电动汽车用感应电机动态平衡点的反馈耗散Hamilton控制

针对电动汽车行驶过程中路况变化频繁,其对应的配套感应电机给定转速和负载不断变化,从而导致系统平衡点也随之变化的特点和忽略铁损引起的控制不精确的问题,研究了基于动态平衡点的计及电动汽车用感应电机反馈耗散Hamilton控制问题.首先根据感应电机的工作特性计算出平衡点,然后选取适当的状态反馈,通过预置反馈的方法建立了系统的动态模型,并基于能量耗散特性实现了对电动汽车用感应电机在动态平衡点处的反馈耗散Hamilton控制,保证了整个系统的全局稳定性.最后仿真结果验证了该控制策略的有效性.     

感应电动机效率优化的自抗扰控制研究

针对现有电动机效率优化控制方法存在控制算法复杂的问题,提出了一种基于改进自抗扰控制器的动态解耦控制策略。自抗扰控制器省去了微分跟踪器,采用线性的扩张状态观测器和线性状态误差反馈控制律,使控制算法得以简化。仿真和实验结果表明,该控制策略在保证电动机轻载运行时效率优化的同时,具有较强的抗干扰能力和鲁棒性,提高了效率优化过程中电动机转速的动态响应速度。     

考虑铁损的电动汽车用感应电机矢量控制及其能量优化策略

电动汽车用感应电机励磁电感一般较小,高速时铁损大,采用经典矢量控制策略存在轻载低效和由忽略铁损引起的控制不精确等问题.首先根据同步旋转坐标系下考虑铁损的感应电机动态数学模型,分析了铁损对按转子磁场定向矢量控制的影响,给出了动态和稳态两种补偿方案.然后从调节磁通水平的角度,提出了一种基于损耗模型的感应电机能量优化控制策略,并讨论了铁损等效电阻变化对优化控制的影响.仿真和实验结果表明,给出的补偿控制策略克服了经典矢量控制磁场定向及转矩控制不准确的缺陷;提出的的能量优化控制策略不但节能效果明显,而且具有寻优速度快、转矩和转速波动小等优点,为高性能要求的电动汽车电驱动系统高效运行提供了有效途径.     

电动汽车用内置式永磁同步电机驱动系统效率优化研究

随着电动汽车的普及及其技术发展,提升电动汽车驱动系统效率对于提高续航和节约能源均有重要意义.现有中小型电动汽车多采用内置式永磁同步电机(IPMSM)驱动系统,对此文章提出一种IPMSM驱动系统效率工程化优化方法.其从IPMSM直接转矩控制角度出发,首先通过理论分析得到IPMSM的铁损和铜损模型以及控制器损耗的等效损耗模...     

感应电机最大转矩电流比的预测函数控制研究

研究感应电动机效率优化的最大转矩电流比控制问题,设计了基于Laguerre模型的稳定自适应预测函数控制器(PFC).采用前馈补偿解耦设计的思想,将系统分解成两个具有可测扰动的单入单出子系统,通过解方程组实现了PFC的设计.仿真实验结果表明,与传统的PI电流控制器相比,该控制器能在线辨识模型参数,跟踪精度高,响应迅速,抗扰能力强,取得了良好的控制效果.     

矿山变频矢量控制系统效率优化方案研究

针对矿山变频调速系统感应电动机采用恒定磁通控制,存在损耗严重、运行效率低的问题,提出了一种矿山变频矢量控制系统的效率优化控制方案。该方案在两相旋转d-q坐标系下,建立了考虑定、转子铜损和铁损的系统损耗模型,推导得到不同运行工况下电动机损耗与转子磁链之间的对应关系;为改善因弱磁运行而造成的系统动态响应过慢问题,通过优化转矩电流和磁链电流比重关系,获得电动机电磁转矩的快速响应和转速跟踪。基于DSP+FPGA控制系统搭建22kW三电平感应电动机调速系统实验平台,实验结果表明,该优化方案可以降低感应电动机矢量控制系统运行损耗,提高系统运行效率,并保证系统的稳态性能和动态响应性能。     

