Jianbo Cao

Energy-Regenerative Fuzzy Sliding Mode Controller Design for Ultracapacitor-Battery Hybrid Power of Electric Vehicle

In order to deal with two major problems of electric vehicle (EV): the short driving range and the short life of batteries, a hybrid power system was designed and applied to the EV. It was composed of an ultracapacitor with high-specific power and long life, four lead-acid batteries with high-specific energy, and a bi-directional DC/DC converter. To improve the stability and reliability of the system, based on researching energy-regenerative process and fuzzy sliding mode controller (Fuzzy-SMC), the energy-regenerative mathematical model of the system was established, and the energy-regenerative Fuzzy-SMC for the system was designed. The experimental results show that the Fuzzy-SMC is superior to PID controller at response speed, steady-state tracking error and resisting perturbation. Additionally, comparing with the EV which uses batteries as its single power source, the ultracapacitor-battery hybrid power system can recover more energy, lengthen the life of batteries, and increase the driving range by 36.8% with PID controller, and by 42.1% with Fuzzy-SMC.

The application of combinatorial optimization by Genetic Algorithm and Neural Network

A optimization model of sizing the storage section in a renewable power generation system was set up, and two methods were used to solve the model: genetic algorithm or combinatorial optimization by genetic algorithm and neural network. The system includes the photovoltaic arrays, the lead-acid battery and a flywheel. The optimal sizing can be considered as a constrained optimization problem: minimization the total capacity of energy storage system, subject to the main constraint of the loss of power supply probability (LPSP). Both of the two optimal algorithm got good results. We can see that, combinatorial optimization by genetic algorithm and neural network can lessen the calculation time, with the results change little.

Particle swarm optimization with normal cloud mutation

The particle swarm optimization algorithms converges rapidly during the initial stages of a search, but often slows considerably and can get trapped in local optima. The swarm particle with mutation can speed up convergence and escape local minima. Because normal cloud model has the properties of randomness and stable tendency, this paper proposed a particle swarm optimization with normal cloud mutation (NCM-PSO). This method is tested and compared with the constriction particle swarm optimization (CPSO) with Gaussian mutation (GM-PSO), the CPSO with Cauchy mutation (CM-PSO), and CPSO without mutation. The results show that the proposed method is superior to the others previously mentioned.

Neural network control of electric vehicle based on position-sensorless brushless DC motor

Based on analyzing the principle of position- sensorless control for brushless DC motor (BLDCM), a control system employing back-EMF method was designed for the position-sensorless electric vehicle (EV). In order to eliminate the influence on back-EMF detection circuit from motor neutral point and RC filter, the system disconnected the reference point of detection circuit from battery cathode, and did the phase- shifting compensation of back-EMF. To improve the stability and reliability of the system, neural network PID (NNPID) control algorithm was researched and applied to the system. The controller comprises a back propagation (BP) NN and a radial basis function (RBF) NN. The former is used to adaptively adjust the parameters of the PED controller on-line. The later is used to establish nonlinear prediction model and perform parameter prediction. The experimental results show that the control system of position-sensorless EV can overcome the disturbance of phase shifting, successfully achieve position-sensorless commutation control and replace Hall sensors. In addition, when using NNPID controller, the control system is superior to that using traditional PID controller at response speed, steady-state tracking error and resisting perturbation in the driving process.

Regenerative-braking sliding mode control of electric vehicle based on neural network identification

Aiming at the main problems of electric vehicle (EV): short driving range, short life of batteries, and variation of model parameters, based on constructing the main circuit diagram of the EVpsilas control system, the mathematical model of regenerative-braking process was established, and a novel regenerative-braking controller was designed, which combined neural network (NN) with traditional sliding mode controller (SMC). The controller comprises a back propagation NN (BPNN), a radial basis function NN (RBFNN) and a SMC. The BPNN is used to adaptively adjust the switching gain of the SMC on-line so as to avoid the whippings. The RBFNN is used to perform system identification and parameter prediction. The experimental results show that the NNSMC could improve the stability and reliability of the system, and is superior to traditional SMC at response speed, steady-state tracking error and resisting disturbance in the regenerative-braking process. Additionally, it can recover more energy, lengthen batteriespsila life, and increase the driving range than SMC by about 6%. This is very significant for saving energy.

