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

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%.

scholarly journals An integrated online adaptive state of charge estimation approach of high-power lithium-ion battery packs

2017 ◽  
Vol 40 (6) ◽  
pp. 1892-1910 ◽  
Author(s):  
Shunli Wang ◽  
Carlos Fernandez ◽  
Liping Shang ◽  
Zhanfeng Li ◽  
Huifang Yuan
Keyword(s):  
Lithium Ion Battery ◽  
Real Time ◽  
High Power ◽  
Computational Cost ◽  
Estimation Method ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
State Of Charge ◽  
The Real ◽  
Soc Estimation

A novel online adaptive state of charge (SOC) estimation method is proposed, aiming to characterize the capacity state of all the connected cells in lithium-ion battery (LIB) packs. This method is realized using the extended Kalman filter (EKF) combined with Ampere-hour (Ah) integration and open circuit voltage (OCV) methods, in which the time-scale implementation is designed to reduce the computational cost and accommodate uncertain or time-varying parameters. The working principle of power LIBs and their basic characteristics are analysed by using the combined equivalent circuit model (ECM), which takes the discharging current rates and temperature as the core impacts, to realize the estimation. The original estimation value is initialized by using the Ah integral method, and then corrected by measuring the cell voltage to obtain the optimal estimation effect. Experiments under dynamic current conditions are performed to verify the accuracy and the real-time performance of this proposed method, the analysed result of which indicates that its good performance is in line with the estimation accuracy and real-time requirement of high-power LIB packs. The proposed multi-model SOC estimation method may be used in the real-time monitoring of the high-power LIB pack dynamic applications for working state measurement and control.

Application of Kalman Filter in SOC Estimation of Power Lithium-Ion Battery

2012 ◽  
Vol 605-607 ◽  
pp. 1939-1943
Author(s):  
Chen Zhao ◽  
Xi Kun Chen
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Estimation Algorithm ◽  
State Of Charge ◽  
Voltage Response ◽  
Integrated Method ◽  
Soc Estimation ◽  
Terminal Voltage ◽  
State Of Charge Estimation

This paper analyses the application of Kalman Filter (KF) in Power Lithium-ion Battery SOC (State of Charge) estimation algorithm. After the analysis of two popular SOC estimate algorithm based on KF, an improved KF-SOC algorithm is proposed. The main advance of this improved algorithm is the introduction of parameter-rectification. The parameter-rectification which based on analysis of battery electrochemical principle and battery terminal voltage response curve is also achieved by KF. The main algorithm of improved KF-SOC is generated by the combination of KF and Ampere-hour integrated method. Later the simulations proved the new algorithm with high accuracy.

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

Electronics ◽  
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%.

An Active Equalizer for Serially Connected Lithium-Ion Battery Cells

2013 ◽  
Vol 732-733 ◽  
pp. 809-812 ◽  
Author(s):  
Hong Rui Liu ◽  
Chao Ying Xia
Keyword(s):  
Lithium Ion Battery ◽  
Lithium Ion ◽  
State Of Charge ◽  
Experimental Results ◽  
Battery Cell ◽  
Charging Current ◽  
In Series ◽  
Battery Cells

This paper proposes an equalizer for serially connected Lithium-ion battery cells. The battery cell with the lowest state of charge (SOC) is charged by the equalizer during the process of charging and discharging, and the balancing current is constant and controllable. Three unbalanced lithium-ion battery cells in series are selected as the experimental object by this paper. The discharging current under a certain UDDS and 20A charging current are used to complete respectively one time balancing experiment of discharging and charging to the three lithium-ion battery cells. The validity of the balancing strategy is confirmed in this paper according to the experimental results.

scholarly journals Soc Estimation of the Lithium-Ion Battery Pack using a Sigma Point Kalman Filter Based on a Cell’s Second Order Dynamic Model

2020 ◽  
Vol 10 (5) ◽  
pp. 1896 ◽  
Author(s):  
Chi Nguyen Van ◽  
Thuy Nguyen Vinh
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Battery ◽  
Dynamic Model ◽  
Dynamic Models ◽  
Lithium Ion ◽  
Second Order ◽  
Battery Pack ◽  
Sigma Point ◽  
Soc Estimation ◽  
Cell Modules

This paper deals with the state of charge (SoC) estimation of a lithium-ion battery pack (LiBP) connected by some cells in series and parallel. The voltage noise, noise and current bias of current through the LiBP are taken into account in the SoC estimation problem. In order to describe the cell dynamic more accurately, especially for practical applications with charge and discharge amplitude varying suddenly, in this paper we use the second dynamic order model of the cell to estimate the SoC of the LiBP. By applying the sigma point Kalman filter (SPKF), the average SoC of the pack and bias current of current measurement are estimated by first estimator; the second estimator estimates the SoC differences of the cell modules from average SoC of the pack. The SoC of the cell modules are the sum of average SoCs of the pack and the SoC differences. By only using two estimators, the calculation complexity for SoC estimation is more reduced; this is very useful for the LiBP, which has the number of cells connected in a large series. This method was applied for the pack of SAMSUNG ICR18650-22P connected by seven cell modules; the cell modules were connected by nine cells in parallel; the LiBP was charged and discharged with amplitude varying suddenly. The estimated SoC of seven cell modules is smaller than 2% for a temperature operating range typically −5 °C to 45 °C. The comparison of the accuracy of SoC estimation based on the first and the second order dynamic models is made; the result shows that the SoC estimation used the second order dynamic model is more exact.

