The Experiments of Dual Kalman Filter in Lithium Battery SOC Estimation

Huang, Jian; Wang, Ying Nan; Wang, Zhong Feng; Han, Feng; Li, Li Gang

doi:10.4028/www.scientific.net/amm.494-495.1509

Cited by 6 publications

(7 citation statements)

References 6 publications

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“…Besides, to promote the estimation result of SOC, a dual extended Kalman filter (DEKF) is proposed to estimate SOC and model parameters simultaneously. 17,18 The DEKF method uses two EKF filters to correct the feedback error and measurement error of the model in real-time, which provides better SOC estimation results.…”

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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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: 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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“…Hu [19] and Xiong [20] used an extended Kalman filter to estimate SOC in the self-correcting model and Thevenin model, respectively, and both of those methods achieved good results. Besides, to further promote the estimation result of SOC, a dual extended Kalman filter (DEKF) is proposed to estimate SOC and model parameters simultaneously [21,22]. The DEKF method uses two EKF filters to correct the feedback error and measurement error of the model in real-time, which provides better SOC estimation results.…”

Section: Introductionmentioning

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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“…For lithium-ion batteries, the battery parameters change very slowly [11]. So we model them as constants with some small perturbations.…”

Section: Parameter Updatingmentioning

confidence: 99%

“…The accuracy of the model-based methods depends on not only the establishment of the battery equivalent model but also the battery model parameters. In the process of charging and discharging, the battery model parameters are not constant, which will lead to the drifting of the SOC estimation result [11]. In order to solve the problem, some researchers consider two Kalman filters structure to estimate SOC [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…Several methods of estimating the battery SOC have been proposed including ampere-hour counting (Ah), open circuit voltage (OCV), artificial neural networks (ANN), fuzzy logic (FL), particle filter (PF), and improved Kalman filters [2][3][4][5][6][7][8][9][10][11][12][13][14][15]. Among them, Ah method is one of the most commonly used methods.…”

Section: Introductionmentioning

confidence: 99%

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State of Charge Estimation for Lithium‐Ion Battery by Using Dual Square Root Cubature Kalman Filter

Luping

Wang

2017

Mathematical Problems in Engineering

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The state of charge (SOC) plays an important role in battery management systems (BMS). However, SOC cannot be measured directly and an accurate state estimation is difficult to obtain due to the nonlinear battery characteristics. In this paper, a method of SOC estimation with parameter updating by using the dual square root cubature Kalman filter (DSRCKF) is proposed. The proposed method has been validated experimentally and the results are compared with dual extended Kalman filter (DEKF) and dual square root unscented Kalman filter (DSRUKF) methods. Experimental results have shown that the proposed method has the most balance performance among them in terms of the SOC estimation accuracy, execution time, and convergence rate.

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The Experiments of Dual Kalman Filter in Lithium Battery SOC Estimation

Cited by 6 publications

References 6 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

State of Charge Estimation for Lithium‐Ion Battery by Using Dual Square Root Cubature Kalman Filter

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