Guangzhao Luo

Lithium Polymer Battery State-of-Charge Estimation Based on Adaptive Unscented Kalman Filter and Support Vector Machine

An accurate algorithm for lithium polymer battery state-of-charge (SOC) estimation is proposed based on adaptive unscented Kalman filters (AUKF) and least-square support vector machines (LSSVM). A novel approach using the moving window method is applied, with AUKF and LSSVM to accurately establish the battery model with limited initial training samples. The effectiveness of the moving window modeling method is validated by both simulations and lithium polymer battery experimental results. The measurement equation of the proposed AUKF method is established by the LSSVM battery model and AUKF has the advantage of adaptively adjusting noise covariance during the estimation process. In addition, the developed LSSVM model is continuously updated online with new samples during the battery operation, in order to minimize the influence of the changes in battery internal characteristics on modeling accuracy and estimation results after a period of operation. Finally, a comparison of accuracy and performance between the AUKF and UKF is made. Simulation and experiment results indicate that the proposed algorithm is capable of predicting lithium battery SOC with a limited number of initial training samples.

A Novel Nonlinear Modeling Method for Permanent-Magnet Synchronous Motors

A field-circuit-coupled parameter adaptive modeling method for a permanent magnet synchronous motor is proposed in this paper. The model combines the merits of a mathematical model and a magnetic field model. It takes into consideration the magnetic saturation, current harmonics, cross coupling, eddy current, and hysteresis loss effect in terms of the corresponding adaptive parameters obtained from the JMAG-finite element analysis (JMAG-FEA) tool in the model. To validate the effectiveness of the proposed model, three modeling methods are comparatively studied through an offline simulation. In addition, a hardware-in-the-loop experiment platform is built combining one DSP-based controller and a dSPACE-based controller, where the three models are located. The results of both, the simulation and HIL experiment, demonstrate the accuracy of the proposed model and the feasibility of the integration scheme of motor design and control. Moreover, the proposed model approximates the actual machine well and can be customized conveniently according to different requirements.

A robust battery state-of-charge estimation method for embedded hybrid energy system

An optimized state of charge (SOC) estimation method is critical for energy control strategy in hybrid energy system. For an embedded system, the executed algorithm should be less time consuming and also robust on measurement noise from sensors. Moreover, the estimation method should also be insensitive to initial SOC for the purpose of avoiding battery relaxing time in real application. The proposed method in this paper combines adaptive unscented Kalman filter (AUKF) and multivariate adaptive regression splines (MARS) to meet the above demands of embedded hybrid energy system. Samples which consist of battery current, terminal voltage and temperature are used to for MARS model training. The effectiveness and robustness of the proposed method is validated by experimental test. Also, the proposed method is compared with least squares support vector machine (LSSVM) based method in estimated accuracy and time consumption. Experiment results indicate that the proposed method is less time consuming as well as good accuracy is guaranteed.

A data driven model for accurate SOC estimation in EVs

Accurate state of charge (SOC) is critical for battery energy management system in electric vehicle (EV) application. Overcharge and over discharge will shorten batterys lifespan and induce potential safety problem, which may even permanently damage the lithium-ion battery. Thus, a data driven model is proposed for improving the accuracy of SOC estimation in this paper. A preliminary mathematic model under constant current is established, which can match the primary high SOC stage. Since the battery model is nonlinear, model free adaptive control (MFAC) is used to get the dynamic linearization model and accomplish SOC estimation process on the basis of the mathematic model. With the small sample scale of new data updated, a data driven model based on partial least squares (PLS) is obtained online. The accuracy of the mathematic model also decreases during the operating process. Finally, the calculated values from the two different models are mixed for an accurate SOC. Experimental results on lithium polymer battery prove the effectiveness of the proposed method.

Finite-control-set model predictive current control for PMSM using grey prediction

This paper proposes a finite control set model predictive current control (FCS-MPCC) with grey prediction for surface mounted PMSM drives. The basic FCS-MPCC is combined with grey prediction to improve the dynamic performance current control. Grey system takes into account both certain and uncertain information in real system, and use the rolling optimal grey sequence to predict the control current for cost function. The performance is demonstrated in both simulation and experiment. The results illustrate that FCS-MPCC with grey prediction expresses a good current response under the load disturbance and good performance under different parameter variations. Meanwhile the steady-state performance of current can be assured.

Secondary Saliency Tracking-Based Sensorless Control for Concentrated Winding SPMSM

Sensorless ac drives have been widely adopted in many industry applications. However, the characteristic of strong multiple saliencies is still a main drawback impeding applying sensorless control over several types of machine, e.g., surface-mounted permanent magnet synchronous machine with concentrated windings (cwSPMSM). This work proposes a novel secondary saliency tracking (SST) algorithm to implement the sensorless control exclusively for such machines at low speed range. The saliency signals of a typical cwSPMSM under consideration are experimentally investigated. The stator background and physical mechanism of its strong multiple saliencies are explained in detail. Instead of processing the primary saliency signal, secondary saliency signal that has a better signal to noise ratio and more precise resolution is processed by a specially designed bandpass filter, and an adaptive notch filter for speed and rotor position estimation. Finally, the effectiveness and accuracy of the newly proposed SST method are verified by experimental results.

Decoupling of secondary saliencies in sensorless AC drives using repetitive control

To decouple strong secondary saliencies in sensorless AC drives, a repetitive control method in an angle domain is proposed. This method regards secondary saliencies as periodic disturbances relative to electrical angle and generates the same saliencies to compensate them. The proposed method is easy to implement and proved effective by experiments.

Hybrid sensorless control for SPMSM With multiple saliencies

Surface-mounted permanent magnet synchronous machine with concentrated windings (cwSPMSM) is widely adopted in many industry applications. However, strong multiple saliencies is a main drawback impeding applying sensorless control over this type of machine. This work proposes a hybrid sensorless control scheme which integrates a novel Secondary Saliency Tracking (SST) algorithm and an improved Active Flux Observer (AFO) for cwSPMSMs. The saliency signals of a typical cwPMSM under consideration are experimentally investigated. Instead of processing the primary saliency signal, secondary saliency signal which has a better signal to noise ratio and more precise resolution, is processed by a special designed band pass filter for speed and rotor position estimation reaching a very low speed range with full-load. An improved AFO method with robust structure but effective performance is adopted for higher speed range estimation. The transient phase (switching between the SST and AFO) performance is guaranteed by a smooth transition region design, reaching a Hybrid Sensorless Control with wide speed range. Finally the effectiveness and accuracy of the newly proposed Hybrid Sensorless Control method are both verified by experimental results.

Grey control strategy of predictive current for PMSM

In this paper, to further improve the performance of dynamic and anti-interference of the permanent magnet synchronous motor (PMSM) control system, the grey control strategy of predictive current for PMSM is proposed by analyzing the characteristics of the actual current response in control system and making full use of historical current data to adjust the current value quickly to follow the change of the given value. The off-line simulation is carried out in condition of rated load and sudden load. Compared with the classical vector control and traditional predictive current control, the proposed algorithm reaches the given speed rapidly and has the performance of good response and small overshoot. It also has the better dynamic and anti-interference performance. To further verify the effectiveness of the proposed control strategy, the real-time simulation of the proposed algorithm is carried out by dSPACE system.