Research On The Measuring Residual Capacity Of Nickel Metal Hydride Battery For Electric Vehicles

At present, there were increasingconcerns about environmental and energy problems, electric vehicles have been developed for it's benefit of zero emission and low noise. Many countries make electric vehicles as the development direction of motor industry. The key technology of electric vehicles is the energy management system. A good energy management system is based on knowing the exact state-of-charge(SOC). In this respect, estimating the state-of-charge is an essential element to the possible commercialization and popularization of electric vehicles.Nickel metal hydride(MH-Ni battery) becomes the first elect of the storage battery in motor industry, because of the excellent synthetic performance of MH-Ni battery, such as high specific energy, long circle life, adapting to big currents discharge and no pollution. Many countries use MH-Ni battery as energy source for electric vehicles. This paper does research on MH-Ni battery. First, describes the electrochemistry characteristic of MH-Ni battery, point out the factors of affecting battery residual capacity(BRC) and the difficulties of estimating state-of-charge. Then, the most common techniques for estimating the SOC of electric vehicle's battery in the world are introduced. And the disadvantages of these methods are described, in addition, our some research work in this field is introduced. Based on these, this paper puts forward a new method of measuring residual capacity of the electric vehicles traction battery. Namely using ampere-hour, open-voltage and the adaptive neural fuzzy inference system (ANFIS) method, correct the actual capacity to get the exact result. An adaptive neuro fuzzy inference system modeling approach is employed for the estimation of BRC in terms of the state of available capacity of the MH-Ni battery powered electric vehicles. The inputs of the model will be the battery temperature and the discharged capacity distribution, which can describe the discharge current profile, the output of the model will be the SOC. This ANFIS model combines the neural network adaptive capabilities and the fuzzy qualitative approach such that the SOC estimation can be performed in an effective way.This paper describes the design of system's software and hardware. It can control MH-Ni battery to charge and discharge, and it can on-line control. It can prevent the battery over-charge and over-discharge in effect. In the course of charge and discharge, the collected parameters of battery can be written in the background database, thus the battery capability and SOC can be analyzed and researched farther after charge and discharge. Using the method of mixed programming between Delphi and Matlab estimate the SOC. The program of Delphi can call the dynamic link library(DLL) which is programmed beforehand. It can on-line estimate and correct SOC in time. It can advance the precision of the estimation of BRC and the capability of the energy management system, then satisfies the practical need of electric vehicles.With the initial experiment, this method has the better precision of the estimation of BRC. In addition, this paper puts forward the idea to advance the system.