Reliable and Robust Observer for Simultaneously Estimating State-of-Charge and State-of-Health of LiFePO4 Batteries

Batteries are everywhere, in all forms of transportation, electronics, and constitute a method to store clean energy. Among the diverse types available, the lithium-iron-phosphate (LiFePO4) battery stands out for its common usage in many applications. For the battery’s safe operation, the state of charge (SOC) and state of health (SOH) estimations are essential. Therefore, a reliable and robust observer is proposed in this paper which could estimate the SOC and SOH of LiFePO4 batteries simultaneously with high accuracy rates. For this purpose, a battery model was developed by establishing an equivalent-circuit model with the ambient temperature and the current as inputs, while the measured output was adopted to be the voltage where current and terminal voltage sensors are utilized. Another vital contribution is formulating a comprehensive model that combines three parts: a thermal model, an electrical model, and an aging model. To ensure high accuracy rates of the proposed observer, we adopt the use of the dual extend Kalman filter (DEKF) for the SOC and SOH estimation of LiFePO4 batteries. To test the effectiveness of the proposed observer, various simulations and test cases were performed where the construction of the battery system and the simulation were done using MATLAB. The findings confirm that the best observer was a voltage-temperature (VT) observer, which could observe SOC accurately with great robustness, while an open-loop observer was used to observe the SOH. Furthermore, the robustness of the designed observer was proved by simulating ill-conditions that involve wrong initial estimates and wrong model parameters. The results demonstrate the reliability and robustness of the proposed observer for simultaneously estimating the SOC and SOH of LiFePO4 batteries.

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A comprehensive equivalent circuit model for lithium-ion batteries, incorporating the effects of state of health, state of charge, and temperature on model parameters

Journal of Energy Storage ◽

10.1016/j.est.2021.103252 ◽

2021 ◽

Vol 43 ◽

pp. 103252

Author(s):

Manh-Kien Tran ◽

Manoj Mathew ◽

Stefan Janhunen ◽

Satyam Panchal ◽

Kaamran Raahemifar ◽

...

Keyword(s):

Lithium Ion Batteries ◽

Equivalent Circuit ◽

Equivalent Circuit Model ◽

Lithium Ion ◽

Circuit Model ◽

State Of Charge ◽

Model Parameters ◽

Health State ◽

State Of Health

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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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Online Identification of Thevenin Equivalent Circuit Model Parameters and Estimation State of Charge of Lithium-Ion Batteries

2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) ◽

10.1109/eeeic.2018.8493924 ◽

2018 ◽

Cited By ~ 11

Author(s):

Edoardo Locorotondo ◽

Luca Pugi ◽

Lorenzo Berzi ◽

Marco Pierini ◽

Giovanni Lutzemberger

Keyword(s):

Lithium Ion Batteries ◽

Equivalent Circuit ◽

Equivalent Circuit Model ◽

Lithium Ion ◽

Circuit Model ◽

State Of Charge ◽

Model Parameters ◽

Online Identification ◽

Thevenin Equivalent

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Lithium-Ion Battery Parameters and State of Charge Joint Estimation Using Bias Compensation Least Squares and the Alternate Algorithm

Mathematical Problems in Engineering ◽

10.1155/2020/1757384 ◽

2020 ◽

Vol 2020 ◽

pp. 1-16 ◽

Cited By ~ 1

Author(s):

Wei Xiong ◽

Yimin Mo ◽

Cong Yan

Keyword(s):

Least Squares ◽

Computational Efficiency ◽

High Accuracy ◽

State Of Charge ◽

Joint Estimation ◽

Model Parameters ◽

Battery Management System ◽

Battery Management ◽

Soc Estimation ◽

Bias Compensation

For safe and efficient operation of electric vehicles (EVs), battery management system is essential. Nevertheless, a challenge lying in battery management systems is how to obtain an algorithm for state of charge (SOC) estimation that has both high accuracy and low computational cost. For this purpose, the battery parameters and SOC joint estimation algorithm based on bias compensation least squares and alternate (BCLS-ALT) algorithm are proposed in this paper. The battery model parameters are identified online using the bias compensation least squares (BCLS), while the SOC is estimated applying the alternate (ALT) algorithm, which can switch the computational logic between H-infinity filter (HIF) and ampere-hour integral (AHI) to improve the computational efficiency and accuracy. The experimental results show that the accuracy of the SOC estimated by the BCLS-ALT algorithm is the highest, and the computational efficiency is also high, with the switching threshold SOCALT being set to 25%. Despite the 20% initial error and the 10% current drift, the proposed BCLS-ALT algorithm can obtain high accuracy and robustness of SOC estimation under different ambient temperatures and dynamic load profiles.

