The Research On Battery Capacity Forecasting System Of Electric Vehicle

As the increasing awareness of global environment protection and outstanding energy issues, the development of vehicle is also facing severe challenges——energy consumption,environmental pollution and other issues. For a long time, the vehicle's traditional development is almost based on oil consumption, but this model is becoming changed, as the green vehicles, electric vehicles will gradually become an ideal model of the green vehicles development. At present, the development of electric vehicles still has a lot of technical bottlenecks, the battery energy management is a key factor in the development of electric vehicles, The performance of battery and the energy management plays an important role on the whole performance of vehicle, and directly determines the electric vehicle's characteristics such as the continuous mileage,acceleration and maximum grad ability. Therefore, the development of battery energy management system and the research of battery energy management method have a great significance for the energy saving and environmental protection.With the difficult problems in battery management system, further studied of the battery remaining capacity estimate had been done in this paper. Firstly, the article introduces the electric vehicle's development status, the battery management system technology status and the traditional estimate methods of battery remaining capacity. Three electric vehicle batteries are compared and lithium-ion battery is researched. In this paper,the battery working principle is analyzed, by establishing the battery model, the voltage, current, temperature, internal resistance and other major characteristics of battery are analyzed and simulated, based on these, the paper applies artificial neural network theory and build-up the RBF neural network to predict the battery remaining capacity, the simulation results proved this method's feasibility and show that this method is easy to implement and has a good prediction accuracy.Finally, the battery management system's hardware and software were designed. For the hardware, detailed analysis of main functions, principles and structure of each module are given, use the core control unit STC89C52Rc to manage the battery energy; The software's flow chart and part program code are given. The top-down approach method during the software design ensures the system's speed and reliability.