Parameter Identification and State-of-Charge Estimation for Lithium-Ion Batteries Using Separated Time Scales and Extended Kalman Filter

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.

Implementation of Battery Digital Twin: Approach, Functionalities and Benefits

“…The objective or fitness function for parameter identification via GA, PSO, DE, and KF algorithms is defined by Equation (1) [49][50][51][52]:…”

A Hybrid Method for SOC Estimation of Power Battery

“…After identifying the system coefficients α 0 , α 1 , α 2 , β 0 , β 1 , the parameters R 0 , R 1 , R 2 , τ 1 , τ 2 are solved by (10). en, C 1 and C 2 can be solved by τ 1 and τ 2 .…”

“…e AHI method can be realized simply, but it needs to estimate the initial value accurately, and the estimation result has accumulated error [9]. e open circuit voltage of power battery has a clear monotonous relationship with SOC, so the open circuit voltage method can be used to estimate SOC, but the battery needs a long time to stand, so it cannot be used in the realtime system [10]. e neural network method can continuously improve the estimation accuracy through learning, but it needs huge sample data and large amount of calculation [11].…”

A strong tracking adaptive fading‐extended Kalman filter for the state of charge estimation of lithium‐ion batteries

“…7,8 The SOC indication in battery-powered vehicles works similarly to the fuel gauges in internal combustion engine-powered vehicles. 9 Even though the SOC is essential, it cannot be directly measured due to challenges like internal and external working conditions, cell size differences, aging characteristics, etc., of the lithium-ion battery. 10,11 Currently, the frequently used methods for SOC estimation of lithium-ion batteries are classified into direct measurement-based such as the Ampere-hour (Ah) integral method 12 and open-circuit voltage method, 13 data-driven methods such as gated recurrent unit and long short-term memory neural networks, 14,15 and model-based methods such as Kalman filtering (KF) methods.…”