Sensorless torque control scheme of induction motor for hybrid electric vehicle

In this paper, the sensorless torque robust tracking problem of the induction motor for hybrid electric vehicle （HEV） applications is addressed, Because motor parameter variations in HEV applications are larger than in industrial drive system, the conventional field-oriented control （FOC） provides poor performance. Therefore, a new robust PI-based extension of the FOC controller and a speed-flux observer based on sliding mode and Lyapunov theory are developed in order to improve the overall performance. Simulation results show that the proposed sensorless torque control scheme is robust with respect to motor parameter variations and loading disturbances. In addition, the operating flux of the motor is chosen optimally to minimize the consumption of electric energy, which results in a significant reduction in energy losses shown by simulations.     

电动汽车用感应电动机矢量控制技术仿真实现

当今世界节能和环保备受关注,使得电动汽车技术正在加速发展。电机技术是发展电动汽车的关键技术之一,该文在分析矢量控制原理的基础上将其应用于电动汽车的驱动电动机控制。仿真表明电动机的控制性能得到了明显改善,能够满足电动汽车运行工况复杂多变的需求。     

Fuzzy torque distribution control for a parallel hybrid vehicle

A fuzzy torque distribution controller for energy management (and emission control) of a parallel hybrid electric vehicle is proposed. The proposed controller is implemented in terms of a hierarchical architecture which incorporates the mode of operation of the vehicle as well as empirical knowledge of energy flow in each mode. Moreover, the rule set for each mode of operation of the vehicle is designed in view of an overall energy management strategy that ranges from maximal emphasis on battery charge sustenance to complete reliance on the electrical power source. The proposed control system is evaluated via computational simulations under the FTP75 urban drive cycle. Simulation results reveal that the proposed fuzzy torque distribution strategy is effective over the entire operating range of the vehicle in terms of performance, fuel economy and emissions.     

An electrostatic induction motor utilizing electrical resonance for torque enhancement

An electrostatic induction motor utilizing electrical resonance to enhance its torque is introduced in this paper. The motor has patterned electrodes embedded on both of its stator and rotor, and driven by three-phase excitation voltage applied to its stator electrodes. The rotor electrodes are connected to external inductances and resistances through slip rings. The torque of the motor is maximized at resonant condition, where the slip frequency is equal to the resonance frequency of the system. A working prototype of the motor was constructed with 18-layered film electrodes. The maximum torque of the motor was 20 mNm at 10 rpm with an excitation by three-phase voltage of 0.9 kV/8.25 kHz. The motor could start itself and rotate in balance with frictions at a speed of 75 rpm with 0.9 kV/8.35 kHz excitation.     

A minimum-time control strategy for torque tracking in permanent magnet AC motor drives

A minimum-time torque control strategy for permanent-magnet AC motor drives is presented. The proposed technique neither requires the solution of a HJB-type equation, which would be practically unfeasible, nor uses Pontryagin's maximum principle. Instead, the solution is obtained by an ad hoc procedure based on the computation of reachability and controllability sets. In principle, the optimal control strategy can be carried out by iteratively solving a fourth-degree polynomial equation. For its efficient implementation, an algorithm based on Sturm sequences is suggested. The sequence of online operations required by the algorithm for a given tolerance on the optimal time is illustrated. The method has been tested on a laboratory prototype. Experimental results show the effectiveness of the technique.     

Sensorless direct torque control of an induction motor by a TLS-based MRAS observer with adaptive integration

This article presents a new speed and flux estimation algorithm for high-performance direct torque control (DTC) induction motor drives based on model reference adaptive systems (MRAS) observers using linear artificial neural networks (ANNs). Two completely new improvements of MRAS speed and flux observers are presented here: the first is a solution to the open-loop integration problem in the reference model, based on the voltage model of the induction machine, by means of a new adaptive neural integrator, the second is the employment of a new adaptation law in the ANN adaptive model, based on the total least-squares (TLS) technique. In particular, the adaptive neural integrator is based on two adaptive noise filters which completely cancel any DC drift present in the voltage or current signals to be integrated. This neural integrator does not need any a priori training of its two only neurons, adapting itself on-line. With regard to the ANN-based adaptive model, since the most suitable least-square technique to be used for training is the TLS technique, here the neuron is trained on-line by means of a TLS EXIN algorithm which is the only neural network able to solve a TLS problem recursively. Also the TLS EXIN algorithm does not require any a priori training, since it adapts itself recursively on-line. Moreover, to improve the dynamical performances of the speed loop of the drive, the adaptive model has been used as predictor, i.e. without any feed-back between its outputs and its inputs. The sensorless algorithm has been verified experimentally both on the classic DTC technique and on the DTC-SVM (space vector modulation), by adopting a proper test set-up. The speed observer has been tested in the most challenging operating conditions. The experimental results show that the dynamical performances of the sensorless drive are comparable or even better than those obtained with the corresponding DTC drives with encoders as for the medium to high-speed ranges. As for low-speed ranges, the presented sensorless DTC algorithm outcomes the performance presented in the literature for MRAS systems, thus permitting to have an accurate estimation equal or better than that obtainable with more complex observers. Finally, experimental results show that the MRAS speed observer is robust to load torque perturbations and permits zero-speed operation at no-load conditions.     