Torque ripple control of position-sensorless brushless DC motor based on neural network identification

In order to reduce the torque ripple of position-sensorless brushless DC motor (BLDCM), Based on analyzing the commutation process, a novel control system employing back-EMF method was designed, which disconnected the reference point of detection circuit from battery cathode and did the phase-shifting compensation of back-EMF. Moreover, through regulating the terminal voltage of motor, the system made the rising ratio and dropping ratio of the phase currents be approximate so as to keep the amplitude of the total current in the constant. To further suppress the torque ripple, neural network (NN) control algorithm was researched and applied to the system. The controller comprises a back propagation (BP) NN and a radial basis function (RBF) NN. The former is used to adaptively adjust the parameters of the PID controller on-line. The later is used to establish nonlinear prediction model and perform parameter prediction. The experimental results show that the proposed method in this paper could ensure prominent reduction of torque ripple, have good robustness, and achieve position-sensorless commutation control of BLDCM successfully.

A novel fore axle whole-turning driving and control system for direct-wheel-driven electric vehicle

Based on comprehensive analysis of differential principle, a novel driving and control system for a direct-wheel-driven electric vehicle (EV) is presented in this paper. Without differential gears, direct-wheel-driven EV has to employ complicated differential algorithm for steering, which remarkably increased the difficulty of the control system of EV. The system proposed in this paper has changed the traditional structure and control method of direct-wheel-driven EV to simplify the control of EV. The propounded system connected two permanent-magnet brushless dc motors (PMBDCMs) directly in series, simplified the differential algorithm thoroughly in velocity control strategy, and adopted the traditional PI controller. By analyzing the dynamics of Four-wheel independent drive electric vehicle and combining the characteristic of the PMBDCM, the operation principle of the propounded system is explicated. Based on the analysis, an equipollent simulation system was developed under Matlab/Simulink platform, and validated the rationality and feasibility of the driving and control system for direct-wheel-driven EV. The particular performance of the propounded system made direct-wheel-driven EV more practical and competitive.

Torque coordinated control of independent driving electric vehicles base on BP neural network

Based on the analysis the driving system of motors in series, it indicated that the system simplifies the structure and realizes the effective self-action differential when the electric vehicle(EV) runs on the level road without any variety or at low velocity. When the EV runs on the variational road, especially the friction force is variant, the EV is unstable according to the character of the permanent-magnet brushless DC motors (PMBDCMs). In this paper, the structure was improved and a BP neural network PID control strategy was proposed to control the coordinated driving torque. In order to enhance the rapidity of convergence and avoid trapping in local optimization, the control system improved BP algorithm and momentum factor is adapted to effectively solve the problem. A model was built for the 2-wheel independent driving EV and when the friction force of the two power wheel is different, the simulations .. were performed and the positive results were obtained by the proposed concept. The propose system improve the EVpsilas maneuverability and stabilization.

Estimation the residual capacity of Ni-MH battery pack using NARMAX method for electric vehicles

This paper presents a nonlinear autoregressive moving average with exogenous variables (NARMAX) method to estimate the residual capacity of high-capacity Ni/MH battery pack in electric vehicles. The state of charge (SOC) represents the battery residual capacity. The SOC of battery cannot be measured directly and estimated from measurable battery parameters such as current and voltage. The proposed NARMAX produces accurate SOC estimate, using industry standard Federal Urban Driving Schedule (FUDS) aggressive driving cycle test procedures. The results indicate that the NARMAX can provide an accurate and effective estimation of the SOC, resulting in minimal computation load and suitable for real-time embedded system application. The maximum average relative error of the estimating results is 0.02%.