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

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
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.

A Novel Method for Estimating State of Charge of Lithium Ion Battery Packs

2010 ◽  
Vol 152-153 ◽  
pp. 428-435 ◽  
Author(s):  
Yuan Liao ◽  
Ju Hua Huang ◽  
Qun Zeng
Keyword(s):  
Lithium Ion Battery ◽  
Estimation Method ◽  
Lithium Ion ◽  
State Of Charge ◽  
Estimation Methods ◽  
Counting Method ◽  
Soc Estimation ◽  
Battery Packs ◽  
Novel Method ◽  
Road Data

In this paper a novel method for estimating state of charge (SOC) of lithium ion battery packs in battery electric vehicle (BEV), based on state of health (SOH) determination is presented. SOH provides information on aging of battery packs and it declines with repeated charging and discharging cycles of battery packs, so SOC estimation depends considerably on the value of SOH. Previously used SOC estimation methods are not satisfactory as they haven’t given enough attention to the decline of SOH. Therefore a novel SOC estimation method based on SOH determination is introduced in this paper; trying to compensate the deficiency for lack of attention to SOH. Real time road data are used to compare the performance of the conventionally often used Ah counting method which doesn’t give any consideration to SOH with the performance of the proposed SOC estimation method, and better results are obtained by the proposed method in comparison with the conventional method.

scholarly journals A variable-length window-wise parameter-dependent state of charge estimation by Kalman filters

2021 ◽  
Author(s):  
Bataa Lkhagvasuren ◽  
Minkyu Kwak ◽  
Hong Sung Jin ◽  
Gyuwon Seo ◽  
Sungyool Bong ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Kalman Filters ◽  
Real Data ◽  
Estimation Algorithm ◽  
Open Circuit ◽  
State Of Charge ◽  
Least Square ◽  
State Equations ◽  
Sigma Point ◽  
Parameter Dependent

<div>This paper proposes a new window-wise state of charge (SOC) estimation algorithm based on Kalman filters (KF). In the first stage, the equivalent circuit model's parameters are estimated by a least square estimation window-wise, assuming a linear SOC and open-circuit voltage (OCV) relation. The algorithm accurately estimates the parameters and observes the changes that depend on SOC. Moreover, based on the estimated parameters, the OCV values are identified. In the next stage, window-wise linear Kalman filter(ES-LKF) without hysteresis and extended Kalman filter (ES-EKF) and sigma-point Kalman filter (ES-SPKF) algorithm with hysteresis are executed to estimate SOC. Having fewer state equations and hysteresis parameters tuned up in an off-line way, the ES-EKF and ES-SPKF perform better than the algorithms considered in previous works. The algorithms are validated by experiments with real data obtained from lab tests.</div>

scholarly journals State-of-Charge Estimation of Lithium-Ion Battery Based on Extended Kalman Filter Algorithm

2020 ◽  
pp. 420-437
Author(s):  
Wu Xiaogang ◽  
Xuefeng Li ◽  
Nikolay I. Shurov ◽  
Alexander A. Shtang ◽  
Michael V. Yaroslavtsev ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Battery ◽  
Electric Vehicle ◽  
Extended Kalman Filter ◽  
Lithium Ion ◽  
Operating Conditions ◽  
State Of Charge ◽  
Battery Management ◽  
Soc Estimation ◽  
Ternary Polymer

As the core component of electric vehicle, lithium-ion battery needs to adopt effective battery management method to prolong battery life and improve the reliability and safety. The accurate estimation of the battery SOC can be used to prevent the battery over charge and over discharge, reduce damage to the battery and improve battery performance, which plays a vital role in the battery management system. The study of battery SOC estimation mainly focused on the battery model construction and SOC estimation algorithm. Aiming at the problem that the state of charge (SOC) of electric vehicle is difficult to be accurately estimated under complex operating conditions, based on the parameter identification of the equivalent circuit of a ternary polymer lithium-ion battery, an Extended Kalman Filter (EKF) algorithm was used to estimate the SOC of the ternary polymer lithium-ion battery. Simulation and experimental results show that the estimation of SOC can be carried out by using the EKF algorithm under the conditions of China Passenger Car Condition (Chinacar) and new European driving cycle (NEDC) Compared with the coulomb counting method, the average error of SOC estimation can be realized is 1.042% and 1.138% respectively, the maximum error within 4%. Application of this algorithm to achieve SOC estimation has good robustness and convergence

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