Lithium-Ion Battery Parameter Identification via Extremum Seeking Considering Aging and Degradation

Battery parameters such as State of Charge (SoC) and State of Health (SoH) are key to modern applications; thus, there is interest in developing robust algorithms for estimating them. Most of the techniques explored to this end rely on a battery model. As batteries age, their behavior starts differing from the models, so it is vital to update such models in order to be able to track battery behavior after some time in application. This paper presents a method for performing online battery parameter tracking by using the Extremum Seeking (ES) algorithm. This algorithm fits voltage waveforms by tuning the internal parameters of an estimation model and comparing the voltage output with the real battery. The goal is to estimate the electrical parameters of the battery model and to be able to obtain them even as batteries age, when the model behaves different than the cell. To this end, a simple battery model capable of capturing degradation and different tests have been proposed to replicate real application scenarios, and the performance of the ES algorithm in such scenarios has been measured. The results are positive, obtaining converging estimations both with new and aged batteries, with accurate outputs for the intended purpose.

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

Energies ◽

10.3390/en14041054 ◽

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.

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Open-Circuit Voltage-Based State of Charge Estimation of Lithium-ion Battery Using Dual Neural Network Fusion Battery Model

Electrochimica Acta ◽

10.1016/j.electacta.2015.12.001 ◽

2016 ◽

Vol 188 ◽

pp. 356-366 ◽

Cited By ~ 73

Author(s):

Xuanju Dang ◽

Li Yan ◽

Kai Xu ◽

Xiru Wu ◽

Hui Jiang ◽

...

Keyword(s):

Neural Network ◽

Lithium Ion Battery ◽

Open Circuit Voltage ◽

Lithium Ion ◽

Open Circuit ◽

State Of Charge ◽

Battery Model ◽

State Of Charge Estimation

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State-of-Charge Estimation of Lithium-Ion Batteries in the Battery Degradation Process Based on Recurrent Neural Network

Energies ◽

10.3390/en14020306 ◽

2021 ◽

Vol 14 (2) ◽

pp. 306

Author(s):

Shuqing Li ◽

Chuankun Ju ◽

Jianliang Li ◽

Ri Fang ◽

Zhifei Tao ◽

...

Keyword(s):

Neural Network ◽

Lithium Ion Battery ◽

High Efficiency ◽

Lithium Ion ◽

Clean Energy ◽

Degradation Process ◽

State Of Charge ◽

Average Error ◽

Estimation Model ◽

Battery Degradation

Due to the rapidly increasing energy demand and the more serious environmental pollution problems, lithium-ion battery is more and more widely used as high-efficiency clean energy. State of Charge (SOC) representing the physical quantity of battery remaining energy is the most critical factor to ensure the stability and safety of lithium-ion battery. The novelty SOC estimation model, which is two recurrent neural networks with gated recurrent units combined with Coulomb counting method is proposed in this paper. The estimation model not only takes voltage, current, and temperature as input feature but also takes into account the influence of battery degradation process, including charging and discharging times, as well as the last discharge charge. The SOC of the battery is estimated by the network under three different working conditions, and the results show that the average error of the proposed neural network is less than 3%. Compared with other neural network structures, the proposed network estimation results are more stable and accurate.

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State of Charge Estimation Error due to Parameter Mismatch in a Generalized Explicit Lithium Ion Battery Model

ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control, Volume 1 ◽

10.1115/dscc2011-6193 ◽

2011 ◽

Cited By ~ 10

Author(s):

Xinfan Lin ◽

Anna Stefanopoulou ◽

Patricia Laskowsky ◽

Jim Freudenberg ◽

Yonghua Li ◽

...

Keyword(s):

Lithium Ion Battery ◽

Hybrid Electric Vehicle ◽

Estimation Error ◽

Lithium Ion ◽

The State ◽

State Of Charge ◽

Parameter Mismatch ◽

Linear Diffusion ◽

Soc Estimation ◽

Battery Model

Model-based state of charge (SOC) estimation with output feedback of the voltage error is steadily augmenting more traditional coulomb counting or voltage inversion techniques in hybrid electric vehicle applications. In this paper, the state (SOC) estimation error in the presence of model parameter mismatch is calculated for a general lithium ion battery model with linear diffusion or impedance-based state dynamics and nonlinear output voltage equations. The estimation error due to initial conditions and inputs is derived for linearized battery models and also verified by nonlinear simulations. It is shown that in some cases of parameter mismatch, the state, e.g. SOC, estimation error will be significant while the voltage estimation error is negligible.

