scholarly journals Recursive State of Charge and State of Health Estimation Method for Lithium-Ion Batteries Based on Coulomb Counting and Open Circuit Voltage

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1811 ◽  
Author(s):  
Alejandro Gismero ◽  
Erik Schaltz ◽  
Daniel-Ioan Stroe
Keyword(s):  
Open Circuit Voltage ◽  
Estimation Method ◽  
Lithium Ion ◽  
Open Circuit ◽  
Operating Conditions ◽  
State Of Charge ◽  
Recursive Least Squares ◽  
State Of Health ◽  
Operation Conditions ◽  
Extensive Test

The state of charge (SOC) and state of health (SOH) are two crucial indicators needed for a proper and safe operation of the battery. Coulomb counting is one of the most adopted and straightforward methods to calculate the SOC. Although it can be implemented for all kinds of applications, its accuracy is strongly dependent on the operation conditions. In this work, the behavior of the batteries at different current and temperature conditions is analyzed in order to adjust the charge measurement according to the battery efficiency at the specific operating conditions. The open-circuit voltage (OCV) is used to reset the SOC estimation and prevent the error accumulation. Furthermore, the SOH is estimated by evaluating the accumulated charge between two different SOC using a recursive least squares (RLS) method. The SOC and SOH estimations are verified through an extensive test in which the battery is subjected to a dynamic load profile at different temperatures.

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

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1054
Author(s):  
Kuo Yang ◽  
Yugui Tang ◽  
Zhen Zhang
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Open Circuit Voltage ◽  
Lithium Ion ◽  
Open Circuit ◽  
State Of Charge ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Soc Estimation ◽  
Battery Model

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.

scholarly journals Capacity Fade in Lithium-Ion Batteries and Cyclic Aging over Various State-of-Charge Ranges

2019 ◽  
Vol 11 (23) ◽  
pp. 6697 ◽  
Author(s):  
Sophia Gantenbein ◽  
Michael Schönleber ◽  
Michael Weiss ◽  
Ellen Ivers-Tiffée
Keyword(s):  
Particle Surface ◽  
High Capacity ◽  
Lithium Ion ◽  
Open Circuit ◽  
Operating Conditions ◽  
State Of Charge ◽  
Capacity Fade ◽  
Active Electrode ◽  
Capacity Loss ◽  
Aging Mechanisms

In order to develop long-lifespan batteries, it is of utmost importance to identify the relevant aging mechanisms and their relation to operating conditions. The capacity loss in a lithium-ion battery originates from (i) a loss of active electrode material and (ii) a loss of active lithium. The focus of this work is the capacity loss caused by lithium loss, which is irreversibly bound to the solid electrolyte interface (SEI) on the graphite surface. During operation, the particle surface suffers from dilation, which causes the SEI to break and then be rebuilt, continuously. The surface dilation is expected to correspond with the well-known graphite staging mechanism. Therefore, a high-power 2.6 Ah graphite/LiNiCoAlO2 cell (Sony US18650VTC5) is cycled at different, well-defined state-of-charge (SOC) ranges, covering the different graphite stages. An open circuit voltage model is applied to quantify the loss mechanisms (i) and (ii). The results show that the lithium loss is the dominant cause of capacity fade under the applied conditions. They experimentally prove the important influence of the graphite stages on the lifetime of a battery. Cycling the cell at SOCs slightly above graphite Stage II results in a high active lithium loss and hence in a high capacity fade.

scholarly journals Accurate Real Time On-Line Estimation of State-of-Health and Remaining Useful Life of Li ion Batteries

2020 ◽  
Vol 10 (21) ◽  
pp. 7836
Author(s):  
Cher Ming Tan ◽  
Preetpal Singh ◽  
Che Chen
Keyword(s):  
Lithium Ion Batteries ◽  
Real Time ◽  
Estimation Method ◽  
Lithium Ion ◽  
Threshold Level ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
State Of Health ◽  
On Line ◽  
Useful Life

Inaccurate state-of-health (SoH) estimation of battery can lead to over-discharge as the actual depth of discharge will be deeper, or a more-than-necessary number of charges as the calculated SoC will be underestimated, depending on whether the inaccuracy in the maximum stored charge is over or under estimated. Both can lead to increased degradation of a battery. Inaccurate SoH can also lead to the continuous use of battery below 80% actual SoH that could lead to catastrophic failures. Therefore, an accurate and rapid on-line SoH estimation method for lithium ion batteries, under different operating conditions such as varying ambient temperatures and discharge rates, is important. This work develops a method for this purpose, and the method combines the electrochemistry-based electrical model and semi-empirical capacity fading model on a discharge curve of a lithium-ion battery for the estimation of its maximum stored charge capacity, and thus its state of health. The method developed produces a close form that relates SoH with the number of charge-discharge cycles as well as operating temperatures and currents, and its inverse application allows us to estimate the remaining useful life of lithium ion batteries (LiB) for a given SoH threshold level. The estimation time is less than 5 s as the combined model is a closed-form model, and hence it is suitable for real time and on-line applications.

Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion batteries

2016 ◽  
Vol 183 ◽  
pp. 513-525 ◽  
Author(s):  
Fangdan Zheng ◽  
Yinjiao Xing ◽  
Jiuchun Jiang ◽  
Bingxiang Sun ◽  
Jonghoon Kim ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Open Circuit Voltage ◽  
Lithium Ion ◽  
Open Circuit ◽  
State Of Charge ◽  
Online Estimation

scholarly journals Online SoC Estimation of Lithium-Ion Batteries Using a New Sigma Points Kalman Filter

2021 ◽  
Vol 11 (24) ◽  
pp. 11797
Author(s):  
Dongdong Ge ◽  
Zhendong Zhang ◽  
Xiangdong Kong ◽  
Zhiping Wan
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Batteries ◽  
Unscented Kalman Filter ◽  
Stress Test ◽  
Estimation Method ◽  
Lithium Ion ◽  
Operating Conditions ◽  
Recursive Least Squares ◽  
Primary Concern ◽  
Soc Estimation

The accurate state of charge (SoC) online estimation for lithium-ion batteries is a primary concern for predicting the remaining range in electric vehicles. The Sigma points Kalman Filter is an emerging SoC filtering technology. Firstly, the charge and discharge tests of the battery were carried out using the interval static method to obtain the accurate calibration of the SoC-OCV (open circuit voltage) relationship curve. Secondly, the recursive least squares method (RLS) was combined with the dynamic stress test (DST) to identify the parameters of the second-order equivalent circuit model (ECM) and establish a non-linear state-space model of the lithium-ion battery. Thirdly, based on proportional correction sampling and symmetric sampling Sigma points, an SoC estimation method combining unscented transformation and Stirling interpolation center difference was designed. Finally, a semi-physical simulation platform was built. The Federal Urban Driving Schedule and US06 Highway Driving Schedule operating conditions were used to verify the effectiveness of the proposed estimation method in the presence of initial SoC errors and compare with the EKF (extended Kalman filter), UKF (unscented Kalman filter) and CDKF (central difference Kalman filter) algorithms. The results showed that the new algorithm could ensure an SoC error within 2% under the two working conditions and quickly converge to the reference value when the initial SoC value was inaccurate, effectively improving the initial error correction ability.

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