Comparative study of ANN/KF for on-board SOC estimation for vehicular applications

Sassi, Hicham Ben; Errahimi, Fatima; Es-Sbai, Najia; Alaoui, Chakib

doi:10.1016/j.est.2019.100822

Cited by 37 publications

(19 citation statements)

References 43 publications

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“…The equivalent circuit model method mainly includes a series of Kalman filter derivative methods, such as: extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filtering (PF) [12][13][14]. The accuracy of the Kalman filter method depends on an accurate battery model which is difficult to obtain [15][16][17]. The artificial intelligence method is an SOC estimation method based on machine learning strategies, including the neural network (NN), deep learning (DL), support vector machine (SVM) and fuzzy logic methods.…”

Section: Introductionmentioning

confidence: 99%

State of Charge Estimation for Lithium-Ion Battery Based on NARX Recurrent Neural Network and Moving Window Method

Wang

et al. 2021

IEEE Access

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An accurate state of charge (SOC) estimation depends on an accurate battery model. The influence of nonlinear and unstable interference factors makes the accurate SOC estimation difficult. To obtain an accurate battery model, a method based on the NARX (nonlinear autoregressive network with exogenous inputs) recurrent neural network and moving window method is proposed. This paper improves the accuracy, modelling speed and robustness of SOC estimation from the following three aspects. First, to overcome the excessive reliance on the amount of data in the model training process, the NARX recurrent neural network is used to establish the battery model. NARX (nonlinear autoregressive with external input) recurrent neural network with the delay and feedback functions can keep the input and output of a previous moment and add it to the calculation of the next moment. Therefore, better estimation results are achieved using a small amount of data; second, the moving window method is used against the gradient explosion and the gradient vanishing that may occur in the NARX model training process. Third, by comparing it with other methods under different working conditions and different temperatures, the validity of the proposed model is verified. The results indicate that the proposed model has a higher accuracy and speed of the SOC estimation. The RMSE performance of the proposed model is reduced by approximately 65%, and the execution time is shortened by approximately 50%.

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Section: Introductionmentioning

confidence: 99%

State of Charge Estimation for Lithium-Ion Battery Based on NARX Recurrent Neural Network and Moving Window Method

Wang

et al. 2021

IEEE Access

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“…The methods use the Kalman filter [9], extended Kalman filter (EKF) [10] and particle point (PF) [11,12]. Additionally, there are SoC estimation methods based on learning algorithms such as neural networks [13,14], fuzzy logic and the nonlinear fractional model [15,16].…”

Section: Introductionmentioning

confidence: 99%

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

Van

Vinh

2020

Applied Sciences

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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.

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“…The main advantage of this method is its ability to estimate the SOC independently, i.e. regardless of the battery's nonlinearity [8][9][10]. Although it does not require the battery model, a huge number of data sets are needed to train the network.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, the model-based SOC utilizes the battery's current and voltage to estimate the parameters of an equivalent (electrical) circuit model. The most widely-use method is the Kalman filter (KF), and its variations such as extended Kalman filter (EKF) [11] and sigma-point Kalman filter (SPKF) [10], [12]. Since KF is basically designed as a state estimator, it requires an offline identification of the model's parameters prior to the online estimation of the SOC.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Online Lyapunov-Based Adaptive State of Charge Observer for Lithium-Ion Batteries Under Low Excitation Level

2020

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The online estimation of the state-of-charge (SOC) of Li-ion battery using the adaptive Lyapunov-based observer is an attractive proposition due to the ensured stability, adaptability and reduced computing requirement. However, the observer requires the presence of the persistent excitation (PE) to guarantee the convergence of the battery model parameters to their correct values. The PE is satisfied using sufficiently rich (SR) signal that contains spectral components to excite the battery model. This paper revisits several important works that utilize such observer and highlights the absence of PE in their practical implementation. Since the previous works utilize dc excitation that lacks the SR characteristics, the validity of the published results is questionable. To rectify the problem, a scheme known as the forced excitation is proposed to estimate the battery parameters under dc or low excitation level. The SR signal is generated by chopping the battery current at a certain rate for specific interval. Moreover, the disruption of the load current (due to the chopping) is compensated using the supercapacitor. The concept is simulated by Matlab/Simulink and is realized experimentally using a Panasonic NCR18650B Li-ion battery. The forced excitation algorithm is implemented on the DS1104 dSPACE platform. The results show that the proposed method satisfies the PE condition and is able to correctly estimate the SOC even with low excitation signals.

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Comparative study of ANN/KF for on-board SOC estimation for vehicular applications

Cited by 37 publications

References 43 publications

State of Charge Estimation for Lithium-Ion Battery Based on NARX Recurrent Neural Network and Moving Window Method

State of Charge Estimation for Lithium-Ion Battery Based on NARX Recurrent Neural Network and Moving Window Method

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

Analysis of Online Lyapunov-Based Adaptive State of Charge Observer for Lithium-Ion Batteries Under Low Excitation Level

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