Battery SOC Estimation And System Implementation Of Hybrid Electric Vehicle

Hybrid Electric Vehicles(HEV) take electricity and fuel as double power output. Under the current situation, non-renewable energy consumption is increasing and environmental problems become serious. Developing HEV can both maintain dynamic performance and reduce environment pollution to some extent. Battery Management System(BMS) is taken as an important part of HEV, it is responsible for collecting external characteristics of the battery, estimating the State Of Charge(SOC) and communicating with control unit in real time. It helps extending battery life, indicating the mileages remains and ensuring safety. Thus ,studying on BMS is of great value.In this dissertation, the charge and discharge characteristics and working principle of MH-Ni battery are analyzed. For the core issue of BMS——SOC, genetic neural network model is established and the simulation is analyzed after summarizing the former estimation methods. The BMS is designed with XC164CS as a core. The hardware design is introduced, which includes the detection circuit of voltage, current and temperature, CAN communication circuit and A/D converter circuit etc. Software design follows the concept of modular design, the main program flow and subsystem flow are given respectively. The putting up of test bench is briefly described, the composition of equipment and the experiment plan are explained. Finally, the experimental study of BMS is taken. The BMS can accurately collect battery information, estimate SOC and communicate with upper level controller. The experimental results obtained and debugging experience laid a foundation for further research in the future.