A multi-scale parameter adaptive method for state of charge and parameter estimation of lithium-ion batteries using dual Kalman filters

Guo, Feng; Hu, Guangdi; Shun, Xiang; Zhou, Pengkai; Hong, Ru; Xiong, Neng

doi:10.1016/j.energy.2019.04.126

Cited by 97 publications

(37 citation statements)

References 30 publications

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“…Many methods for estimating battery model parameter are proposed, such as the recursive least square algorithm, the dual/joint KFs, the dual PFs, and variations of the above methods . However, the abovementioned methods only select a few battery model parameters to be estimated, such as battery capacity and battery impedance.…”

Section: Introductionmentioning

confidence: 99%

The equivalent circuit battery model parameter sensitivity analysis for lithium‐ion batteries by Monte Carlo simulation

Guo

Zhou

et al. 2019

Int J Energy Res

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To enhance the estimation accuracy of battery's state of charge, it is imperative to estimate the battery model parameter. To reduce the calculation efforts, the number of the battery model parameter to be estimated should be less while ensuring the state of charge estimation accuracy. Especially in engineering applications, the calculating ability is usually limited. So, it needs to choose the critical battery model parameter to be estimated. This paper's contributions are as follows: The global sensitivity analysis of the battery model parameter is achieved by the Monte Carlo simulation method. The results show that the open circuit voltage and the ohmic resistance are the high sensitivity parameters. Guided by the results of parameter sensitivity analysis, a dual extended Kalman filters method is utilized to achieve online battery model parameter estimation. The experiments prove that the state of charge estimation accuracy is improved by the online parameter estimation. Estimating high sensitivity parameters can reduce running time. And the SOC estimation accuracy can be guaranteed.

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Section: Introductionmentioning

confidence: 99%

The equivalent circuit battery model parameter sensitivity analysis for lithium‐ion batteries by Monte Carlo simulation

Guo

Zhou

et al. 2019

Int J Energy Res

Self Cite

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“…Among them, the algorithms that are more suitable for real-time SoC estimation can be roughly divided into: model-based estimation methods [19] and data-driven estimation methods [20]. Model-based estimation methods mainly include AF algorithms (such as EKF and UKF) and observer-based methods (such as sliding mode observer (SMO) and H∞ observer) [21], while data-driven algorithms mainly include Coulomb's integral algorithm [22], neural network (NN) algorithm [20], and so on. Because completely data-driven SoC estimation methods completely consider the power batteries as a black box, the estimation accuracy depends too much on the training dataset and the quality of the data collected in real time.…”

Section: Application Of Combination Algorithm In Battery Soc Estimationmentioning

confidence: 99%

Research on Parameter Self-Learning Unscented Kalman Filtering Algorithm and Its Application in Battery Charge of State Estimation

Liu

et al. 2020

Energies

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A novel state estimation algorithm based on the parameters of a self-learning unscented Kalman filter (UKF) with a model parameter identification method based on a collaborative optimization mechanism is proposed in this paper. This algorithm can realize the dynamic self-learning and self-adjustment of the parameters in the UKF algorithm and the automatic optimization setting Sigma points without human participation. In addition, the multi-algorithm collaborative optimization mechanism unifies a variety of algorithms, so that the identification method has the advantages of member algorithms while avoiding the disadvantages of them. We apply the combination algorithm proposed in this paper for state of charge (SoC) estimation of power batteries and compare it with other model parameter identification algorithms and SoC estimation methods. The results showed that the proposed algorithm outperformed the other model parameter identification algorithms in terms of estimation accuracy and robustness.

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“…Besides, several EKF-based methods, such as lazy EKF and robust EKF, are proposed to estimate the SoC of the battery and in different scenarios for vehicle onboard battery and microgrid energy storage units [15,16]. The Dual EKF (DEKF) adopted in [17,18] to estimate the SoC of the battery and the parameter of the model shows faster convergence in shorter calculation time. The unscented KF (UKF) transforms the nonlinear models by linear interpolation.…”

Section: Of 21mentioning

confidence: 99%

“…x < 2828, SoC = 0 (18) The Ah method quantifies the external influence factors which mainly refer to the charging/discharging rate and the temperature. The principle of Ah method is shown in (20).…”

mentioning

confidence: 99%

State of Charge Estimation of a Composite Lithium-Based Battery Model Based on an Improved Extended Kalman Filter Algorithm

et al. 2019

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The battery State of Charge (SoC) estimation is one of the basic and significant functions for Battery Management System (BMS) in Electric Vehicles (EVs). The SoC is the key to interoperability of various modules and cannot be measured directly. An improved Extended Kalman Filter (iEKF) algorithm based on a composite battery model is proposed in this paper. The approach of the iEKF combines the open-circuit voltage (OCV) method, coulomb counting (Ah) method and EKF algorithm. The mathematical model of the iEKF is built and four groups of experiments are conducted based on LiFePO4 battery for offline parameter identification of the model. The iEKF is verified by real battery data. The simulation results with the proposed iEKF algorithm under both static and dynamic operation conditions show a considerable accuracy of SoC estimation.

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A multi-scale parameter adaptive method for state of charge and parameter estimation of lithium-ion batteries using dual Kalman filters

Cited by 97 publications

References 30 publications

The equivalent circuit battery model parameter sensitivity analysis for lithium‐ion batteries by Monte Carlo simulation

The equivalent circuit battery model parameter sensitivity analysis for lithium‐ion batteries by Monte Carlo simulation

Research on Parameter Self-Learning Unscented Kalman Filtering Algorithm and Its Application in Battery Charge of State Estimation

State of Charge Estimation of a Composite Lithium-Based Battery Model Based on an Improved Extended Kalman Filter Algorithm

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