Joint Estimation of SOC and Available Capacity of Power Lithium-Ion Battery

Temperature has an important effect on the battery model. A dual-polarization equivalent circuit model considering temperature is established to quantify the effect of temperature, and the initial parameters of the model are identified through experiments. To solve the defect of preset noise, the H-infinity filter algorithm is used to replace the traditional extended Kalman filter algorithm, without assuming that the process noise and measurement noise obey Gaussian distribution. To eliminate the influence of battery aging on SOC estimation, and considering the different time-varying characteristics of the battery states and parameters, the dual time scale double H-infinity filter is used to jointly estimate the revised SOC and available capacity. The simulation results at two temperatures show that, compared with the single time scale, the double time scale double H-infinity filter reduces the simulation time by nearly 90% under the premise that the accuracy is almost unchanged, which proves that the proposed joint estimation algorithm has the dual advantages of high precision and high efficiency.

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Online State of Charge Estimation for Lithium-Ion Battery by Combining Incremental Autoregressive and Moving Average Modeling with Adaptive H-Infinity Filter

Mathematical Problems in Engineering ◽

10.1155/2018/7480602 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16 ◽

Cited By ~ 1

Author(s):

Zheng Liu ◽

Xuanju Dang ◽

Hanxu Sun

Keyword(s):

Moving Average ◽

Equivalent Circuit Model ◽

Lithium Ion ◽

State Of Charge ◽

Parameters Identification ◽

Estimation Methods ◽

Model Parameters ◽

Battery Management System ◽

H Infinity ◽

Soc Estimation

The state of charge (SOC) estimation is one of the most important features in battery management system (BMS) for electric vehicles (EVs). In this article, a novel equivalent-circuit model (ECM) with an extra noise sequence is proposed to reduce the adverse effect of model error. Model parameters identification method with variable forgetting factor recursive extended least squares (VFFRELS), which combines a constructed incremental autoregressive and moving average (IARMA) model with differential measurement variables, is presented to obtain the ECM parameters. The independent open circuit voltage (OCV) estimator with error compensation factors is designed to reduce the OCV error of OCV fitting model. Based on the IARMA battery model analysis and the parameters identification, an SOC estimator by adaptive H-infinity filter (AHIF) is formulated. The adaptive strategy of the AHIF improves the numerical stability and robust performance by synchronous adjusting noise covariance and restricted factor. The results of experiment and simulation have verified that the proposed approach has superior advantage of parameters identification and SOC estimation to other estimation methods.

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State of charge estimation for lithium-ion batteries connected in series using two sigma point Kalman filters

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v12i2.pp1334-1349 ◽

2022 ◽

Vol 12 (2) ◽

pp. 1334

Author(s):

Chi Nguyen Van ◽

Thuy Nguyen Vinh

Keyword(s):

Lithium Ion Battery ◽

Kalman Filters ◽

Equivalent Circuit Model ◽

Lithium Ion ◽

Estimation Algorithm ◽

State Of Charge ◽

Time Interval ◽

Sigma Point ◽

Soc Estimation ◽

In Series

This paper proposes a method to estimate state of charge (SoC) for Lithium-ion battery pack (LIB) with 𝑁 series-connected cells. The cell’s model is represented by a second-order equivalent circuit model taking into account the measurement disturbances and the current sensor bias. By using two sigma point Kalman filters (SPKF), the SoC of cells in the pack is calculated by the sum of the pack’s average SoC estimated by the first SPKF and SoC differences estimated by the second SPKF. The advantage of this method is the SoC estimation algorithm performed only two times instead of 𝑁 times in each sampling time interval, so the computational burden is reduced. The test of the proposed SoC estimation algorithm for 7 samsung ICR18650 Lithium-ion battery cells connected in series is implemented in the continuous charge and discharge scenario in one hour time. The estimated SoCs of the cells in the pack are quite accurate, the 3-sigma criterion of estimated SoC error distributions is 0.5%.

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A Novel Adaptive Multi-time Scale Joint Online Estimation Method for SOC and SOH of Lithiumion Batteries

Journal of Physics Conference Series ◽

10.1088/1742-6596/2095/1/012004 ◽

2021 ◽

Vol 2095 (1) ◽

pp. 012004

Author(s):

Weibo Chen ◽

Huijuan Ying

Keyword(s):

Real Time ◽

Time Scale ◽

Estimation Method ◽

Equivalent Circuit Model ◽

Lithium Ion ◽

Joint Estimation ◽

Least Square ◽

Estimation Accuracy ◽

Online Estimation ◽

Battery Model

Abstract The state of charge (SOC) and state of health (SOH) are essential indicators for estimating the performance of lithium-ion batteries. In most of the existing methods to estimate SOC and SOH through step-by-step calculation may bring obstacles to real-time prediction of battery performance. To adapt the complex and dynamic situation of the batteries and estimate SOC and SOH in an accurate and fast manner, a novel multi-time scale joint online estimation method is proposed. In order to quickly identify the battery model and estimate the battery state, SOC and SOH are evaluated on a multi time scale framework based on extended Kalman filter (EKF). To improve the accuracy of the equivalent circuit model (ECM), a variable forgetting factor recursive least square (VFFRLS) method is introduced to identify the internal parameters in the battery model. A fuzzy variable time scale EKF (FVEKF) is proposed to estimate SOC and SOH online, where the fuzzy inference engine change the time scale to increase the convergence speed especially in complex stress conditions. Database from the University of Maryland is adopted to testify the effectiveness and efficiency of the algorithm. The results demonstrate that the method has better estimation accuracy and efficiency comparing to traditional joint estimation method, and meet the requirements of real-time estimation.

