Parameter and State of Charge Estimation Simultaneously for Lithium‐Ion Battery Based on Improved Open Circuit Voltage Estimation Method

Gong, Dongliang; Gao, Ying; Kou, Yalin

doi:10.1002/ente.202100235

Cited by 27 publications

(20 citation statements)

References 51 publications

(64 reference statements)

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“…Equation (8) was found using the Eulers forward approach and can be represented in the discrete-time state-space model.…”

Section: Soc Estimation By Using a Proposed Nsomentioning

confidence: 99%

“…Normally, direct measurement methods are open-loop systems. 8 The open-circuit voltage (OCV) method and impedance measurement methods come under direct measurement. The most famous direct measurement method is the OCV method.…”

Section: Introductionmentioning

confidence: 99%

“…Typically, two basic types of SOC estimation methods exist, named direct measurement and book‐keeping estimation measurement methods. Normally, direct measurement methods are open‐loop systems 8 . The open‐circuit voltage (OCV) method and impedance measurement methods come under direct measurement.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of lithium‐ion battery state of charge for electric vehicles using a nonlinear state observer

Sakile

Sinha

2021

Energy Storage

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The state of charge (SOC) estimation of lithium-ion batteries is complex due to the various nonlinear uncertainties present in the battery. However, in this paper, a new nonlinear state observer (NSO) is proposed to be designed for the estimation of accurate and robust SOC. This proposed observer is suitable for both continuous and discrete-time nonlinear systems. To design the nonlinear observer, two-RC equivalent circuit model state equations are simulated for the dynamic behavior of the lithium-ion battery. The seventh-order polynomial fitting approach is assumed for the nonlinear relationship between opencircuit voltage (OCV) and SOC, and the exponential fitting method is used to estimate the battery's offline parameters. Lyapunov's stability criterion achieves the stability and convergence capability of the proposed method. An urban dynamometer driving schedule (UDDS) cycle was adopted to estimate the performance of the proposed observer by comparing it with the wellestablished methods like unscented Kalman filter (UKF) and sliding mode observer (SMO) algorithms, and it was found that the proposed observer achieved better performance like accurate SOC, high convergence capability, and less SOC error. K E Y W O R D S lithium-ion battery, nonlinear state observer, open-circuit voltage and equivalent circuit model, SOC

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“…Equation (8) was found using the Eulers forward approach and can be represented in the discrete-time state-space model.…”

Section: Soc Estimation By Using a Proposed Nsomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Estimation of lithium‐ion battery state of charge for electric vehicles using a nonlinear state observer

Sakile

Sinha

2021

Energy Storage

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“…At present, the most commonly used battery SOC estimation methods are the amperehour method [1,2], open-circuit voltage method [3][4][5], internal resistance method [6], kalman filter method [7][8][9][10], and some data-driven methods, such as the Gaussian process regression method [11,12] and the support vector machine method [13]. The ampere-hour method is easy to implement, but errors can easily accumulate.…”

Section: Introductionmentioning

confidence: 99%

Battery State of Charge Estimation Based on Composite Multiscale Wavelet Transform

et al. 2022

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The traditional battery state of charge (SOC) estimation method, which is based on neural networks, directly uses terminal voltage and terminal current as the input data. Although it is convenient to implement, it produces a large estimation error when the current and voltage change drastically. To solve this problem, a new method, which uses a composite multiscale wavelet transform, is proposed to estimate the battery SOC. In the proposed method, a wavelet transform is applied to the input data, and this process obtains the approximate coefficients and detail coefficients of the input data at different scales. A neural network then uses these coefficients as inputs to estimate the SOC. The experimental results show that the proposed method can improve the accuracy of the battery SOC estimation without changing the neural network structure or algorithm.

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“…In the presence of noise, the EKF method outperforms the KF algorithm in terms of precision. On the other hand, due to the Jacobian matrixes, the EKF technique Coulomb counting method [7] Simple, easy implementation, and fast computation Applicable only for open-loop systems, and this method is very sensitive 2 Open-circuit voltage method [7,8] Simple, easy implementation Applicable only for open-loop systems and unsuitable for flat OCV-SOC curves 3 Kalman-filter-based method [9][10][11] An accurate and commonly used method in literature, closed-loop system Improved methods are only suitable for linear systems, and complexity is more 4 Extended Kalman filter and unscented Kalman filter methods [12][13][14][15][16][17][18] More accurate, robust, and applicable to the nonlinear systems Complex algorithm and use of the mass of matrix 5 Support vector machine method [19][20][21] Accurate and robust Sensitive to the training data and the complex controller is required 6…”

Section: Introductionmentioning

confidence: 99%

Estimation of State of Charge and State of Health of Lithium‐Ion Batteries Based on a New Adaptive Nonlinear Observer

Sakile

Sinha

2021

Advcd Theory and Sims

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Estimation of accurate state of charge (SOC) and state of health (SOH) of lithium‐ion batteries has become more difficult in electric vehicles due to various uncertainties in the battery. The main objective of this paper is to estimate the accurate and robust SOC and SOH of the lithium‐ion battery. Here, a first‐order resistor‐capacitor (RC) electrical equivalent circuit model is considered for the analysis and modeling, an adaptive nonlinear observer (ANO) is proposed to convert nonlinear equations into linearized equations. The transfer function is obtained from the linearized equations to estimate the actual parameters and the internal states of the battery. When comparing the proposed ANO to the conventional method extended Kalman filter, the new ANO gives better dynamic results, less SOC error, and high convergence capability. An extra random variable noise is added to the system; the proposed model is suitable for the SOC at current noise factors. The convergence capability of the observer is analyzed with the linear criterion. The simulation results of the proposed ANO are validated through the MATLAB/Simulink platform under the hybrid pulse power characteristics test.

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Parameter and State of Charge Estimation Simultaneously for Lithium‐Ion Battery Based on Improved Open Circuit Voltage Estimation Method

Cited by 27 publications

References 51 publications

Estimation of lithium‐ion battery state of charge for electric vehicles using a nonlinear state observer

Estimation of lithium‐ion battery state of charge for electric vehicles using a nonlinear state observer

Battery State of Charge Estimation Based on Composite Multiscale Wavelet Transform

Estimation of State of Charge and State of Health of Lithium‐Ion Batteries Based on a New Adaptive Nonlinear Observer

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