Research Of Adaptive Online Power Lithium-ion Battery Management System

With the rapid development of science and technology in today's society,the problem of environmental pollution and energy shortage has become more and more serious.In order to cope with the problem,people are focusing on various new energy vehicles,of which power battery and battery management system(BMS)is an extremely important partThe thesis mainly focuses on the research of adaptive online power lithium-ion battery management system.To solve the problem that the traditional battery management system is relatively closed,a more open system is proposed,including system design,hardware construction,software management,online working process visualization and algorithm simulationFirstly,considering various characteristics of the battery parameters,the estimation method of state of charge(SOC)is mainly discussed.According to the different environmental conditions during charging and discharging,the corresponding battery state estimation strategy is given During charging process,ampere-time integration method based on the open circuit voltage is applied,whose static performance is more stable.When discharging,the adaptive state of the battery is adopted to estimate the SOC of the battery to improve the accuracy of the estimateSecondly,the overall framework of the system and the BMS hardware platform have been basically constructed,mainly including the design of the acquisition circuit of the battery parameters,the construction of the voltage equalization circuit between the cells and the thermal management of the system and the realization of the Bluetooth serial communication circuit for data upload.Additionally,a BMS real-time online app is realized based on the Android platform to design and offer a visual interface for each functional module of the BMS and to realize the online monitoring and real-time alarm function of the system combined with the real-time data platform The construction of the mobile online visual platform has certain expansion significance for the traditional BMS,and provides a feasible development direction for the future BMS materialization.Finally,Matlab/Simulink is used to simulate the whole system,focusing on the SOC estimation algorithm,SOC-based equalization control,and anomaly detection.The effectiveness of the adaptive Kalman filter algorithm for battery SOC estimation is verified.