Enhanced closed loop State of Charge estimator for lithium-ion batteries based on Extended Kalman Filter

Pérez, Gustavo; Garmendia, Maitane; Reynaud, J.F.; Crego, Jon; Viscarret, U.

doi:10.1016/j.apenergy.2015.06.063

Cited by 78 publications

(28 citation statements)

References 44 publications

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“…The estimation procedure employed in the HF method is summarized in Table 3. The EKF method for battery SOC estimation is widely studied, and the process of this algorithm is introduced in Pérez et al, Hu et al, Xiong et al, Chiang et al, and Lee et al [16,[42][43][44][45]; thus, the details are not discussed in the current study. To achieve fairness, the parameters of EKF and STHF are assigned identical values, as listed in Table 5.…”

Section: Estimation Results With White Gaussian Noisesmentioning

confidence: 99%

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Strong Tracking of a H-Infinity Filter in Lithium-Ion Battery State of Charge Estimation

et al. 2018

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Abstract:The accuracy of state-of-charge (SOC) estimation, one of the most important functions of a battery management system (BMS), is the basis for the proper operation of an electric vehicle. This study proposes a method for accurate SOC estimation. To achieve a balance between accuracy and simplicity, a second-order resistor-capacitor equivalent circuit model is applied before the algorithm is deduced, and the parameters of the established model are determined using a fitting technique. Battery state space equations are then described. A strong tracking H-infinity filter (STHF) is proposed based on an H-infinity filter (HF) and a strong tracking filter. By introducing a suboptimal fading factor, the STHF approach can use the relevant information in the estimation residual sequence to update the estimation results. To verify the robustness of this approach, battery test experiments are performed at different temperatures on lithium-ion batteries. Finally, the SOC estimation results obtained using the STHF suggest that the STHF method exhibits high robustness against the measured noises and initial error. For comparison, the estimation results of the commonly used extended Kalman filter (EKF) and HF methods are also displayed. It is suggested that the proposed STHF approach obtains a more accurate SOC estimation.

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Section: Estimation Results With White Gaussian Noisesmentioning

confidence: 99%

“…For simplicity, S k , W k , and V k were set as the identity matrices, and their dimensions were determined using Equation (16). P 0 was determined by the initial error.…”

mentioning

confidence: 99%

Strong Tracking of a H-Infinity Filter in Lithium-Ion Battery State of Charge Estimation

et al. 2018

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“…Extended Kalman filter (EKF) is an improved method based on the traditional Kalman filter that can be used for the estimation of nonlinear state variables [19]. And when two sets of state variables need to be estimated, dual extended Kalman filtering (DEKF) can be used [20].…”

Section: Estimation Principle Of Dekfmentioning

confidence: 99%

“…k H Jacobian matrixes of state variables are shown in equation (18). k * P are the covariance matrixes of the state, which are used to reflect the change of state variables, as shown in equation (19).…”

Section: Estimation Principle Of Dekfmentioning

confidence: 99%

State Estimation of Lithium Batteries for Energy Storage Based on Dual Extended Kalman Filter

Yang

et al. 2020

Mathematical Problems in Engineering

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In general, battery packs are monitored by the battery management system (BMS) to ensure the efficiency and reliability of the energy storage system. SOC and SOH represent the battery's energy and lifetime, respectively. ey are the core aspects of the battery BMS. e traditional method assumes that the SOC is determined by the integral of the current input and output from the battery over time, which is an open-loop-based approach and often accompanies by poor estimation accuracy and the accumulation of sensor errors. e contribution of this work is to establish a new equivalent circuit model based on the lithium battery external characteristic, and the battery parameters are identified by considering the influence of capacity fade, voltage rebound, and internal capacitance-resistance performance. e correlation between the ohmic internal resistance and real capacity is obtained by degradation test. en, the dual extended Kalman filter (DEKF) is used to perform real-time prediction of the lithium battery state. And through the simulation analysis and experiments, the feasibility and precision of the estimation method are well proved.

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“…However, a neural network requires a large amount of training data, while data acquisition and screening can introduce estimation error. The extended Kalman filter (EKF) is a popular approach used to estimate battery SOC [11][12][13][14][15][16]. The underlying principle of EKF is linearizing a system model and filter through a Kalman filter (KF).…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Square Root Unscented Kalman Filter Approach for State of Charge Estimation of Lithium-Ion Batteries

2017

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An accurate state of charge (SOC) estimation is of great importance for the battery management systems of electric vehicles. To improve the accuracy and robustness of SOC estimation, lithium-ion battery SOC is estimated using an adaptive square root unscented Kalman filter (ASRUKF) method. The square roots of the variance matrices of the SOC and noise can be calculated directly by the ASRUKF algorithm, which ensures the symmetry and nonnegative definiteness of the matrices. The process values and measurement noise covariance can be adaptively adjusted, which greatly improves the accuracy, stability, and self-adaptability of the filter. The effectiveness of the proposed method has been verified through experiments under different operating conditions. The obtained results were compared with those of extended Kalman filter (EKF) and unscented Kalman filter (UKF) , which indicates that the ASRUKF method provides better accuracy, robustness and convergence in the estimation of battery SOC for electric vehicles. The proposed method has a mean SOC estimation error of 0.5% and a maximum SOC estimation error of 0.8%. These errors are lower than those of other methods.

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Enhanced closed loop State of Charge estimator for lithium-ion batteries based on Extended Kalman Filter

Cited by 78 publications

References 44 publications

Strong Tracking of a H-Infinity Filter in Lithium-Ion Battery State of Charge Estimation

Strong Tracking of a H-Infinity Filter in Lithium-Ion Battery State of Charge Estimation

State Estimation of Lithium Batteries for Energy Storage Based on Dual Extended Kalman Filter

An Adaptive Square Root Unscented Kalman Filter Approach for State of Charge Estimation of Lithium-Ion Batteries

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