A State of Charge Estimation Method Based on Adaptive Extended Kalman-Particle Filtering for Lithium-ion Batteries

Xia, Bizhong; Guo, Shengkun; Wang, Wei; Lai, Yongzhi; Wang, Huawen; Wang, Mingwang; Zheng, Weiwei

doi:10.3390/en11102755

Cited by 11 publications

(11 citation statements)

References 37 publications

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“…To get an accurate SOC estimation, it is vital to reduce the influences of the system noise from the complex working environment of the EVs [6]. The noise, especially the nonlinear noise, would create huge errors when estimating the SOC.…”

Section: Introductionmentioning

confidence: 99%

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An Improved State of Charge and State of Power Estimation Method Based on Genetic Particle Filter for Lithium-ion Batteries

Liu

Zheng

et al. 2020

Energies

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In this paper, an improved method for estimating the state of charge (SOC) of lithium-ion batteries is proposed, which is developed from the particle filter (PF). An improved genetic particle filter (GPF), owing to the advantages of the PF and genetic algorithm, is proposed to overcome the disadvantage of the traditional particle filter: lacking the diversity of particles. Firstly, the relationship between SOC and open-circuit voltage (OCV) is identified on the low-current OCV test. Secondly, a first-order resistor and capacitance (RC) model is established, then, the least-squares algorithm is used to identify the model parameters via the incremental current test. Thirdly, GPF and the improved GPF (IGPF) are proposed to solve the problems of the PF. The method based on the IGPF is proposed to estimate the state of power (SOP). Finally, IGPF, GPF, and PF are employed to estimate the SOC on the federal urban driving schedule (FUDS). The results show that compared with traditional PF, the errors of the IGPF are 20% lower, and compared with GPF, the maximum error of the IGPF has declined 1.6% SOC. The SOC that is estimated by the IGPF is applied to estimate the SOP for battery, considering the restrictions from the peak SOC, the voltage, and the instruction manual. The result shows that the method based on the IGPF can successfully estimate SOP.

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Section: Introductionmentioning

confidence: 99%

“…In this aspect, many researchers pay their attention to improve the algorithm's accuracy or simplify the calculation process of the algorithm. From the perspective of the algorithm, the Kalman filter and particle filter (PF) are studied a lot [6][7][8][9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

An Improved State of Charge and State of Power Estimation Method Based on Genetic Particle Filter for Lithium-ion Batteries

Liu

Zheng

et al. 2020

Energies

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“…The PF requires a considerable number of initial particles and the calculation capacity is very high. In [19], the dependence of the computational load of the PF was solved using a hybrid method to estimate the SoC of a battery based on an adaptive extended Kalman particle filter (AEKPF). Five experimental tests were carried out to evaluate the performance of the algorithm and showed that the average of the absolute error was less than 1%.…”

Section: Introductionmentioning

confidence: 99%

An Optimized Impedance Model for the Estimation of the State-of-Charge of a Li-Ion Cell: The Case of a LiFePO4 (ANR26650)

et al. 2019

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This article focused on the estimation of the state of charge (SoC) of a Li-con Cell by carrying out a series of experimental tests at various operating temperatures and SoC. The cell was characterized by electrochemical impedance spectroscopy (EIS) tests, from which the impedance frequency spectrum for different SoC and temperatures was obtained. Indeed, the cell model consisted of a modified Randles circuit type that included a constant phase element so-called Warburg impedance. Each circuit parameter was obtained from the EIS tests. The obtained were been used to develop two numerical models for each parameter, i.e., one based on numerical correlations and the other based on the artificial neural network (ANN) method. A genetic algorithm was used to solve and optimize the numerical models. The accuracy of the models was examined and the results showed that the ANN-based model was more accurate than the correlations-based model. The root mean square relative error (RMSRE) of the parameters Rs, R1, C1 and W for the ANN-based model were: 4.63%, 13.65%, 10.96% and 4.4%, respectively, compared to 7.09%, 27.45%, 34.36% and 7.07% for the correlations-based model, respectively. The SoC was estimated using the extended Kalman filter based on a Randles model, with an estimation RMSRE of about 1.19%.

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“…18,19 The ECM belongs to the semimechanism semiempirical model, in which the mathematical circuit expression is used to simulate the battery behavior by using the circuit elements such as voltage source, resistor, capacitor, and inductors. 23 The LIBs often exhibit some resistance-capacitance (RC) characteristics during the charge-discharge process 24 and further consider the hysteresis of open-circuit voltage (OCV) after the charge-discharge time period. 21,22 The electrical modeling method is used to simulate the working process regardless of its internal chemical reaction and therefore has good applicability.…”

mentioning

confidence: 99%

“…21,22 The electrical modeling method is used to simulate the working process regardless of its internal chemical reaction and therefore has good applicability. 23 The LIBs often exhibit some resistance-capacitance (RC) characteristics during the charge-discharge process 24 and further consider the hysteresis of open-circuit voltage (OCV) after the charge-discharge time period. 25 The problem with this equivalent modeling method is that a simple model cannot reflect the dynamic changes of the battery and may lead to incorrect recognition results.…”

mentioning

confidence: 99%

A novel adaptive state of charge estimation method of full life cycling lithium‐ion batteries based on the multiple parameter optimization

Cao

Wang

Fernandez

et al. 2019

Energy Science & Engineering

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The state of charge (SoC) estimation is the safety management basis of the packing lithium‐ion batteries (LIB), and there is no effective solution yet. An improved splice equivalent modeling method is proposed to describe its working characteristics by using the state‐space description, in which the optimization strategy of the circuit structure is studied by using the aspects of equivalent mode, analog calculation, and component distribution adjustment, revealing the mathematical expression mechanism of different structural characteristics. A novel particle adaptive unscented Kalman filtering algorithm is introduced for the iterative calculation to explore the working state characterization mechanism of the packing LIB, in which the incorporate multiple information is considered and applied. The adaptive regulation is obtained by exploring the feature extraction and optimal representation, according to which the accurate SoC estimation model is constructed. The state of balance evaluation theory is explored, and the multiparameter correction strategy is carried out along with the experimental working characteristic analysis under complex conditions, according to which the optimization method is obtained for the SoC estimation model structure. When the remaining energy varies from 10% to 100%, the tracking voltage error is <0.035 V and the SoC estimation accuracy is 98.56%. The adaptive working state estimation is realized accurately, which lays a key breakthrough foundation for the safety management of the LIB packs.

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A State of Charge Estimation Method Based on Adaptive Extended Kalman-Particle Filtering for Lithium-ion Batteries

Cited by 11 publications

References 37 publications

An Improved State of Charge and State of Power Estimation Method Based on Genetic Particle Filter for Lithium-ion Batteries

An Improved State of Charge and State of Power Estimation Method Based on Genetic Particle Filter for Lithium-ion Batteries

An Optimized Impedance Model for the Estimation of the State-of-Charge of a Li-Ion Cell: The Case of a LiFePO4 (ANR26650)

A novel adaptive state of charge estimation method of full life cycling lithium‐ion batteries based on the multiple parameter optimization

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