State-of-charge estimation for lithium-ion battery using the Gauss-Hermite particle filter technique

Li, Bin; Peng, Kai; Li, Guidan

doi:10.1063/1.5020028

Cited by 19 publications

(11 citation statements)

References 16 publications

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“…Furthermore, the forgetting factor RLS method was also used to determine the battery parameters with the PF as a SOC estimator [120]. Different variants of the PF have also been reported [121,122]. Recently, Ye et al [123] proposed an online double scale and adaptive particle filter.…”

Section: Sigma Point Kalman Filter (Spkf)mentioning

confidence: 99%

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Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

et al. 2019

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Energy storage system (ESS) technology is still the logjam for the electric vehicle (EV) industry. Lithium-ion (Li-ion) batteries have attracted considerable attention in the EV industry owing to their high energy density, lifespan, nominal voltage, power density, and cost. In EVs, a smart battery management system (BMS) is one of the essential components; it not only measures the states of battery accurately, but also ensures safe operation and prolongs the battery life. The accurate estimation of the state of charge (SOC) of a Li-ion battery is a very challenging task because the Li-ion battery is a highly time variant, non-linear, and complex electrochemical system. This paper explains the workings of a Li-ion battery, provides the main features of a smart BMS, and comprehensively reviews its SOC estimation methods. These SOC estimation methods have been classified into four main categories depending on their nature. A critical explanation, including their merits, limitations, and their estimation errors from other studies, is provided. Some recommendations depending on the development of technology are suggested to improve the online estimation.

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Section: Sigma Point Kalman Filter (Spkf)mentioning

confidence: 99%

“…Gao et al 2011 [114] Unspecified Schwunk et al 2013 [115] Unspecified He et al 2013 [117] ≤± 3.5% Burgos-Mellado et al 2016 [118] Unspecified Zhou et al 2016 [119] ME ≤ ± 1.61% Xia et al 2017 [121] ≤± 0.5% Li et al 2018 [122] ≤± 1% Du et al 2018 [120] ME ≤ ± 3.5% Ye et al 2018 [123] ≤± 1%…”

Section: Reference Mae (%)mentioning

confidence: 99%

Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

et al. 2019

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“…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%

“…Compared with the improved Kalman filter that applies Kalman gain to overcome the disadvantage of the Kalman filter, PF has been widely used to process the non-Gaussian random systems because of its ability to deal with nonlinear problems [10]. However, some particles would degenerate when the calculation is continuing, leading to a loss of diversity for the particles.…”

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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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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“…53 The multitimescale observer was designed for the SoC estimation of the LIBs, in which the design parameter sets could be tuned in different timescales. 54 The accurate power allocation strategy was studied for the SoC balancing treatment of the distributed DC microgrid, 55 which was also achieved by a dual EKF algorithm and the charging voltage curve. 56 Through the estimating result, it can be further estimated on how long the battery is going to last for even a dynamic power demand.…”

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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State-of-charge estimation for lithium-ion battery using the Gauss-Hermite particle filter technique

Cited by 19 publications

References 16 publications

Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

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

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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