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For the electric vehicle motor controller driver section requirements of the multiple isolated power supply problems, analyzed the common DC/DC converter, the single-ended flyback converter was selected as the motor controller auxiliary power supply. Taking into account the parasitic parameters of the high frequency transformer and switch transistor effects on the circuit, the loss model was showed. Its feasibility is verified by experiments. Experimental results showed that the designed RCD clamp circuit switch could effectively reduce stress and meet the requirements of the motor controller.

As the electric vehicles usually run under complex conditions of city road, there always exists parameter perturbation of motor, which will affect the vehicle performance. To solve the problem, an improved sliding mode variable structure control (SMC) method which combines continuous function and reaching law has been proposed in this paper. Then the improved vector control algorithm’s codes are generated automatically in the MMC (Model-based Motor Control) platform. The results show that the improved SMC algorithm has small chattering and strong robustness to the parameter perturbation and the external disturbances.

Accurately identifying road condition can send relevant information to the motor control system, so that control system of the motor can adjust the control strategy timely, eventually, the intelligent and optimal control of electric vehicles is realized. In this paper, according to these mathematical model, the permanent magnet synchronous motors simulation model and vehicles simulation model are proposed. Then, output torque of motor and speed of motor are served as the input of RBF neural network, which helps road condition to be identified. The simulation result shows that the road condition is well identified by proposed method based on RBF neural network.

Regarding to the electric vehicle (EV) with dual-energy storage system (DESS) composed of batteries and ultra-capacitors, study on the structure and drive modes of DESS, after a detailed analysis of energy storage system based on power, resistance and constraints in driving, establish a mathematical model of energy management system of EV with DESS, and an energy management based on the fuzzy control strategy is designed. Finally, a simulation of EV with DESS by using ADVISOR simulation platform is studied, whose results show that the EV with DESS based on fuzzy control strategy can be more effective in distributing power between energy storage systems, and the dynamic performance as well as economic efficiency are also improved

In this paper, the test bench for electric vehicle drive system is designed and the real-time display of speed, torque, current, voltage and other conventional parameters are realized that can offer lots of authentic data for motor performance evaluation, motor parametric design and optimizing control strategy. Road simulation is an important function in the test bench, so the paper analyses the driving electric vehicle in the longitudinal and gives the theoretical dynamometer dynamic output load values that can provide the theory reference for the further implementation of road conditions simulation based on LabVIEW. Finally, in order to validate the test bench feasibility, the start, accelerated and deceleration tests are implemented and show good performance of the bench.

One method which is based on model to settle the long development cycle of driver motor control software is adopted for Electric Vehicle (EV). It is to achieve Field Oriented Control algorithm (FOC) designing, model building, and microcontroller driver integration, task scheduling, code generation; Model simulation experiments are completed aiming at three kinds of running conditions such as a constant speed, accelerating, on load; finally, these running conditions are realized in EV and dynamometer platform. Simulation and experimental results show FOC has a well performance on speed regulating by comparing technical parameters with Chang An Ben Ben Mini, adopting the method also shorten the development cycle of motor control software.

A battery management system (BMS) based on the LIN-Bus was designed for the Valve Regulated Lead Acid Battery (VRLA). The VRLA BMS plays an important role as a vehicle power supply system which is part of the entire vehicle system. In order to improve the stability of various sub-systems inside the vehicle, VRLA parameters estimation, including current, voltage and temperature, are very important. However, due to lead-acid battery charging and discharging are a complex process of the electrochemical process. The VRLA BMS is consisted of a VRLA battery, a NI USB-8476 LIN transceiver manufactured by National Instruments(NI), a battery test system(BTS) and a high stability, high reliability ADuC7033 for which is high precision and rapid data acquisition ARM7 chip. The hardware design of sampling circuit, the whole VRLA BMS system structure, the software design of keil ARM7 and LabVIEW8.5 and LIN Bus communication were introduced for realtime on-line monitoring and controlling. Experimental results show that measurement is high credible and the error is within 1%. The BMS that was proposed by this paper can be used in Electric Vehicle(EV).

To track the state of charge (SOC) of Ni-MH battery pack at the hybrid electric vehicle, an artificial neural network (ANN) is designed. Current, voltage and the previous SOC are used to inputs of ANN, and output is SOC. The result show that, this artificial neural network can track the state of charge(SOC) of the batteries accurately, in the average tracking error less than 5%; the ANN is in low dependence on the initial SOC, and the output can be achieved target value only in 90 seconds.