Disturbance observer-based state-of-charge estimation for Li-ion battery used in light electric vehicles

Messier, Pascal; Nguyễn, Bảo-Huy; LeBel, Felix-A.; Trovão, João P.

doi:10.1016/j.est.2019.101144

Cited by 32 publications

(11 citation statements)

References 33 publications

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“…1 b ). The variation of the impedances as a function of SOC and temperature are explained in [21, 22]. The impedance evolutions over R s and three parallel RC circuits are depicted in Fig.…”

Section: Ev System Model In Emrmentioning

confidence: 99%

Effect of battery voltage variation on electric vehicle performance driven by induction machine with optimal flux‐weakening strategy

Nguyen

Nguyễn

Trovão

et al. 2020

IET Electrical Systems in Transportation

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Section: Ev System Model In Emrmentioning

confidence: 99%

Effect of battery voltage variation on electric vehicle performance driven by induction machine with optimal flux‐weakening strategy

Nguyen

Nguyễn

Trovão

et al. 2020

IET Electrical Systems in Transportation

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“…Time consumption is very high, complicated due to the mass of matrix operation, and requires more sample data. [19] Therefore, these methods are not suitable for real-time computation. The nonlinear-based particle filter (PF) was used in ref.…”

Section: Introductionmentioning

confidence: 99%

Lithium‐Ion Battery State of Charge Estimation Using a New Extended Nonlinear State Observer

Sakile

Sinha

2022

Advcd Theory and Sims

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In this paper, an extended nonlinear state observer (ENSO) is proposed to estimate lithium-ion batteries' accurate state of charge (SOC). The two RC equivalent circuit model is used to describe the dynamic behavior of the lithium-ion battery. Based on the circuit state equations, the proposed ENSO is designed to estimate SOC. The ninth-order polynomial fitting curve shows a nonlinear relationship between open-circuit voltage and SOC. Furthermore, the proposed ENSO's stability and convergence rate are guaranteed by Lyapunov's stability analysis. The performance of the proposed observer is compared to that of conventional techniques such as the unscented Kalman filter and sliding mode observer. The proposed method has given better dynamic performance with accurate SOC, reducing computational cost and enhancing the convergence capability compared to conventional methods. The proposed model's effectiveness is validated through the MATLAB/Simulink platform.

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“…A neural network-based SOC observer is proposed in [20] with the convergence proved utilizing Lyapunov stability analysis. A disturbance observer-based strategy is designed in [21] to estimate the SOC, which can reduce the computational burden without reducing the estimation accuracy.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Square-Root Unscented Kalman Filter-Based State-of-Charge Estimation for Lithium-Ion Batteries with Model Parameter Online Identification

Ouyang

et al. 2020

Energies

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The state-of-charge (SOC) is a fundamental indicator representing the remaining capacity of lithium-ion batteries, which plays an important role in the battery’s optimized operation. In this paper, the model-based SOC estimation strategy is studied for batteries. However, the battery’s model parameters need to be extracted through cumbersome prior experiments. To remedy such deficiency, a recursive least squares (RLS) algorithm is utilized for model parameter online identification, and an adaptive square-root unscented Kalman filter (SRUKF) is designed to estimate the battery’s SOC. As demonstrated in extensive experimental results, the designed adaptive SRUKF combined with RLS-based model identification is a promising SOC estimation approach. Compared with other commonly used Kalman filter-based methods, the proposed algorithm has higher precision in the SOC estimation.

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Disturbance observer-based state-of-charge estimation for Li-ion battery used in light electric vehicles

Cited by 32 publications

References 33 publications

Effect of battery voltage variation on electric vehicle performance driven by induction machine with optimal flux‐weakening strategy

Effect of battery voltage variation on electric vehicle performance driven by induction machine with optimal flux‐weakening strategy

Lithium‐Ion Battery State of Charge Estimation Using a New Extended Nonlinear State Observer

Adaptive Square-Root Unscented Kalman Filter-Based State-of-Charge Estimation for Lithium-Ion Batteries with Model Parameter Online Identification

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