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A nonlinear fractional-order H∞ observer for SOC estimation of battery pack of electric vehicles

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/0954407021994349 ◽

2021 ◽

pp. 095440702199434

Author(s):

Wei Yue ◽

Cong-zhi Liu ◽

Liang Li ◽

Xiang Chen ◽

Fahad Muhammad

Keyword(s):

Fractional Order ◽

Estimation Method ◽

Equivalent Circuit Model ◽

Lithium Ion ◽

Open Circuit ◽

Design Criterion ◽

Observer Design ◽

Nonlinear Observer ◽

Battery Pack ◽

Soc Estimation

This work is focused on designing a fractional-order [Formula: see text] observer and applying it into the state of charge (SOC) estimation for lithium-ion battery pack system. Firstly, a fractional order equivalent circuit model based on the fractional capacitor is established and identified. Secondly, the SOC estimation method based on the fractional-order [Formula: see text] observer is proposed. The nonlinear intrinsic relationship between the open-circuit voltage and SOC is described as a polynomial function, and its Lipschitz proposition has been discussed. Then, the nonlinear observer design criterion is established based on the Lyapunov method. Finally, the effectiveness of the proposed method is verified with high accuracy and robustness by the experiment results.

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Lithium-Ion Battery SOC Estimation Based on Adaptive Forgetting Factor Least Squares Online Identification and Unscented Kalman Filter

Mathematics ◽

10.3390/math9151733 ◽

2021 ◽

Vol 9 (15) ◽

pp. 1733

Author(s):

Hao Wang ◽

Yanping Zheng ◽

Yang Yu

Keyword(s):

Kalman Filter ◽

Least Squares ◽

Unscented Kalman Filter ◽

Equivalent Circuit Model ◽

Lithium Ion ◽

Forgetting Factor ◽

Soc Estimation ◽

Online Identification ◽

Adaptive Forgetting Factor ◽

Adaptive Forgetting

In order to improve the estimation accuracy of the battery state of charge (SOC) based on the equivalent circuit model, a lithium-ion battery SOC estimation method based on adaptive forgetting factor least squares and unscented Kalman filtering is proposed. The Thevenin equivalent circuit model of the battery is established. Through the simulated annealing optimization algorithm, the forgetting factor is adaptively changed in real-time according to the model demand, and the SOC estimation is realized by combining the least-squares online identification of the adaptive forgetting factor and the unscented Kalman filter. The results show that the terminal voltage error identified by the adaptive forgetting factor least-squares online identification is extremely small; that is, the model parameter identification accuracy is high, and the joint algorithm with the unscented Kalman filter can also achieve a high-precision estimation of SOC.

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Adaptive State-of-Charge Estimation for Lithium-Ion Batteries by Considering Capacity Degradation

Electronics ◽

10.3390/electronics10020122 ◽

2021 ◽

Vol 10 (2) ◽

pp. 122

Author(s):

Peipei Xu ◽

Junqiu Li ◽

Chao Sun ◽

Guodong Yang ◽

Fengchun Sun

Keyword(s):

Equivalent Circuit Model ◽

Lithium Ion ◽

Maximum Error ◽

State Of Charge ◽

Recursive Least Squares ◽

Accurate Estimation ◽

Model Parameters ◽

Soc Estimation ◽

Capacity Degradation ◽

Battery Capacity

The accurate estimation of a lithium-ion battery’s state of charge (SOC) plays an important role in the operational safety and driving mileage improvement of electrical vehicles (EVs). The Adaptive Extended Kalman filter (AEKF) estimator is commonly used to estimate SOC; however, this method relies on the precise estimation of the battery’s model parameters and capacity. Furthermore, the actual capacity and battery parameters change in real time with the aging of the batteries. Therefore, to eliminate the influence of above-mentioned factors on SOC estimation, the main contributions of this paper are as follows: (1) the equivalent circuit model (ECM) is presented, and the parameter identification of ECM is performed by using the forgetting-factor recursive-least-squares (FFRLS) method; (2) the sensitivity of battery SOC estimation to capacity degradation is analyzed to prove the importance of considering capacity degradation in SOC estimation; and (3) the capacity degradation model is proposed to perform the battery capacity prediction online. Furthermore, an online adaptive SOC estimator based on capacity degradation is proposed to improve the robustness of the AEKF algorithm. Experimental results show that the maximum error of SOC estimation is less than 1.3%.

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