Diesel engine torque ripple reduction through LPV control in hybrid electric vehicle powertrain: Experimental results

This paper presents a case of persistent harmonic active control for an HEV (Hybrid Electric Vehicle) powertrain. The active control is adapted for a hybrid powertrain consisting of a one-cylinder diesel engine, coupled with a PMSM (Permanent Magnet Synchronous Machine). The PMSM assures the propulsion of the vehicle, as in conventional mild-hybrid electrical vehicles. In addition, it provides speed ripple reductions of the diesel engine. Due to the HEV speed variation, the active control must match this variation. The speed is introduced as a parameter in order to devise an LPV (linear parameter varying) control strategy. The suitability of LPV control for engine torque ripple reduction is demonstrated through a torque control implementation of the PMSM. The control strategy uses the internal model principle of multi-sinusoidal persistent disturbances. The controller is designed to involve several steps, including LMI-based (Linear Matrix Inequalities) optimization. The results show that, for the first and second orders of the ripple, speed oscillations can be reduced when the speed varies. An industrial test bed is used to validate the effectiveness of the approach and the power consumption of the strategy is analyzed.     

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.     

轮毂电机驱动电动汽车各轮毂电机扭矩分配算法的仿真和评价

为精确控制轮毂电机驱动电动汽车各轮毂电机的扭矩,获得更好的车辆动力学控制性能,利用Matlab／Simulink对再分配伪逆算法（Redistributed Pseudo—Inverse algorithm,RPI）、层叠广义逆法（Cascading Generalized Inverse algorithm,CGI）和加权最小二乘法（Weighted Least-Squares algorithm,WLS）进行数值仿真,对这3种算法从计算速度、迭代次数和计算精度等方面进行对比分析与评价,结果表明WLS综合性能最优．     

Design of genetic-fuzzy control strategy for parallel hybrid electric vehicles

Hybrid Electric Vehicles (HEVs) generate the power required to drive the vehicle via a combination of internal combustion engines and electric generators. To make HEVs as efficient as possible, proper management of the different energy elements is essential. This task is performed using the HEV control strategy. The HEV control strategy is the algorithm according to which energy is produced, used and saved. This paper describes a genetic-fuzzy control strategy for parallel HEVs. The genetic-fuzzy control strategy is a fuzzy logic controller that is tuned by a genetic algorithm. The objective is to minimize fuel consumption and emissions, while enhancing or maintaining the driving performance characteristics of the vehicle. The tuning process is performed over three different driving cycles including NEDC, FTP and TEH-CAR. Results from the computer simulation demonstrate the effectiveness of this approach in reducing fuel consumption and emissions without sacrificing vehicle performance.     

基于模糊技术感应电机最大效率控制策略的研究

针对感应电机效率控制过程中存在的寻优速度慢以及对电机参数依赖性大等问题,基于模糊技术,提出一种新的最大效率控制策略.由电动机损耗模型选取最优磁链搜索初值,根据输入功率偏差的大小自动调整模糊控制维数,大大提高了搜索速度,同时确保了效率寻优的稳定性.采用前馈补偿方法,并引入一阶微分环节解决了效率控制中的低频转矩脉动和转矩快速响应问题.仿真和实验结果表明,所提出的控制策略是有效的.     

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.     

A novel induction motor control scheme using IDA-PBC

A new control scheme for induction motors is proposed in the present paper, applying the interconnection and damping assignment-passivity based control （IDA-PBC） method. The scheme is based exclusively on passivity based control, without restricting the input frequency as it is done in field oriented control （FOC）. A port-controlled Hamiltonian （PCH） model of the induction motor is deduced to make the interconnection and damping of energy explicit on the scheme. The proposed controller is validated under computational simulations and experimental tests using an inverter prototype.     

