Development Of An Efficient Energy Control System For Electric Driven Agricultural Machinery And Equipment

Since the 18 th National Congress of the Communist Party of China,China’s environmental protection efforts have been increasing,and the concept of green agriculture has been deeply rooted in the people.At the same time,the rapid development of the electric vehicle industry also provides a good reference for agricultural intelligence and electrification,and promotes the development of agricultural production toward the direction of intelligent and efficient production.As a key link in the construction of smart agriculture,the development level of electric agricultural machinery determines the modernization degree of smart agriculture to a certain extent.However,due to the complex and diverse actual operating conditions of electric agricultural machinery,electric agricultural machinery batteries have shortcomings such as high power consumption,long charging time,short driving range and so on,which seriously restricts the development of electric agricultural machinery.If the electric agricultural machinery battery information monitoring and management can be conducted in real time,the above problems will be solved to a large extent.Therefore,it is very necessary to design a set of electric driven agricultural machinery battery management system(BMS)suitable for agricultural operation environment.The main work of this paper is as follows:(1)The research significance,development status and the estimation method of battery charge state are introduced in detail.Study the principle and charge and discharge characteristics of lithium iron phosphate battery,and carry out relevant experiments to recalibrate the capacity and voltage of the battery.(2)Study the estimation of the charge state of the battery pack,establish the battery model simulation according to the working characteristics of the battery,and verify the battery model with the actual working condition data,and prove that the established model has good accuracy.The extended Kalman and trace Kalman filter algorithms in the Kalman filter class are selected to realize the state of charge(SOC)estimation based on the battery Thevenin dynamic model,and the actual measured SOC is compared with the SOC through the algorithm simulation.The results confirm that the two algorithms can accurately and quickly obtain the SOC of the battery.(3)According to the operation scenario of electric driven agricultural machinery,analyze the functional requirements of the efficient energy control system of agricultural machinery,and study and design the software and hardware structure of the system.In terms of hardware design,the distributed topology structure is adopted to conduct modular management of the system,including real-time monitoring,protection,energy management and other functional modules.In the software design,different strategies are designed according to the different functions of the modules,and the flow chart of each module is drawn,and the code is written in the software tools.(4)Using the electric agricultural machinery loaded with lithium iron phosphate battery,according to the operating conditions of the agricultural machinery,monitoring the working state of the battery in real time,and the collection function,control function and fault protection function of the battery control system are tested.The test results are: the error of single voltage,total battery pack voltage,current and temperature is within 3%;the system can provide real-time protection when failure;the error between the reported SOC value and the actual measurement value is within 5%;verify the estimation of the battery SOC value by algorithm and the reliability of battery energy control through embedded development.