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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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A Strategy for Estimating State-of-Charge and State-of-Health of Li-Ion Batteries in Electric and Hybrid Electric Vehicles

Volume 6: Energy, Parts A and B ◽

10.1115/imece2012-87324 ◽

2012 ◽

Author(s):

Xiaowei Zhao ◽

Guoyu Zhang ◽

Lin Yang

Keyword(s):

Equivalent Circuit ◽

Estimation Method ◽

Equivalent Circuit Model ◽

Circuit Model ◽

State Of Charge ◽

State Of Health ◽

Li Ion Batteries ◽

Soc Estimation ◽

Kaiman Filtering ◽

Li Ion

A task that has to be solved for the application of batteries in vehicles with an electric drive train is the determination of the actual state-of-health (SOH) and state-of-charge (SOC) of the battery cells. In this paper, an on board strategy for estimating SOC and SOH of Li-ion batteries is proposed. The equivalent circuit model is used for both SOC and SOH estimations. In SOH algorithm, the estimated value of battery capacity not only reflects the aging degree of battery pack, but also provides information for SOC estimation. Meanwhile, the extended Kaiman filtering is used in SOC estimation. Because the performance of the equivalent circuit model will be better at small currents than at high currents, extended Kaiman filtering is substituted by Ampere-Hour counting when the absolute value of current is greater than a calibration value. The Digatron battery tester was used to evaluate the proposed estimation method, and results show that the estimation method has high accuracy and efficiency at ordinary temperatures.

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Application of Machine Learning in Battery: State of Charge Estimation Using Feed Forward Neural Network for Sodium-Ion Battery

Electrochem ◽

10.3390/electrochem3010003 ◽

2022 ◽

Vol 3 (1) ◽

pp. 42-57

Author(s):

Devendrasinh Darbar ◽

Indranil Bhattacharya

Keyword(s):

Neural Network ◽

Equivalent Circuit Model ◽

Reference Electrode ◽

State Of Charge ◽

Training Data ◽

Model Parameters ◽

Feed Forward Neural Network ◽

Feed Forward ◽

Non Linear ◽

Battery State Of Charge

Estimating the accurate State of Charge (SOC) of a battery is important to avoid the over/undercharging and protect the battery pack from low cycle life. Current methods of SOC estimation use complex equations in the Extended Kalman Filter (EKF) and the equivalent circuit model. In this paper, we used a Feed Forward Neural Network (FNN) to estimate the SOC value accurately where battery parameters such as current, voltage, and charge are mapped directly to the SOC value at the output. A FNN could self-learn the weights with each training data point and update the model parameters such as weights and bias using a combination of two gradient descents (Adam). This model comprises the Dropout technique, which can have many neural network architectures by dropping the neuron/mode at each epoch/training cycle using the same weights and biases. Our FNN model was trained with data comprising different current rates and tested for different cycling data, for example, 5th, 10th, 20th, and 50th cycles and at a different cutoff voltage (4.5 V). The battery used for estimating the SOC value was a Na-ion based battery, which is highly non-linear, and it was fabricated in a house using Na0.67Fe0.5Mn0.5O2 (NFM) as a cathode and Na metal as a reference electrode. The FNN successfully estimated the SOC value for the highly non-linear nature of the Na-ion battery at different current rates (0.05 C, 0.1 C, 0.5 C, 1 C, 2 C), for different cycling data, and at higher cut-off voltage of –4.5 V Na+, reaching the R2 value of ~0.97–~0.99, ~0.99, and ~0.98, respectively.

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