Lithium-Ion Battery State of Charge (SoC) Estimation with Non-Electrical parameter using Uniform Fiber Bragg Grating (FBG)

Ee, Yen-Jie; Tey, Kok-Soon; Lim, Kok-Sing; Shrivastava, Prashant; Adnan, S.B.R.S.; Ahmad, Harith

doi:10.1016/j.est.2021.102704

Cited by 45 publications

(13 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It can be seen from equation (22), When SOC has an error of D, f(SOC) also has an error of 1.12D, that is U}f(SOC)}SOC. SOC and capacity can also be linked by ampere-hour integration.…”

Section: Analysis Of the Influence Degree Of Each Parameter On Termin...mentioning

confidence: 99%

See 1 more Smart Citation

A three-time-scale dual extended Kalman filtering for parameter and state estimation of Li-ion battery

Zhu

Qian

Liu

2023

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

Estimation of battery state and parameters play an important role in electric vehicle battery management system (BMS). Second-order RC model is applied, the initial parameters of battery model are determined by experiments. Data points of open circuit voltage and state of charge (OCV-SOC) are determined by experiment. Different function forms are used to fit the OCV-SOC discrete points, and the function form with great fitting effect is selected as the OCV-SOC fitting form. Dual extended Kalman filter which is divided into Parameter filter and state filter is applied. Battery state in state filter is a fast-time-varying parameter, The battery model parameters in parameter filter are divided into two parts. the battery model parameters are classified according to the influence of each parameter on the terminal voltage. A longer sampling time is applied to the parameters that have a strong impact on the terminal voltage, and a longest sampling time is applied to the parameters that have a weak impact on the terminal voltage. The time-scale classification method is validated both quantitatively and qualitatively. Compared with the previous methods, the three-time-scale classification method can reduce the number of parameter updates.

show abstract

“…It can be seen from equation (22), When SOC has an error of D, f(SOC) also has an error of 1.12D, that is U}f(SOC)}SOC. SOC and capacity can also be linked by ampere-hour integration.…”

Section: Analysis Of the Influence Degree Of Each Parameter On Termin...mentioning

confidence: 99%

“…After the discharge is stopped, the battery model constitutes zero input response, and the voltage-time satisfies the following equation 21,22 :…”

Section: Battery Model and Experiments Designmentioning

confidence: 99%

A three-time-scale dual extended Kalman filtering for parameter and state estimation of Li-ion battery

Zhu

Qian

Liu

2023

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

show abstract

“…In general, the SOC simulation curves for the two improved algorithms under different SOC initial values can quickly fall to the error reference line. [39][40][41] are also listfigureed. Compared with other algorithms, the IIAE algorithm has advantages of lower Max and RMSE, but its mean error is higher.…”

Section: Effects Of the Noise Covariance On The Estimationmentioning

confidence: 99%

Enhancing Stability and Robustness of State-of-Charge Estimation for Lithium-Ion Batteries by Using Improved Adaptive Kalman Filter Algorithms

2021

View full text Add to dashboard Cite

The traditional Kalman filter algorithms have disadvantages of poor stability (the program cannot converge or crash), robustness (sensitive to the initial errors) and accuracy, partially resulted from the fact that noise covariance matrices in the algorithms need to be set artificially. To overcome the above problems, some adaptive Kalman filter (AKF) algorithms are studied, but the problems still remain unsolved. In this study, two improved AKF algorithms, the improved Sage-Husa and innovation-based adaptive estimation (IAE) algorithms, are proposed. Under the different operating conditions, the estimation accuracy, filter stability, and robustness of the two proposed algorithms are analyzed. Results show that the state of charge (SOC) Max error based on the improved Sage-Husa and the improved IAE is less than 3% and 1.5%, respectively, while the Max errors of the original algorithms is larger than 16% and 4% The two proposed algorithms have higher filter stability than the traditional algorithms. In addition, analyses of the robustness of the two proposed algorithms are carried out by changing the initial parameters, proving that neither are sensitive to the initial errors.

show abstract

“…However, these techniques are not dynamic and have a slow time response. However, to improve the speed of peak detection, different machine learning techniques have been developed such as support vector machine (SVM) [17], decision tree with SVM [18], extreme learning machine [19], K-nearest neighbours' algorithm [20], deep learning network [21], feature extraction support vector machine (FE-SVM) [22], deep convolutional neural network [23], etc. In these techniques, however, there are some drawbacks regarding mean square error and speed, etc.…”

Section: Introductionmentioning

confidence: 99%

Opto-Electronics Review

Kumar,

Sengupta

2022

View full text Add to dashboard Cite

An adaptive and precise peak wavelength detection algorithm for fibre Bragg grating using generative adversarial network is proposed. The algorithm consists of generative model and discriminative model. The generative model generates a synthetic signal and is sampled for training using a deep neural network. The discriminative model predicts the real fibre Bragg grating signal by the calculation of the loss functions. The maxima of loss function of the discriminative signal and the minima of loss function of the generative signal are matched and the desired peak wavelength of fibre Bragg grating is determined. The proposed algorithm is verified theoretically and experimentally for a single fibre Bragg grating peak. The accuracy has been obtained as ±0.2 pm. The proposed algorithm is adaptive in the sense that any random fibre Bragg grating peak can be identified within a short wavelength range.

show abstract

Lithium-Ion Battery State of Charge (SoC) Estimation with Non-Electrical parameter using Uniform Fiber Bragg Grating (FBG)

Cited by 45 publications

References 34 publications

A three-time-scale dual extended Kalman filtering for parameter and state estimation of Li-ion battery

A three-time-scale dual extended Kalman filtering for parameter and state estimation of Li-ion battery

Enhancing Stability and Robustness of State-of-Charge Estimation for Lithium-Ion Batteries by Using Improved Adaptive Kalman Filter Algorithms

Opto-Electronics Review

Contact Info

Product

Resources

About