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Lithium-ion battery state of charge estimation using a fractional battery model

ICFDA'14 International Conference on Fractional Differentiation and Its Applications 2014 ◽

10.1109/icfda.2014.6967413 ◽

2014 ◽

Cited By ~ 4

Author(s):

J. M. Francisco ◽

J. Sabatier ◽

L. Lavigne ◽

F. Guillemard ◽

M. Moze ◽

...

Keyword(s):

Lithium Ion Battery ◽

Lithium Ion ◽

State Of Charge ◽

Battery Model ◽

Battery State Of Charge ◽

State Of Charge Estimation

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Research on State of Charge Estimation for Power Battery of Electric Vehicle

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.336-338.799 ◽

2013 ◽

Vol 336-338 ◽

pp. 799-803

Author(s):

Chang Fu Zong ◽

Hai Ou Xiang ◽

Lei He ◽

Dong Xue Chen

Keyword(s):

Estimation Method ◽

Open Circuit ◽

State Of Charge ◽

Method Of Least Squares ◽

Soc Estimation ◽

Battery Model ◽

Power Battery ◽

Internal Parameters ◽

Battery State Of Charge ◽

State Of Charge Estimation

An optimized battery state of charge (SOC) estimation method has been proposed in this paper. The method is based on extended Kalman filter (EKF) and combines Ah counting method and open-circuit voltage (OCV) method. According to the current excitation-response of a battery, the internal parameters of the battery model were identified by the method of least squares. Then the proposed estimation method is verified by experiments. The results show that the estimation method can reduce the cumulative error caused by long discharge and it can estimate the battery SOC effectively and accurately.

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State of Charge Estimation of Lithium-ion Battery Based on Improved Recurrent Neural Network

Journal of Physics Conference Series ◽

10.1088/1742-6596/2109/1/012005 ◽

2021 ◽

Vol 2109 (1) ◽

pp. 012005

Author(s):

Zhenya Wang ◽

Xiaodong Li ◽

Yahui Wang

Keyword(s):

Neural Network ◽

Recurrent Neural Network ◽

Lithium Iron Phosphate ◽

Lithium Ion ◽

Iron Phosphate ◽

Maximum Error ◽

State Of Charge ◽

Equivalent Model ◽

Estimation Model ◽

Soc Estimation

Abstract Aiming at the problem of gradient disappearance and gradient explosion in the recurrent neural network (RNN) algorithm in the battery estimation model, this paper proposes a state of charge (SOC) estimation model developed using an independent recurrent neural network (IndRNN). Firstly, the Thevenin equivalent model of the battery is established, and the parameters of the equivalent model are identified through experiments. Then, the Relu activation function is introduced into the RNN to separate the neurons in each layer. Finally, the model was trained on various experimental data sets collected from the lithium iron phosphate battery experimental platform under different discharge conditions. Without any prior knowledge about the battery interior, the proposed battery model successfully characterizes the non-linear behavior of the battery. The results show that under different discharge conditions, IndRNN is better than RNN in terms of maximum error, the mean error, and the root mean square error (RMSE), which greatly improves the accuracy of SOC estimation.

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Modeling and validation of lithium-ion battery with initial state of charge estimation

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v21.i3.pp1317-1331 ◽

2021 ◽

Vol 21 (3) ◽

pp. 1317

Author(s):

Ali Mohsen Alsabari ◽

M.K Hassan ◽

Azura CS ◽

Ribhan Zafira

Keyword(s):

Lithium Ion Battery ◽

Equivalent Circuit Model ◽

Lithium Ion ◽

Industrial Applications ◽

State Of Charge ◽

Lookup Table ◽

Maximum Relative Error ◽

Initial State ◽

Internal Parameters ◽

Terminal Voltage

The modeling of lithium-ion battery is an important element to the management of batteries in industrial applications. Various models have been studied and investigated, ranging from simple to complex. The second-order equivalent circuit model was studied and investigated since the dynamic behavior of the battery is fully characterized. The simulation model was built in Matlab Simulink using the Kirchhoff Laws principle in mathematical equations, while the battery's internal parameters were identified by using the BTS4000 (Battery tester) device. To estimate the full state of charge (SOC), the initial state of charge (SOC0) must be identified or measured. Hence, this paper seeks for the SOC estimation by using experimental terminal voltage data and SOC with Matlab lookup table. Then, the simulated terminal voltage, as well as the SOC of the battery are compared and validated against measured data. The maximum relative error of 0.015 V and 2% for terminal voltage and SOC respectively shows that the proposed model is accurate and relevant based on the error analysis.

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