Research On Management System And State Of Charge Estimation Of Mine Lithium Power Supply

Mine hoist safety is an important part of production safety in coal industry.It is particularly important to provide stable and reliable power supply for the mine hoist load monitoring equipment.The lithium battery has become the choice of backup power supply for mine because of its excellent working and cycling characteristics.The research of battery management system(BMS)used to manage and monitor the characteristics and working status of lithium batteries is of high practical value in engineering.In this paper,the backup power supply of the hoist load monitoring equipment is analyzed.The main research contents are as follows:Firstly,according to the application background of the lithium power supply for mine,the working principle of lithium power supply is analyzed and the external characteristic parameters of lithium power supply are determined.At the same time,the working characteristics of lithium power supply are analyzed and the relevant working parameters are determined.The corresponding flameproof and intrinsically safe design is carried out for the special mine environment.Secondly,according to the design requirements of mine battery management system(BMS),the overall scheme of mine lithium power BMS is proposed,and the hardware design is carried out for different module units.In order to ensure the real-time performance of BMS,transplanting the system of ?C/OSII on STM32 schedules the tasks of BMS in the form of modules.Subsequently,the upper part of the lithium battery management system and the BMS test platform are described.Then,in order to accurately estimate the SOC of lithium batteries,a battery model that can represent the external characteristics of lithium batteries is established.The equivalent circuit model with second-order RC loop is selected by comparing several commonly used battery models building methods for the calculation amount and model accuracy.Then parameters identification methods of the model are studied.According to the specific characteristics of different parameters in the model,the parameters identification of the circuit model is carried out.The validity of the model is validated by different data obtained from discharge experiments at different discharge rates.Then the factors that may affect the SOC estimation results in the battery operation are experimented,and the influencing factors and the revised calculation formula are analyzed and determined.Finally,the SOC estimation methods are studied according to the obtained model.Particle filter with good applicability for non-linear systems is selected to estimate.Improvement strategies are proposed for particle degradation problems of particle filter and battery state equation,respectively,to optimize standard particle filter.The lithium battery experiment was carried out on the platform of lithium battery laboratory of Jiangsu Furuishi new energy company.According to two groups of different discharge experiments,the comparison and analysis of standard particle filter,extended Kalman algorithm and optimized particle filter were carried out.The experimental results have shown that the optimized particle filter algorithm is superior to standard particle filter algorithm and extended Kalman algorithm.Particle filter based on particle swarm optimization is superior in the two optimization algorithms,and it can estimate the state of charge of battery more accurately.