DSP芯片TMS320LF2407在感应电机控制中的应用

本文提出了一种基于微分几何中反馈线性化方法的变结构控制算法，它在用于对间接磁场定向下的感应电动机速度和磁通实现精确解耦的同时，还具备对电动机参数变化的鲁棒性。基于DSP的仿真实验结果证明了控制策略的有效性。     

计及铁损时电动汽车用感应电机的Hamilton建模及无源控制

针对电动汽车电驱动系统的非线性特点,采用端口受控Hamilton系统理论与无源性控制原理研究了计及铁损的电动汽车用感应电机系统的建模和控制问题.首先,选取系统的总能量作为Hamilton函数,推导出考虑铁损的感应电机端口受控Hamilton模型,然后利用系统的互联和阻尼配置以及能量成形对闭环电动汽车电驱动系统进行无源控制,并分析了闭环系统的稳定性.由于闭环系统的Hamilton函数可作为Lyapunov存储函数,从而使控制器设计和稳定性分析更容易,控制器更加简单和易于实现.仿真结果验证了新控制策略的有效和快速性.     

电动汽车增程器燃油效率优化控制

针对如何提高增程器燃油效率,降低整车油耗的问题,提出了一种基于PRP共轭梯度法的增程器燃油效率优化控制方法.首先,以产生给定能量燃油效率最高为优化性能指标,以发动机转矩和发电机转矩为寻优变量,建立了增程器燃油效率优化控制问题的离散系统模型.然后,对算法实现过程中发动机、发电机的最高转速和最大转矩限制的处理方法进行了阐述,给出了基于PRP共轭梯度法的增程器燃油效率优化控制问题的数值实现方法的详细步骤.最后,仿真和实验结果表明本文提出的优化控制方法可以有效提高增程器的燃油效率.     

轮毂电机驱动电动汽车横摆稳定性控制

针对轮毂电机驱动的电动汽车的横摆稳定性问题,提出了一种基于极限学习机(ELM)网络的直接横摆力矩控制方法.采用自适应积分终端滑模(AITSM)控制策略设计横摆角速度控制器,并通过ELM的快速在线学习能力实现在闭环系统中进行集总干扰估计和补偿.控制器的主要贡献在于,一是实现横摆角速度跟踪误差的有限时间收敛,二是引入了 E...     

Robust output feedback learning control for induction motor servo drives

This paper addresses the output feedback tracking control problem for induction motor servo drives with mechanical uncertainties: rotor angle, rotor speed and stator currents are assumed to be available for feedback. A robust adaptive learning control is designed under the assumption that the reference profile for the rotor angle is periodic with known period: it ‘learns’ the periodic disturbance signal by identifying the Fourier coefficients of any truncated approximation; ??₂ and ??_∞ transient performances are guaranteed in the ‘learning phase’. It is shown that, for any motor initial condition belonging to an arbitrary given compact set, by properly setting the control parameters: (i) the rotor position and flux modulus tracking errors exponentially converge to residual sets, which may be arbitrarily reduced by increasing the number of terms in the truncated Fourier series; (ii) when the unknown periodic disturbance can be represented by a finite Fourier series, the rotor position and flux modulus tracking errors exponentially converge to zero. As in field oriented‐control, the control algorithm generates references for the magnetizing flux component and for the torque component of the stator current leading to significant simplifications for current‐fed motors. Copyright © 2008 John Wiley & Sons, Ltd.     

Learning control for induction motor servo drives with uncertain rotor resistance

The tracking control problem for induction motor servo drives with mechanical and electrical uncertainties is addressed. Under the assumptions that the reference profile for the rotor angle is periodic of known period and the rotor flux modulus reference signal is persistently exciting, a robust adaptive learning control is designed, which is adaptive with respect to the uncertain rotor resistance and is able to ‘learn’ the periodic disturbance signal due to mechanical uncertainties by identifying the Fourier coefficients of its truncated approximation.     

