Abstract
With the development of new energy vehicle technology, battery management systems used to monitor the state of the battery have been widely researched. The accuracy of the battery status assessment to a great extent depends on the accuracy of the battery model parameters. This paper proposes an improved method for parameter identification and state-of-charge (SOC) estimation for lithium-ion batteries. Using a two-order equivalent circuit model, the battery model is divided into two parts based on fast dynamics and slow dynamics. The recursive least squares method is used to identify parameters of the battery, and then the SOC and the open-circuit voltage of the model is estimated with the extended Kalman filter. The two-module voltages are calculated using estimated open circuit voltage and initial parameters, and model parameters are constantly updated during iteration. The proposed method can be used to estimate the parameters and the SOC in real time, which does not need to know the state of SOC and the value of open circuit voltage in advance. The method is tested using data from dynamic stress tests, the root means squared error of the accuracy of the prediction model is about 0.01 V, and the average SOC estimation error is 0.0139. Results indicate that the method has higher accuracy in offline parameter identification and online state estimation than traditional recursive least squares methods.
Highlights
In order to meet people’s urgent needs for low-carbon transportation, the electric vehicle industry is developing rapidly
For the management of the batteries during electric vehicle operation, to achieve the best performance and prolong battery life, it is necessary to monitor various states inside the battery depending on the battery management system (BMS) in real-time
In order to verify the effectiveness of the improved algorithm in a complex dynamic sitIn order to verify the operating effectiveness of the test improved in aiscomplex uation, the dynamic stress tests (DST)
Summary
In order to meet people’s urgent needs for low-carbon transportation, the electric vehicle industry is developing rapidly. Many estimation algorithms have been proposed and applied on SOC, and those widely used methods are open circuit voltage (OCV) methods [6,7,8], Coulomb counting methods [9], model-based methods [10,11], and data-driven methods [12,13,14]. In order to obtain a battery model that can more accurately describe the actual electrochemical process, the recognition strategies need to be adopted on different time scales. Where z represents SOC, k i 1,2,3,4,5,6,7 represents the coefficient of fitting battery model at different scales and introduces the parameter identification process and the relationship between U and z.
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