SOC calculation method based on extended Kalman filter of power battery for electric vehicle

Liu, Xin Lei; Cheng, Zeng Mu; Yi, Fengyan; Qiu, Tian

doi:10.1109/iske.2017.8258840

Cited by 12 publications

(5 citation statements)

References 3 publications

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“…At present, many improved methods have been generated based on this method [61][62][63][64][65][66]. For example, the Extended Kalman Filtering (EKF) method, which linearizes the nonlinear system; the Unscented Kalman Filtering (UKF) method is obtained by adding the transformation of U to KF which deals with the nonlinear problem with a probability distribution; the Central Difference Kalman Filtering (CDKF) method processes KF using the central difference method., etc.…”

Section: Kalman Filter Methodsmentioning

confidence: 99%

Review on the State of Charge Estimation Methods for Electric Vehicle Battery

Zhang

Fan

2020

WEVJ

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Battery technology has been one of the bottlenecks in electric cars. Whether it is in theory or in practice, the research on battery management is extremely important, especially for battery state-of-charge estimation. In fact, the battery has a strong time-varying and non-linear properties, which are extremely complex. Therefore, accurately estimating the state of charge is a challenging task. This paper reviews various representative patents and papers related to the state of charge estimation methods for an electric vehicle battery. According to their theoretical and experimental characteristics, the estimation methods were classified into three groups: the traditional methods based on the battery experiments, the modern methods based on control theory, and other methods based on the innovative ideas, especially focusing on the algorithms based on control theory. The results imply that the algorithms based on control theory, especially intelligent algorithms, are the focus of research in this field. The future development direction is to establish a rich database, improve hardware technology, come up with a much better battery model, and give full play to the advantages of each algorithm.

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Section: Kalman Filter Methodsmentioning

confidence: 99%

Review on the State of Charge Estimation Methods for Electric Vehicle Battery

Zhang

Fan

2020

WEVJ

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“…(13) (14) Where, , . Using the equations of a discrete system established by equations ( 13)-( 14), the basic recursion process of the extended equations is as follows:…”

Section: Extended Kalman Filter Algorithm Researchmentioning

confidence: 99%

“…From Figure 1, in 2018, the sales volume of electric vehicles was low, and the fundamental reason was the limitation of technical conditions, but the main scientific and technological projects of "electric vehicles" carried out in China have established a "three vertical and three horizontal" research and development layout, fully launched the research and development of large-scale electric vehicle technology, and China's electric vehicles have entered the fast lane [12][13]. At the same time, with the support of national policies, China's electric vehicle industry has not only improved production and sales, but also focused on improving the performance and quality of the entire vehicle [14]. BAIC, BYD and other family car brands are at the forefront of global car sales rankings, and they are gradually catching up with Tesla, the global leader in electric vehicles.…”

Section: Introductionmentioning

confidence: 99%

Research on SOC estimation of residual power of lithium-ion batteries for electric vehicles based on extended Kalman filtering algorithm

Jiang

2023

Applied Mathematics and Nonlinear Sciences

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As the energy carrier of electric vehicles, how to accurately estimate the remaining power (SOC) of the battery is one of the key technologies in the field of electric vehicle design. Effective estimation of SOC can bring accurate continuous mileage information to the driver, theoretically avoid overcharging and discharging the battery, and also protect the driver’s driving safety. In the research of SOC estimation method, constructing a suitable battery model is an important means to improve SOC estimation and to improve the prediction accuracy. In order to obtain a higher response accuracy of the model, this paper proposes an electric vehicle SOC model based on the extended Kalman filter algorithm. Based on the actual data of lithium-ion power battery, SOC estimation research is carried out. The research shows that: when the internal temperature of the battery is the same as the ambient temperature, and both are 25 °C, the model is accurate, the terminal voltage difference is small, and the average voltage difference is 9mV respectively; at room temperature, the extended Kalman filter algorithm has a significant effect on the recovery percentage of SOC voltage. The average is over 73%, and the accuracy is high. The extended Kalman algorithm in this paper we use to estimate the SOC current waveform. The simulation results show that the SOC discharge current is 4A, which has high estimation accuracy and strong applicability.

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“…One of the solutions traditionally approached to deal with nonlinear ones, for SoC estimation, are those based on adaptive systems, in particular Kalman filter (KF) and its variants [ 18 , 19 , 20 ]. In this method, the battery is seen as a power system and is described as an equivalent circuit.…”

Section: Related Workmentioning

confidence: 99%

State of Charge Estimation of Battery Based on Neural Networks and Adaptive Strategies with Correntropy

Vieira

Villanueva

Flores

et al. 2022

Sensors

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Nowadays, electric vehicles have gained great popularity due to their performance and efficiency. Investment in the development of this new technology is justified by increased consciousness of the environmental impacts caused by combustion vehicles such as greenhouse gas emissions, which have contributed to global warming as well as the depletion of non-oil renewable energy source. The lithium-ion battery is an appropriate choice for electric vehicles (EVs) due to its promising features of high voltage, high energy density, low self-discharge, and long life cycles. In this context, State of Charge (SoC) is one of the vital parameters of the battery management system (BMS). Nevertheless, because the discharge and charging of battery cells requires complicated chemical operations, it is therefore hard to determine the state of charge of the battery cell. This paper analyses the application of Artificial Neural Networks (ANNs) in the estimation of the SoC of lithium batteries using the NASA’s research center dataset. Normally, the learning of these networks is performed by some method based on a gradient, having the mean squared error as a cost function. This paper evaluates the substitution of this traditional function by a measure of similarity of the Information Theory, called the Maximum Correntropy Criterion (MCC). This measure of similarity allows statistical moments of a higher order to be considered during the training process. For this reason, it becomes more appropriate for non-Gaussian error distributions and makes training less sensitive to the presence of outliers. However, this can only be achieved by properly adjusting the width of the Gaussian kernel of the correntropy. The proper tuning of this parameter is done using adaptive strategies and genetic algorithms. The proposed identification model was developed using information for training and validation, using a dataset made available in a online repository maintained by NASA’s research center. The obtained results demonstrate that the use of correntropy, as a cost function in the error backpropagation algorithm, makes the identification procedure using ANN networks more robust when compared to the traditional Mean Squared Error.

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SOC calculation method based on extended Kalman filter of power battery for electric vehicle

Cited by 12 publications

References 3 publications

Review on the State of Charge Estimation Methods for Electric Vehicle Battery

Review on the State of Charge Estimation Methods for Electric Vehicle Battery

Research on SOC estimation of residual power of lithium-ion batteries for electric vehicles based on extended Kalman filtering algorithm

State of Charge Estimation of Battery Based on Neural Networks and Adaptive Strategies with Correntropy

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