State of charge estimation of LiFePO4 batteries based on online parameter identification

Zhang, Jinlong; Wei, Yanjun; Qi, Hanhong

doi:10.1016/j.apm.2016.01.047

Cited by 31 publications

(11 citation statements)

References 17 publications

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“…The modelling parameters such as resistance and capacitance change with various factors such as C‐rate, SOC, temperature, and ageing. Ordinary recursive least square (ORLS) is being used extensively by many researchers for the purpose of BECM parameter identification [35]. To improve the identification error of ORLS, its variants have been used recently for battery parameter identification such as RLS with forgetting factor (RLSF) [7, 15], weighted RLS [36], recursive extended least square (RELS) [37], moving window RLS [38] etc.…”

Section: Battery Modellingmentioning

confidence: 99%

Strict Lyapunov super twisting observer design for state of charge prediction of lithium‐ion batteries

Sethia

Majhi

Nayak

et al. 2021

IET Renewable Power Gen

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The effective implementation of battery management system (BMS) in various applications such as electric vehicles (EVs), renewable energy sources (RES) integrated smart‐grids and micro‐grids, necessitates accurate estimation of the battery parameters and states. This paper primarily focuses on offering an improved solution to the state of charge (SOC) estimation problem of lithium‐ion (Li‐ion) batteries. After extensive analysis of the current state‐of‐the‐art methods, a new strict Lyapunov super twisting algorithm (SLSTA) based approach is proposed for precise estimation of SOC under a comprehensive range of uncertainties. The error convergence and robustness of the proposed state observer are demonstrated using Lyapunov stability theory. Since the modelling parameters of the battery equivalent circuit utilised in this paper vary with various operational and external factors, a standard online method is employed for their real‐time identification. The presented method is executed on a lithium‐polymer (LiPo) battery with the help of a dynamic stress test (DST). The experimental results demonstrate that the proposed approach outperforms the well‐known approaches in terms of accuracy, computational complexity, and convergence time.

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Section: Battery Modellingmentioning

confidence: 99%

Strict Lyapunov super twisting observer design for state of charge prediction of lithium‐ion batteries

Sethia

Majhi

Nayak

et al. 2021

IET Renewable Power Gen

View full text Add to dashboard Cite

show abstract

“…The process of these approaches can be divided into three procedures: model building, identification of model parameters, and SOC estimation [6]. Many battery models have been reported to date, e.g., the electrochemical model [16], equivalent circuit models (ECMs) [17,18], and neural network models [19][20][21][22]. The electrochemical model is rather complex and requires a large number of parameters for SOC estimation [16].…”

Section: Introductionmentioning

confidence: 99%

“…Many battery models have been reported to date, e.g., the electrochemical model [16], equivalent circuit models (ECMs) [17,18], and neural network models [19][20][21][22]. The electrochemical model is rather complex and requires a large number of parameters for SOC estimation [16]. The neural network models [19] require vast training data of all driving conditions for accurate estimation.…”

Section: Introductionmentioning

confidence: 99%

Online Parameters Identification and State of Charge Estimation for Lithium-Ion Battery Using Adaptive Cubature Kalman Filter

Luo

Tan

et al. 2021

WEVJ

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The state of charge (SOC) of a lithium-ion battery plays a key role in ensuring the charge and discharge energy control strategy, and SOC estimation is the core part of the battery management system for safe and efficient driving of electric vehicles. In this paper, a model-based SOC estimation strategy based on the Adaptive Cubature Kalman filter (ACKF) is studied for lithium-ion batteries. In the present study, the dual polarization (DP) model is employed for SOC estimation and the vector forgetting factor recursive least squares (VRLS) method is utilized for model parameter online identification. The ACKF is then designed to estimate the battery’s SOC. Finally, the Urban Dynamometer Driving Schedule and Dynamic Stress Test are utilized to evaluate the performance of the proposed method by comparing with results obtained using the extended Kalman filter (EKF) and the cubature Kalman filter (CKF) algorithms. The simulation and experimental results show that the proposed ACKF algorithm combined with VRLS-based model identification is a promising SOC estimation approach. The proposed algorithm is found to provide more accurate SOC estimation with satisfying stability than the extended EKF and CKF algorithms.

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“…e inconsistency of the cell leads to more workload on modelling if accurate model of each cell in the battery pack is needed [28,29]. erefore, updating the parameters online is necessary for modelling accuracy and states estimation [30,31]. Recursive Least Squares (RLS) and Kalman filter are generally applied to identify the parameters of ECM [32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Online Parameter Identification of the Lithium-Ion Battery with Refined Instrumental Variable Estimation

Meng

Peng

et al. 2020

Complexity

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Refined Instrumental Variable (RIV) estimation is applied to online identify the parameters of the Equivalent Circuit Model (ECM) for Lithium-ion (Li-ion) battery in this paper, which enables accurate parameters estimation with the measurement noise. Since the traditional Recursive Least Squares (RLS) estimation is extremely sensitive to the noise, the parameters in the ECM may fail to converge to their true values under the measurement noise. The RIV estimation is implemented in a bootstrap form, which alternates between the estimation in the system model and the noise model. The Box-Jenkins model of the Li-ion battery transformed from the two RC ECM is selected as the transfer function model for the RIV estimation in this paper. The errors of the two RC ECM are independently generated by the residual of high-order Auto Regressive (AR) model estimation. With the benefit of a series of auxiliary models, the data filtering technology can prefilter the measurement and increase the robustness of the parameters against the noise. Reasonable parameters are possible to be obtained regardless of the noise in the measurement by RIV. Simulation and experimental tests on a LiFePO4 battery validate the efficiency of RIV for parameter online identification compared with traditional RLS.

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State of charge estimation of LiFePO4 batteries based on online parameter identification

Cited by 31 publications

References 17 publications

Strict Lyapunov super twisting observer design for state of charge prediction of lithium‐ion batteries

Strict Lyapunov super twisting observer design for state of charge prediction of lithium‐ion batteries

Online Parameters Identification and State of Charge Estimation for Lithium-Ion Battery Using Adaptive Cubature Kalman Filter

Online Parameter Identification of the Lithium-Ion Battery with Refined Instrumental Variable Estimation

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