网络的逆变器约束自适应PI控制

针对负载扰动以及输入输出约束条件下逆变器的电压跟踪控制问题，根据同步旋转（dq）坐标系下的电压电流时域数学模型，提出一种基于神经网络的约束自适应PI控制策略。首先，利用状态转换函数将带约束的系统转换为等价的无约束系统，同时引入动态辅助系统处理输入约束问题；其次，通过反步法进行控制器设计，并为解决传统反步法“微分爆炸”问题，运用神经网络逼近包括虚拟控制律导数在内的非线性项；最后，所设计的虚拟控制律和控制律由PI项和前馈项组成，且PI控制器的增益可在线调整；理论分析表明，所提控制策略能保证闭环系统的所有误差信号最终一致有界，Matlab/Simulink平台仿真结果则进一步验证了该方案的可行性和有效性。     

电子节气门开度鲁棒自适应控制器设计

为解决电感和负载转矩扰动对电子节气门鲁棒自适应控制的影响,将电感的影响等效为未知常值电压,将负载转矩扰动的影响等效为白噪声,建立电子节气门动态方程,并基于李亚普诺夫函数设计电子节气门开度鲁棒自适应控制策略。以某电子节气门为例,在电子节气门控制仿真系统中依次输入常数信号、阶跃信号、正弦信号,分别模拟汽车匀速行驶、急加速、连续变速3种工况,进行数值仿真验证。结果表明：该控制策略可将电感和负载转矩扰动对电子节气门开度控制精度的影响控制在抑制水平内;3种工况下节气门开度均能快速、精确地跟踪目标开度,且达到稳态后跟踪误差基本维持在0°附近。     

基于扰动观测器的分数阶终端滑模电液变桨控制方法

为改善风电机组电液变桨系统的控制性能,文章提出了基于扰动观测器的分数阶终端滑模控制方法。建立风电机组电液变桨系统数学模型,利用滑模状态扰动观测器（SMSPO）对变桨系统参数的不确定性和未知扰动进行实时补偿。采用分数阶微积分理论设计终端滑模控制器的滑模面,在保证有限时间收敛的同时,改善了滑模控制自身抖动。利用Simulink进行试验验证,结果表明,该方法增强了变桨系统的抗干扰能力,削弱了系统的抖动,提高了桨距角的跟踪精度和变桨系统的稳定性。     

模糊自适应内模控制在电加热锅炉温度控制中的应用研究

针对电加热锅炉夹套温度控制具有的非线性、滞后性、易受扰动等特点,在JX-300X DCS系统设计出模糊自适应内模控制控制方法.通过建立内部模型,利用模糊规则在线调整内模控制器中滤波器时间常数Tr.采用SCX语言进行模糊自适应内模控制算法的编程设计,在DCS的平台上进行控制试验.试验结果表明,模糊自适应内模控制比常规PI...     

车辆高速避让路径跟踪控制策略

建立车辆动力学模型,研究车辆在高速避让工况下的路径跟踪控制策略。基于自抗扰控制设计车辆横摆角速度以及道路曲率2种不同的路径跟踪控制器;设计基于车辆偏航位移和偏航角度加权的线性二次最优控制器;对3种控制器控制效果进行仿真验证,分析路径跟踪侧向位移误差、侧向加速度、前轮转向角以及偏航角度变化。越野车实车测试结果表明,路径跟踪控制策略正确。     

A robust PM synchronous motor drive with adaptive uncertaintyobserver

A robust controller, which combines the merits of integral-proportional (IP) position control and adaptive control, is designed for a permanent magnet (PM) synchronous motor (SM) drive in this study. First, an IP position controller is designed according to the estimated plant model to match the time-domain command tracking specifications. Second, an adaptive uncertainty observer, which is implemented to estimate the lump of uncertainty of the controlled plant, is described. Then, a robust controller is formulated using the adaptive uncertainty observer to increase the robustness of the motor drive system. The effectiveness of the proposed controller is demonstrated by both simulation and experimental results     

An assist-mode hybrid electric motorcycle

This paper proposes a design and implementation of an assist-mode, hybrid electric motorcycle (H.E.M.). The proposed hybrid electric motorcycle is a revised vehicle from a 50 cc motorcycle and designed to match up with a 100 cc motorcycle. In order to expedite the developing phase and lower down the cost, a master–slave tracking control method is utilized. A dc servo-motor is deployed to track the speed of the rear wheel of the motorcycle and to provide more torque through power composite into the rear wheel so that the performance of hybrid electric motorcycle can be promoted. The advance of performance as well as the energy saving can both be expected. In road trip experiment, the H.E.M. prototype achieves an average gasoline mileage of 46 km l⁻¹ compared to the original 34 km l⁻¹. The overall efficiency is about 35% lift. Experimental results confirm the feasibility and prosperities of the proposed hybrid electric motorcycle.     