MPC-based torque control of permanent magnet synchronous motor for electric vehicles via switching optimization

In order to effectively achieve torque demand in electric vehicles (EVs), this paper presents a torque control strategy based on model predictive control (MPC) for permanent magnet synchronous motor (PMSM) driven by a two-level three-phase inverter. A centralized control strategy is established in the MPC framework to track the torque demand and reduce energy loss, by directly optimizing the switch states of inverter. To fast determine the optimal control sequence in predictive process, a searching tree is built to look for optimal inputs by dynamic programming (DP) algorithm on the basis of the principle of optimality. Then we design a pruning method to check the candidate inputs that can enter the next predictive loop in order to decrease the computational burden of evaluation of input sequences. Finally, the simulation results on different conditions indicate that the proposed strategy can achieve a tradeoff between control performance and computational efficiency.     

Shortest path stochastic control for hybrid electric vehicles

When a hybrid electric vehicle (HEV) is certified for emissions and fuel economy, its power management system must be charge sustaining over the drive cycle, meaning that the battery state of charge (SOC) must be at least as high at the end of the test as it was at the beginning of the test. During the test cycle, the power management system is free to vary the battery SOC so as to minimize a weighted combination of fuel consumption and exhaust emissions. This paper argues that shortest path stochastic dynamic programming (SP‐SDP) offers a more natural formulation of the optimal control problem associated with the design of the power management system because it allows deviations of battery SOC from a desired setpoint to be penalized only at key off. This method is illustrated on a parallel hybrid electric truck model that had previously been analyzed using infinite‐horizon stochastic dynamic programming with discounted future cost. Both formulations of the optimization problem yield a time‐invariant causal state‐feedback controller that can be directly implemented on the vehicle. The advantages of the shortest path formulation include that a single tuning parameter is needed to trade off fuel economy and emissions versus battery SOC deviation, as compared with two parameters in the discounted, infinite‐horizon case, and for the same level of complexity as a discounted future‐cost controller, the shortest‐path controller demonstrates better fuel and emission minimization while also achieving better SOC control when the vehicle is turned off. Linear programming is used to solve both stochastic dynamic programs. Copyright © 2007 John Wiley & Sons, Ltd.     

基于二次型性能指标的混合动力汽车实时优化控制

为解决混合动力系统实时优化控制问题,本文提出了一种基于二次型性能指标最优的混合动力汽车功率分配优化方案.通过合理的假设和近似,建立了混合动力系统的线性模型,并利用二次型最优控制理论将混合动力最优控制问题转化为二次型最优调节问题进行求解,得到了一个结构简单的实时优化控制算法.5种道路工况下的仿真结果表明,本文提出的控制方法在未来道路工况未知的情况下能够实现混合动力系统的实时优化控制,且节油率与离线计算以燃油消耗最小为性能指标的全局最优控制的节油率相近.     

电动车燃料电池控制系统

如何改善燃料电池的输出性能是燃料电池电动车的一个重要问题. 首先燃料电池是一个多变量复杂系统.燃料电池的工作和环境温度、流量及湿度对燃料电池实际功率输出有重大影响. 然后提出一种自适应模糊复合控制算法。考虑到燃料电池的多动态特性, 加入自整因子层对量化因子和比例因子进行在线修改, 从而改善了控制器的静态和动态特性. 有实验结果证明所提出算法的有效性. 最后, 详细设计燃料电池电动车的能量控制系统.     

Vector control for induction motor drives based on adaptive variable structure control algorithm

This paper presents a new adaptive robust control for induction motor drives. The proposed control scheme is based on the so‐called field oriented control theory that allows to control the induction motor like a separately excited direct current motor drive, where the field flux (produced by the field current) and the armature flux (produced by the armature current) are decoupled. The robust control law is based on the sliding mode control theory, but unlike the traditional sliding mode control schemes, the proposed design incorporates an adaptive switching gain that avoids the need of calculating an upper limit of the system uncertainties. Moreover the proposed control law is smoothed out in order to avoid the high control activity inherent to the switching control laws. The resulting closed loop system is proven to be stable using the Lyapunov stability theory. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society