Jiang Jiuchun

Harmonic-study of electric vehicle chargers

Electric vehicle (EV) chargers are highly nonlinear power system load. They present a potential problem to power systems in the forms of excessive harmonic currents and poor power factor. Thus it is important to analyze the effect of EV chargers on power system harmonic current and then to take some measures to improve the loading factor of power systems and acquire better power quality. In this paper, we have set up individual EV charger model on the base of analyze of actual data from a commercial-available EV charge and battery. By computer calculation and simulation we analyzed harmonic current of individual EV charger and also have predicted the harmonic currents produced by a group of EV chargers.

CNG engine air-fuel ratio control using fuzzy neural networks

Accurate control of the air fuel ratio in a spark-ignition engine is critical to satisfying future emissions regulators. The goal of this research is to explore the use of fuzzy neural networks as a means of precisely controlling the air fuel ratio of a lean-burn compressed natural gas (CNG) engine. A control, without based on engine model, has been utilized to construct a feedforward/feedback control scheme to regulate air fuel ratio. Using fuzzy neural networks, a fuzzy neural hybrid controller is obtained based on PI controller. The new controller, which can adjust self parameters online, has been tested in transient air fuel ratio control of engine.

Effects analysis of model parameters uncertainties on battery SOC estimation using H-infinity observer

The accurate estimation of the state-of-charge (SOC) of battery is the basic premise for the effective energy management and the important guarantee for the safe and efficient operation in electric vehicles. To improve SOC estimation accuracy and robustness, the paper analyzes the effects of different initial SOC errors and parameters variation on SOC estimation accuracy and robustness with H-infinity observer. A model in Matlab/Simulink is established to make calculation process come true, which is based on a new battery with nominal capacity of 90Ah. The simulation and experiment al results indicate that the H-infinity observer based SOC estimation can converge to the true values quickly even if at the set maximum initial error and its steady error can be controlled within 2%. The effects of model parameters change resulted from battery degradation including SOC-OCV relationship, capacity and internal resistance on H-infinity observer are investigated. It is concluded that SOC estimation accuracy with H-infinity observer largely depends on the accurate of the curve of SOC-OCV (Open circuit voltage, OCV), which provide a foundation for battery management.

Research on the charge mode of series-connected batteries

Traditional charge mode based on terminal voltage of batteries results in serious overcharge since status of battery cell is not known, it seriously shortens batteriespsila cycle life, even results in hidden trouble of safety. A new type of charge mode combining BMS (Battery Management Systems) and charger is presented in this paper. By data sharing between BMS and charger, the charger can known very well information about each batterypsilas voltage, temperature and failures, according to this, charger can adjust charging current. Therefore, it ensures no overcharge of any battery occurs and reduces influences that charging have on batteries, and increases safety of charging process. In this paper, principles of model parameter identification under the charge mode and an even more scientific consistency evaluation based on capacity and internal resistance are also elaborated.

Study on optimum design procedure of charger based on full-bridge phase-shifted ZVZCS converter

An optimum design procedure of an Electric Vehicle charger based on a full-bridge phase-shifted zero-voltage and zero-current switching (FB-PS-ZVZCS) dc-dc converter is proposed. The converter uses a small capacitor and two small diodes to form the clamp circuit, which makes the converter simple and easy to control. According to the analysis of the operation principles, several conclusions about the influences of the converterpsilas main parameters to the operating characteristics and the restrictions between parameters are drawn. Based on the conclusions, a parameter optimum design procedure is proposed. The proposed procedure can be used to design the values of the switches snubber capacitor, the clamp capacitor, the transformerpsilas turns ratio and the maximum duty cycle, etc. By using the proposed procedure, a converter prototype is designed. The prototype is simulated in Matlab/Simulink and verified by experiments. The simulation and experiment results validated the parameters as well as the proposed optimum design procedure.

A Study on Battery Management System of Ni-MU Bat ery Packs for Hybrid Electric Vehicle Applications

This paper proposes a decentralized battery management system (BMS) of nickel metal hydride (Ni-MH) battery packs for hybrid electric vehicle (HEV) applications, which is necessary and important to guarantee the Ni-MH battery packs performance in the HEV application. The presented BMS, which consists of a central management unit and data acquisition modules, features the characteristics of reduced wiring, great ability of expansion, easy installation, and high precision and reliability. The estimation algorithm of the state of charge (SOC) of the battery pack combines the open circuit voltage method and the coulometer method in order to obtain high precision.

Capacity estimation of large-scale retired li-ion batteries for second use based on support vector machine

Capacities of li-ion batteries are difficult to estimate quickly and accurately when second use batteries are in large scale with dispersed parameters. This may result in costing too much time and money to reuse batteries. By analyzing the capacity and resistance characteristics, there is no functional relationship between them. In order to solve this nonlinear problem, a SVM model with 3 parameters (i.e. penalty coefficient, kernel function parameter and loss function parameter) is established. The inputs are five calculated values of resistance and the output is the capacity value of batteries. Data of 70 battery modules are adopted to train the model while data of other 30 battery modules are used to test the model. In this process, two parameters optimization methods using genetic algorithm have been proposed. By comparison, the coefficient of determination (COD) value of method 2 is higher than method 1 both in training model and testing model. The average error of method 2 between measured values and estimated values is 0.67% whereas that of method 1 is 1.35%. In method 2, 90% of the estimation errors are under 2.5%. The results provide valuable insights for large-scale retired li-ion batteries into second use.

SOH estimation for lithium-ion batteries: A cointegration and error correction approach

In this paper, the degradation of battery SOH is modeled using error correction approach. The duration of charging in constant current mode and constant voltage mode along with the impedance are used to account for the observed degradation trend by proving that there exists a cointegration relationship, which can ensure a stable long-run equilibrium relationship between them, and then use this relationship to prediction the future SOH. The experiment approves that the error correction model has better performance compared to traditional autoregressive integrated moving average model.

Unified calculation of eigen-solutions in power systems based on matrix perturbation theory

Calculation of eigen-solutions plays an important role in the small signal stability analysis of power systems. In this paper, a novel approach based on matrix perturbation theory is proposed for the calculation of eigen-solutions in a perturbed system. Rigorous theoretical analysis is conducted on the solution of distinct, multiple, and close eigen-solutions, respectively, under perturbations of parameters. The computational flowchart of the unified solution of eigen-solutions is then proposed, aimed toward obtaining eigen-solutions of a perturbed system directly with algebraic formulas without solving an eigenvalue problem repeatedly. Finally, the effectiveness of the matrix perturbation based approach for eigen-solutions’ calculation in power systems is verified by numerical examples on a two-area four-machine system.

Research on a fuzzy neural hybrid controller

By integrating fuzzy neural network and PI controller, a fuzzy neural hybrid controller based on PI controller is proposed. The parameters of new controller can be adjusted on line based on the ability of fuzzy neural network. The effectiveness of the proposed controller can be demonstrated by simulation in nonlinear and time variable plant.