基于自适应非奇异智能终端滑模观测器的风力机载荷增广预测控制

为有效降低风力机在高风速运行时的不平衡载荷,提出一种基于自适应非奇异智能终端滑模观测器的载荷增广预测控制策略。首先,针对模型不匹配导致的模型预测控制性能下降的问题,将指令跟踪误差与系统状态的变化量增广为状态向量,设计增广预测模型以消除稳态跟踪误差;其次,设计自适应非奇异终端滑模观测器对系统状态进行估计,以提高控制系统的可靠性;然后,设计多目标变速灰狼优化算法同时对控制器和观测器参数寻优;最后,基于Simulink仿真平台验证了所提控制策略的有效性。结果表明,所提控制策略可有效消除稳态误差,缩短调节时间并提高控制性能。     

Exploitation of vehicle's kinetic energy in power management of tow -wheel drive electric vehicles based on ANFIS DTC-SVM comparative study

The concept of the hybrid power system in electric vehicles means that there are many sources in this electric vehicle. The electric vehicle of two-wheel drives motors doesn't exploit the two front wheel; this kind of electric vehicle prompted us to propose using the front wheels in electric vehicle energy management, which creates another energy source. The hybrid vehicle can associate more than one source to each other to secure a long time working. The two rear wheels are generally controlled by classical controllers as the DTC-SVM controller that is one of many methods to control a motor's speed. It Based on three classical controllers. We want to replace the PI speed controller with an intelligent controller and show the possibility of integrating it in this kind of control. In this paper, we exploit the electric vehicle's Kinetic energy in energy management by combining the permanent magnet synchronous generator in the vehicle's front wheels, and integrating the ANFIS controller with back motors. The generator's power represents about 19% of the total electric vehicle power. The ANFIS management strategy gave the best résults 96.6 as efficiency and the smallest consumption of Air/fuel compared with the others methods about 55.75–199 (Ipm).     

Fuel cell passenger car temperature tracking control based on cascade internal model control with nonlinear feedforward compensate

The heat dissipation capacity of the radiator of vehicle fuel cell thermal management system is easily affected by the ambient, so the system needs to have a high ability to suppress disturbance. Based on cascade internal model control with nonlinear feedforward compensate (CIFC), a temperature tracking control scheme is designed for a real vehicle fuel cell system. By defining the heat dissipation coefficient, CIFC can compensate the heat dissipation capacity of the radiator in different environment and driving condition for real vehicle so that reduce the impact of environmental factors on the fuel cell temperature management system. The control scheme is applied on a real fuel cell passenger car. Simulation and real vehicle experiments show that the scheme has good accuracy, stability, rapidity and robustness. The simulation results show that the algorithm can track the set temperature under each load current without steady-state error, the step response adjustment time is 54s and there is no overshoot both under normal atmospheric temperature and higher atmospheric temperature. The real vehicle experiment results show that the overshoot is less than 2°C, the temperature tracking speed can reach 1.1°C, and the system can stay stable with the change of vehicle speed and load without steady-state error.     

Study on the control strategy for a boost converter used in hybrid energy storage system of electric vehicles

升压型直流变换器采用滑模变结构控制策略存在收敛速度较慢、抖振剧烈等导致的动态响应品质差问题。本文提出一种双幂次滑模趋近滞环控制策略,在电流跟踪误差估计值的基础上定义滑模面以实现电流跟踪控制,依据系统的未知扰动和负载变化建立自适应状态观测器,结合李雅普诺夫函数设计自适应律,并计算自适应占空比。提出一种双幂次趋近律,根据系统不同趋近过程的特点制定参数选择标准,对系统的动态响应品质进行目的性调节,并设计滑模滞环控制器以削弱由符号函数项所引起的抖振。对以上方法进行了仿真验证,结果显示可有效改善系统的动态特性和电流控制鲁棒性。     

An optimization scheduling method of electric vehicle virtual energy storage to track planned output based on multiobjective optimization

Electric vehicle virtual energy storage technology can effectively improve the utilization of renewable energy. Aiming at the impact of the uncertainty of electric vehicle on the power grid, an optimized dispatching method of hybrid energy storage systems based on multiobjective optimization in the scenario of tracking plan output is proposed in this paper. The predicted value of the photovoltaic power obtained by the particle swarm optimization (PSO)-back propagation (BP) neural network is used to formulate the planned output of photovoltaic power generation, and the principle component analysis algorithm is used to extract the main features affecting photovoltaic power generation to further improve the prediction accuracy of photovoltaic output power. From the perspective of the service life of electric vehicles, a two-stage optimal control method of hybrid energy storage systems based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to achieve energy distributions between electric vehicles and supercapacitors. Fully consider the benefits of electric vehicle users and the capacity of tracking plans, a multiobjective optimization model of hybrid energy storage systems to track planned output is established, and the nondominated sorted genetic algorithm-III is adopted to solve the model. The validity of the model is verified by a simulation test of actual operating data of a business park in China. The simulation results show that after the optimized control, the average absolute error of the deviation power reduces from 1.092 to 0.0528 MW, power fluctuating times of electric vehicles decreases from 151 to 80, and the daily income benefit increases from $404.468 to $483.116 in the cloudy day. The method proposed in this paper can effectively improve the controllability of renewable energy, and provide a theoretical basis for the application of electric vehicle virtual energy storage technology.     

发动机怠速的离散滑模控制

设计了发动机怠速的离散滑模(DSM)控制器,用已开发出的4缸、1.4L的AJR发动机怠速控制系统的非线性模型进行发动机怠速转速的控制。实验结果表明,与原机的控制器相比较而言,DSM控制器在跟踪期望怠速转速及抗干扰等方面具有优良的性能。