Unscented Kalman Filter-Based Battery SOC Estimation and Peak Power Prediction Method for Power Distribution of Hybrid Electric Vehicles

Wang, Weida; Wang, Xiantao; Chen, Xiang; Wei, Chao; Zhao, Yongsheng

doi:10.1109/access.2018.2850743

Cited by 80 publications

(33 citation statements)

References 21 publications

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“…First, the general form of the state and measurement Equation of the UKF algorithm is given as follows [12]:…”

Section: The Parameter Self-learning Unscented Kalman Filter Algorithmmentioning

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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“…First, the general form of the state and measurement Equation of the UKF algorithm is given as follows [12]:…”

Section: The Parameter Self-learning Unscented Kalman Filter Algorithmmentioning

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) We perform UT transformation on the point set generated by (13) and obtain the set of particles at time t [9,15,17]. The equations are as follows:…”

Section: Power Battery Soc Estimation Algorithmmentioning

confidence: 99%

“…The neural network method is easy to build an intelligent model, but it requires a large amount of experimental data to train the neural network model, and the amount of calculation is large. In order to overcome the effects of cumulative errors and excessive computational problems, a series of the filter method based on the battery model have been proposed [14][15][16][17][18]. Piller et al [8] proposed the Kalman filter (KF) method for SOC estimation, which has closed-loop correction structure.…”

Section: Introductionmentioning

confidence: 99%

“…However, the KF is a linear filter; the battery model is a nonlinear model; it will produce a large error. In order to get better results, scholars proposed a lot of methods based on the extended Kalman filter (EKF) [10,[13][14][15]26] and the unscented Kalman filter (UKF) [16][17][18]24]. Wang et al [26] proposed a dual extended Kalman filter method for the SOC estimation.…”

Section: Introductionmentioning

confidence: 99%

“…Plett [14,15,18] proposed EKF and UKF for SOC and battery model parameter estimation. Wang [17] proposed an improved unscented Kalman filter for the SOC estimation. However, the accuracy of EKF and UKF both are not sufficiently enough to be used in engineering applications.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Unscented Particle Filter for SOC Estimation Algorithm Based on a Dynamic Parameter Identification

Liu

2019

Mathematical Problems in Engineering

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In order to solve the problem that the model-based State of Charge (SOC) estimation method is too dependent on the model parameters in the SOC estimation of electric vehicles, an improved genetic algorithm is proposed in this paper. The method has the advantages of being able to quickly determine the search range, reducing the probability of falling into local optimum, and having high recognition accuracy. Then we can realize online dynamic identification of power battery model parameters and improve the accuracy of model parameter identification. In addition, considering the complex application environment and operating conditions of electric vehicles, an SOC estimation method based on improved genetic algorithm and unscented particle filter (improved GA-UPF) is proposed. And we compare the improved GA-UPF algorithm with the least square unscented particle filter (LS-UPF) and improved GA unscented Kalman filter (improved GA-UKF) algorithm. The comparison results show that the improved GA-UPF algorithm proposed in this paper has higher estimation accuracy and better stability. It also reflects the practicability and accuracy of the improved GA parameter identification algorithm proposed in this paper.

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State of charge estimation for the vanadium redox flow battery based on the Sage–Husa adaptive extended Kalman filter

Zixuan

Fan

et al. 2023

Circuit Theory & Apps

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SummaryFocusing on improving the accuracy of vanadium redox flow battery (VRFB) state of charge (SOC) estimation, this paper will combine the extended Kalman filter (EKF) estimator with the Sage–Husa adaptive method, referred to as the SAEKF estimator. Firstly, a second‐order Equivalent Circuit Model (ECM) is established to provide the correct parameterization and degrees of freedom. Next, under a hybrid pulse power characterization (HPPC) text, the parameter estimator in MATLAB/Simulink is utilized to identify the ECM parameters by using nonlinear least squares method with Trust‐Region‐Reflective (TRR) iterative algorithm. Then, during the HPPC test, the estimators implemented on the ARM‐Cortex STM32F103 estimate the VRFB SOC under both the 0°C and 5°C conditions. Finally, the mean absolute error (MAE) of the IEKF estimator are 4.66% at the 0°C and 0.895% at the 5°C. In the meantime, the MAE of the SAEKF estimator are 4.15% at the 0°C and 0.703% at the 5°C. The evaluation factors of the 0°C and 5°C experiments in the SAEKF estimator are smaller than those of the IEKF and EKF estimators, indicating that the SAEKF estimator outperforms the IEKF estimator in VRFB SOC estimation.

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Unscented Kalman Filter-Based Battery SOC Estimation and Peak Power Prediction Method for Power Distribution of Hybrid Electric Vehicles

Cited by 80 publications

References 21 publications

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

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

Unscented Particle Filter for SOC Estimation Algorithm Based on a Dynamic Parameter Identification

State of charge estimation for the vanadium redox flow battery based on the Sage–Husa adaptive extended Kalman filter

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