Linfeng Zheng

A novel model predictive sliding mode control for AC/DC converters with output voltage and load resistance variations

This paper presents a novel model predictive sliding mode control (MPSMC) strategy for a three-phase grid connected AC/DC converter. The grid current is predicted for controlling the active and reactive power flows for the next sampling time instead of predicting them directly. This MPSMC scheme employs a sliding mode control (SMC) algorithm to calculate the reference values of active and reactive powers in the cost function. The reaching, existing and tracking conditions are analyzed to ensure that the designed sliding surface and control law are effective to control the system. The simulation results by Matlab/Simulink show that the MPSMC strategy is able to meet the system requirements of active and reactive powers, as well as the DC output voltage. Compared with the results obtained from the conventional model predictive PI control (MPPIC) scheme, the proposed strategy can improve the dynamic performance dramatically in terms of the response speed under system disturbances, such as varying output voltage and load demand.

A comparative study of battery balancing strategies for different battery operation processes

To reduce the effect of cell inconsistencies and improve battery pack capacity, battery balancing techniques are essentially required in battery management systems (BMSs). This paper presents a comparative study of four battery balancing strategies for different battery operation processes. These balancing strategies are developed from the state-of-the-art battery balancing circuits and algorithms reported in recent literature. The performance of balancing strategies is evaluated and compared by battery pack maximum available capacity, state of charge (SOC) variances at the end of charge (EOC) and end of discharge (EOD). The comparative result is helpful for BMSs developers to employ optimal balancing strategies in actual applications.

Error analysis of SOC estimation based on PI observer

The PI observer model for SOC estimation of battery is established based on the first order Thevenin model. On the basis of this model, we analyze the factors influencing the model parameters of battery and the trend of parameters. Based on state equations of SOC, steady state error of SOC estimation is deduced theoretically, and a practical example is provided. From the example can we see, SOC estimation based on PI observer is affected by the accuracy of the parameters such as the internal resistance, the open circuit voltage and the capacity of the battery model, and these factors have different extent of influence. The simulation results show that this model can accurately estimate the SOC of the battery with exact parameters.

Embedded implementation of SOC estimation based on the Luenberger observer technique

A new method of the SOC estimation using the Luenberger observer technique is presented in this paper. The simulation model of the proposed SOC estimation method is modeled in Matlab/Simulink and is generated into code automatically by Matlab/RTW. Then the code is embedded into battery management system (BMS) for SOC estimation. To investigate the feasibility and reliability of the proposed approach, the constant-current constant-voltage (CC-CV) charging test and Beijing dynamic stress test (BJDST) are performed to estimate the SOC. Results show that the proposed approach can accurately estimate the SOC within 2% error, which is adaptable for engineering application.

Model-Predictive Sliding-Mode Control for Three-Phase AC/DC Converters

This paper presents a model-predictive sliding-mode control (MPSMC) scheme for a three-phase ac/dc converter to achieve better stability and dynamic performances. In the conventional model-predictive control method, a proportional–integral (PI) controller is used to generate the active power reference. This traditional model-predictive PI control (MPPIC) scheme, however, produces a large overshoot/undershoot, a long settling time, and a large steady-state error under disturbances. To overcome these deficiencies, a sliding-mode controller is employed to replace the PI controller. Since the control law and the controller are designed based on the system model, the proposed MPSMC scheme can reduce the effects of unexpected disturbances, such as the output voltage demand and the resistance load variations. Both methods have been simulated in MATLAB/Simulink during various disturbances. Compared with the performances of MPPIC, the results obtained from MPSMC show that the settling time of the dc voltage can be minimized by about 91%, and the overshoot can be eliminated from 9.13% during the steady-state progress. The active and reactive power from MPSMC can also be controlled to the desired values, respectively, with a much smaller overshoot/undershoot and a faster response speed. Similar dynamic improvements can be achieved with MPSMC when the dc voltage demand varies. The simulation results are validated by experimental results.

Moving average filter-based model predictive control for electric vehicles bidirectional chargers

The paper proposes a moving average filter (MAF)-based model predictive control (MPC) for the electric vehicles (EVs) bidirectional chargers. Grid virtual flux is used to estimate the grid voltage through a low pass filter (LPF). An MAF, which acts as an ideal LPF, can eliminate the effect of unbalanced/distorted grid voltage and unknown characteristic harmonics. Both the two-cascaded LPF-based and the proposed MAF-based MPC strategies can achieve bi-directional power flow for EV batteries. Compared with the system results obtained from the two-cascaded LPF based MPC algorithm, the proposed control method can improve the system performance by reducing the current total harmonic distortion under a balance/unbalance grid voltage. The reactive power performance can be improved when the active power reference varies.

Experimental analysis and modeling of temperature dependence of lithium-ion battery direct current resistance for power capability prediction

Accurate lithium-ion battery power capability prediction gives an indication for managing power flows in or out of batteries within the safe operating area, which is one of the primary challenging functions of battery management systems (BMSs). The battery direct current resistance (DCR) is typically employed for power capability prediction, but its characteristic depends significantly on the ambient temperature. It is essential to investigate systematically the temperature dependence of battery DCR for achieving reliable power capability prediction. Based on a large amount of battery test data, a battery DCR model is proposed for quantitatively describing its temperature dependence. This model is then applied for battery power capability prediction, and the results are verified by experimental results.