The Research Of Battery Management System

With the rapid development of electric vehicles, the demand on battery energy is higher and higher. As one important part in the power battery, battery management system (BMS) directly detects and manages the whole operation process of electric vehicles power batteries, including charge and discharge process, SOC estimation, battery temperature, the balance between monomer batteries and battery failure diagnosis. Therefore, it is very necessary to research particular battery management system for managing and maintaining battery energy.With the lead-acid battery as the research object, this thesis points out the main factors of influencing the batteries state of charge (SOC) beginning with analyzing the performance characteristics of lead-acid battery. Firstly, this thesis introduces several kinds of SOC measuring methods and analyzes their advantages and disadvantages. Based on this, this thesis proposes one kind of SOC measuring method based on artificial neural network. In the process of artificial neuron nets, one kind of fast learning algorithm, LM algorithm, is adopted in the learning process of network. The accuracy of calculating can be improved by using the parallel computing and self-learning of neural network. In the modeling process, this method does not refer to the complex physical and chemical reaction inside batteries. This method has high forecast precision through preliminary simulation analysis.This thesis makes explanations about the design and realization of the software and hardware of the battery management system in detail. This hardware platform based on the management system of DSP 2407 measures the working conditions of batteries such as voltage, current and temperature. Thus, the data of algorithmic analysis and balance management is obtained. Then, these data is sent to the central control system through CAN communication and displayed on the LCD. Finally, the hardware platform is used to verify the system function.