The Estimation Of State Of Charge Research And Battery Management System Design For Lithium Battery

With the growing challenge of energy crisis and environmental problems, lithiumion batteries due to advantages of no pollution and high energy density, are widelyused in electronics, electric vehicles and energy storage power supply, etc. In order toimprove the safety and service life of the lithium ion battery, we need to effectivelycontrol and manage the battery packs. State of charge (SOC) is one of the mostimportant parameters in the battery management system (BMS), which is thefoundation of associated functions in the system. Therefore, real-time and accurateestimation of the battery SOC has an important practical significance.Accurate SOC estimation needs a precise battery model. This paper firstlyestablished a model for the lithium ion battery. According to the externalcharacteristic of battery performance, this paper established the dynamic circuit modelon the basis of the traditional circuit model. During the modeling process, this paperintroduced an electrochemical KiBaM model to simuliate the battery capacity ratioeffect and recovery effect. Besides, in order to get a more accurate description of theopen circuit voltage of the battery, this paper considered the hysteresis voltage in theprocess of charging and discharging. After completing the feature modeling of thebattery, the model parameters are identified based on the data of the operatingcondition test. Finally a comparative analysis revealed that the the simulation voltageof the dynamic model can track the actual measured value, which can well simulatethe dynamic and static characteristics of the battery.On the basis of the dynamic battery model, this paper determined the battery SOCestimation based on the extended kalman filter algorithm flow. Aimed at thedisadvantages of the extended kalman filter algorithm, a finite difference extendedkalman filter algorithm is proposed for SOC estimation in this paper, which uses thefinite difference method instead of partial derivative calculation for nonlinearfunctions, and the improved algorithm has a strong robustness. According to thebattery SOC estimation results contrast under several different operating conditiontests, it can be proved that the finite difference extended kalman filter is better thanthe extended kalman filter, which has higher precision and can maintain smaller erroras model error exits. At the end of the paper, we introduces the battery management system of thephotovoltaic energy storage system, the system uses the distributed designs, includingthe control system and the supervising and measuring system of the battery pack,where the control system generates the code of estimation algorithm of SOCautomatically by using Matlab RTW (Real-Time-Workshop) and realize the onlineestimation of SOC on the hardware and the supervising and measuring system isresponsible for the implementation of voltage acquisition and transmittion of thebattery in real time and display the voltage on the LCD screen.