SOC estimation for Li-Ion batteries based on equivalent circuit diagrams and the application of a Kalman filter

Rahmoun, Ahmad; Biechl, Helmuth; Rosin, Argo

doi:10.1109/pq.2012.6256238

Cited by 22 publications

(25 citation statements)

References 2 publications

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“…When using the DAEKF method to estimate SOC and SOH, the algorithm has clear requirements for the statistical characteristics of noise. The traditional method calculated H x,k , and H θ,k by fixed length (M), which can be obtained by Equation (16). However, in practical application, the noise will be affected by the measurement error of the sensor and the external environment, which influenced the error innovation sequence (EIS), as shown in Equation (14).…”

Section: Intelligent Noise Estimatormentioning

confidence: 99%

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Joint estimation of state of charge and state of health for lithium‐ion battery based on dual adaptive extended Kalman filter

Min

Gao

et al. 2021

Int J Energy Res

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Lithium-ion batteries (LIBs) are widely used in electric vehicles due to its high energy density and low pollution. As the key monitoring parameters of battery management system (BMS), accurate estimation of the state of charge (SOC) and state of health (SOH) can promote the utilization rate of battery, which is of great significance to ensure the safe use of LIBs. In this paper, a novel dual Kalman filter method is proposed to achieve simultaneous SOC and SOH estimation. This paper improves the estimation accuracy of SOC and SOH from the following four aspects. Firstly, the widely used equivalent circuit model is established as the battery model in this paper, and the forgetting factor recursive least squares (FFRLS) method is applied to identify the model parameters. Secondly, two kinds of single-variable battery states are established to analyze the influence of OCV-SOC curve and battery capacity on SOC estimation. Based on this, an error model is proposed combined with Kalman filter to achieve better estimation results of SOC and SOH. Besides, to promote the accuracy of SOC estimation, based on the error innovation sequence (EIS) and residual innovation sequence (RIS), the improved dual adaptive extended Kalman filter (IDAEKF) algorithm based on dynamic window is proposed. Finally, the superiority of the proposed model is verified under different cycles. Experimental results show that the estimation error of SOC and SOH is controlled within 1%. K E Y W O R D S dynamic window, improved dual-adaptive extended Kalman filter (IDAEKF), lithium-ion battery (LIB), state of charge (SOC), state of health (SOH), error model

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Section: Intelligent Noise Estimatormentioning

confidence: 99%

“…Plett 15 proposed EKF to estimate SOC, and the SOC estimation error is less than 5%. Rahmoun et al 16 used EKF to estimate SOC based on the first-order ECM and the second-order ECM, respectively. The experimental results show that the SOC estimated based on the second-order ECM has better results than the first-order ECM.…”

Section: Introductionmentioning

confidence: 99%

Joint estimation of state of charge and state of health for lithium‐ion battery based on dual adaptive extended Kalman filter

Min

Gao

et al. 2021

Int J Energy Res

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show abstract

“…where x(k∕k − 1) and x(k∕k) denote the priori estimate and posteriori estimate of the state vector, K g is Kalman gain, Q and R are the noise covariance matrix. KF algorithm mainly consists of two stages: First, the first two equations of Equation (18) are used to complete the state prediction, and the estimated state vector x (k − 1/ k − 1) and covariance matrix…”

Section: Soc Estimation Based On An Improved Dkfmentioning

confidence: 99%

“…Step Step 4: Secondary SOC filtering. According to Table 1 and Equation (18), by using the error between Ah integral method and the EKF algorithm, the KF algorithm was used to estimate and update SOC. This process overcomes the interference of the accumulated cur-rent measurement error resulting from the Ah integral method Step 5: Repeat steps 3 and 4 to obtain SOC estimation results.…”

Section: Soc Estimation Based On An Improved Dkfmentioning

confidence: 99%

SOC estimation for lithium‐ion batteries based on a novel model

Gao

et al. 2021

IET Power Electronics

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Lithium‐ion batteries (LIBs) are widely used in electric vehicles because of their high energy density and less pollution. As an important parameter of the battery management system, accurate estimation of the state of charge (SOC) of the battery can ensure the energy distribution and safe use of the battery. This paper obtains better estimation accuracy from four aspects. First, the battery model is established via Thevenin equivalent circuit model, and the parameters are identified by the forgetting factor recursive least squares. Second, the influence of dual extended Kalman filter on SOC estimation is analysed, a novel algorithm‐based improved dual Kalman filter is proposed. Besides, to reduce the influence of the system noise on the estimation results, an adaptive intelligent algorithm is applied to promote the accuracy of SOC estimation. Finally, compared with the estimated SOC results of the traditional algorithm, the experimental results show the effectiveness of the algorithm.

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“…In [91,92], NN; in [93], adaptive wavelet neural network (AWNN); and in [94], Elman neural network (ENN) methods are proposed to estimate the SOC of lithium ion batteries. Many researchers pay attention to KF [95,96]; and its derivatives broadly such as series Kalman filter (SKF) [97]; EKF [10,[98][99][100][101][102][103][104][105][106][107][108]; improved extended Kalman filter (IEKF) [109]; AEKF [110][111][112][113]; model adaptive extended Kalman filter (MAEKF) [114]; robust extended Kalman filter (REKF) [115]; multiscale extended Kalman filter (MEKF) [116]; UKF [117][118][119]; adaptive unscented Kalman filter (AUKF) [120,121]; sigma point Kalman filter (SPKF) [122,123] and iterated extended Kalman filter (ITEKF) [124]. In [119], a modified battery equivalent circuit model is designed that contains the impact of different temperatures and current rates on the SOC.…”

Section: Battery State Of Charge (Soc) Estimationmentioning

confidence: 99%

A comprehensive review on estimation strategies used in hybrid and battery electric vehicles

Cuma

Köroğlu

2015

Renewable and Sustainable Energy Reviews

304

119

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SOC estimation for Li-Ion batteries based on equivalent circuit diagrams and the application of a Kalman filter

Cited by 22 publications

References 2 publications

Joint estimation of state of charge and state of health for lithium‐ion battery based on dual adaptive extended Kalman filter

Joint estimation of state of charge and state of health for lithium‐ion battery based on dual adaptive extended Kalman filter

SOC estimation for lithium‐ion batteries based on a novel model

A comprehensive review on estimation strategies used in hybrid and battery electric vehicles

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