New Energy Vehicle Power Lithium Battery Model Establishment Method and SOC Estimation Research

Abstract

As the main source of power for pure electric vehicles, new energy vehicle power lithium batteries are also a key technology that restricts the development of pure electric vehicles, and can directly affect the driving performance of vehicles. A reliable and efficient management system (Battery Management System, BMS) can allow the lithium-ion battery to output its best performance stably, while ensuring that the battery has a long enough service life. State of charge (SOC) estimation plays an important role in Li-ion battery management systems. The establishment of the battery working model is an important part of the state of charge estimation. Therefore, higher requirements are put forward for the establishment of the battery model. At the same time, the error of the subsequent data processing and estimation results is smaller, and the Kalman filter is often used for processing. This paper starts from the current situation of model research and improvement, focuses on the equivalent circuit model, analyzes the advantages and disadvantages of each model, and summarizes the comparative research on the analysis and processing effect of Kalman filter. And further experiment verification of SOC estimation through battery test experiments and algorithms, use Thevenin model, use least squares method for parameter identification, and finally use Kalman filter for SOC estimation, according to the analysis of the results, the fitting accuracy is 1.69%, provided by battery research a strong theoretical foundation.