State observers are unnecessary for induction motor control

Induction motors constitute a theoretically interesting and practically important class of nonlinear systems, which are evolving into a benchmark example for nonlinear control. They are described by a fifth-order nonlinear differential equation with two inputs, and only three state variables available for measurement. A lot of research in the field has been devoted to the design of observers which, combined with a suitable control strategy, would yield stable behaviour. Our contribution in this paper is to show that the control objective can be achieved without reconstruction of the motor state. Specifically, we present a globally stable nonlinear dynamic output feedback controller for torque tracking of induction motors which does not rely on state reconstruction ideas. Another important feature of our sheme is that the control law is globally defined, even in startup. This stems from the fact that we do not aim at linearizing the system dynamics, but instead exploit the energy dissipation properties of the motor model. For the sake of illustration we present the result for a model described in the stator frame (ab model), but the theory applies as well to models expressed in a rotating frame (dq model). We also show how, as a particular case of torque tracking, we can solve the rotor speed tracking problem.     

An MRAS method for sensorless control of induction motor over a wide speed range

This paper addresses the problem of wide speed range sensorless control of induction motor.The proposed method is based on model reference adaptive system (MRAS),in which the current model serves as the adjustable model,and a novel hybrid model integrating the modified voltage model (MVM) and high-frequency signal injection method (HFSIM) are established to serve as the reference model.The HFSIM works together with MVM to improve the performance of the rotor speed and rotor flux position estimation at low speed,whereas at high speed,the MVM acts alone.In addition,a rotor resistance online estimation scheme is proposed to update the rotor resistance contained in the adjustable model and to ensure the estimation accuracy further.Experimental results show that the proposed MRAS method is very effective from low to high speed range,including zero speed.     

Sensorless induction motor drive: An innovative component for advanced motion control

This paper reports the basic concept and recent industrial applications of a sensorless vector-controlled speed-control system for an inverter-fed induction motor in Japan. The vector control artificially gives the linear torque controllability, which is inherent for a separately excited DC motor, to the induction motor drive. First, in this paper, the fundamental concept of vector control is introduced. Next, representative speed-sensorless vector-control systems will be related. Also, a method for the automatic measurement of the electrical parameters of the induction motor is introduced. To know the motor parameters precisely is especially important for a speed-sensorless vector-control system. Finally, several examples of industrial applications of the sensorless speed control are described.     

Observer-Based Nonlinear Control of A Torque Motor with Perturbation Estimation

This paper presents an observer-based nonlinear control method that was developed and implemented to provide accurate tracking control of a limited angle torque motor following a 50Hz reference waveform. The method is based on a robust nonlinear observer, which is used to estimate system states and perturbations and then employ input-output feedback linearization to compensate for the system nonlinearities and uncertainties. The estimation of system states and perturbations allows input-output linearization of the nonlinear system without an accurate mathematical model of nominal plant. The simulation results show that the observer-based nonlinear control method is superior in comparison with the conventional model-based state feedback linearizing controller.     

煤矿井下无轨胶轮电动车矢量控制系统仿真研究

以三相交流异步电动机为研究对象,分析了异步电动机矢量控制理论,建立了无轨胶轮电动车用异步电动机矢量控制系统的仿真模型,并进行了仿真分析。仿真结果表明,该电动车矢量控制系统具有良好的稳态和动态性能。     

Predictive energy management for hybrid vehicle

Hybrid vehicle control strategies are algorithms devoted to the energy management. At each sampling time they choose the powertrain operating point in order to minimize a criterion, usually the fuel consumption. In simulation, an optimization algorithm can be derived from the minimum principle. For real time control, a Model Predictive Control scheme can be used but it requires the prediction of the future driving conditions. Their time ordered prediction is very difficult. Moreover, if the optimal costate is constant, only the prediction of their distribution is sufficient and allows deriving a real time control strategy. Experimental results are provided to illustrate the benefits of this approach.     

Extended complex Kalman filter for sensorless control of an induction motor

This paper deals with the design of an extended complex Kalman filter (ECKF) for estimating the state of an induction motor (IM) model, and for sensorless control of systems employing this type of motor as an actuator. A complex-valued model is adopted that simultaneously allows a simpler observability analysis of the system and a more effective state estimation. The observability analysis of this model is first performed by assuming that a third order ECKF has to be designed, by neglecting the mechanical equation of the IM model, which is a valid hypothesis when the motor is operated at constant rotor speed. It is shown that this analysis is more effective and easier than the one performed on the corresponding real-valued model, as it allows the observability conditions to be directly obtained in terms of stator current and rotor flux complex-valued vectors. Necessary observability conditions are also obtained along with the well-known sufficient ones. It is also shown that the complex-valued implementation allows a reduction of 35% in the computation time w.r.t. the standard real-valued one, which is obtained thanks to the lower dimensions of the matrices of the ECKF w.r.t. the ones of the real-valued implementation and the fact that no matrix inversion is required. The effectiveness of the proposed ECKF is shown by means of simulation in Matlab/Simulink environment and through experiments on a real low-power drive.