| Following continue popularizing of mobile phone, digital camera, PDA, etc and enormous application potential of Li-ion battery, the demand to Li-ion battery detecting equipment is especially strict. Real-time cycle life testing becomes prohibitively expensive if one considers the number of different variables that can change such as depth of discharge (DOD), charge and discharge rates and cycling temperature. Thus, it becomes very critical that develop high-accuracy, multi-channel detecting equipment and intelligence method. This paper, according to the features of Li-Ion battery, presents the design of a new kind of Li-Ion battery integrated testing system in library. It can achieve auto-testing and auto-sorting for Li-Ion battery.This paper introduces the total design scheme according to the design request of system. The system adopts a principal-subordinate structure. Computer as supervisory system accomplish the real-time control and data collect to detecting system. Detecting system is used for independent controlling the timely process of Li-Ion battery formation and testing, returning energy and uploading data, etc. At the same time supervisory system has made a few discussions to intellectual detecting equipment. The experimental results of the system are given in the end of paper. Those results indicate that the system has advantages such as good dependability,high precision,and simple operation, etc. It is appropriate for the demand of the testing of Li-Ion battery in library.This paper proposed a new method for rapid prediction of discharge capacity of lithium ion batteries through partly discharge. Artificial neural network was applied to the prediction of the capacity of lithium ion battery and the establishment of model, by analyzing the relation between the voltage and internal resistance and the capacity of lithium ion battery. Testing results indicated that this method consisted with the requirements of battery classification. |
PDF Full Text Request