SOC estimation for LiFePO&lt;inf&gt;4&lt;/inf&gt; battery in EVs using recursive least-squares with multiple adaptive forgetting factors

Duong, Van-Huan; Bastawrous, Hany Ayad; Lim, KaiChin; See, Khay Wai; Zhang, P.; Dou, S. X.

doi:10.1109/iccve.2014.7297603

Cited by 6 publications

(3 citation statements)

References 3 publications

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“…A large number of algorithms, including genetic algorithms (GA) [37], least squares (LA) [38], and hybrid pulse power characteristic (HPPC) test [39], have been used in recent years for parameter identification of evenin models.…”

Section: Model Parameter Identificationmentioning

confidence: 99%

Hybrid Methods Using Neural Network and Kalman Filter for the State of Charge Estimation of Lithium-Ion Battery

Cui

Dai²,

Sun³

et al. 2022

Mathematical Problems in Engineering

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With the increasing carbon emissions worldwide, lithium-ion batteries have become the main component of energy storage systems for clean energy due to their unique advantages. Accurate and reliable state-of-charge (SOC) estimation is a central factor in the widespread use of lithium-ion batteries. This review, therefore, examines the recent literature on estimating the SOC of lithium-ion batteries using the hybrid methods of neural networks combined with Kalman filtering (NN-KF), classifying the methods into Kalman filter-first and neural network-first methods. Then the hybrid methods are studied and discussed in terms of battery model, parameter identification, algorithm structure, implementation process, appropriate environment, advantages, disadvantages, and estimation errors. In addition, this review also gives corresponding recommendations for researchers in the battery field considering the existing problems.

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Section: Model Parameter Identificationmentioning

confidence: 99%

Hybrid Methods Using Neural Network and Kalman Filter for the State of Charge Estimation of Lithium-Ion Battery

Cui

Dai²,

Sun³

et al. 2022

Mathematical Problems in Engineering

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show abstract

“…The parameters estimated are updated by including new information from the next sampling time [52]. The so-called forgetting factor may not provide accurate estimations of all battery model parameters, therefore, a multiple adaptive forgetting factors technique could be used to increase the accuracy [51,52].…”

Section: Recursive Least Square (Rls)-based Estimationmentioning

confidence: 99%

Archives of Electrical Engineering

Hallmann,

Wenge,

Komarnicki

et al. 2022

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The use of lithium-ion battery energy storage (BES) has grown rapidly during the past year for both mobile and stationary applications. For mobile applications, BES units are used in the range of 10-120 kWh. Power grid applications of BES are characterized by much higher capacities (range of MWh) and this area particularly has great potential regarding the expected energy system transition in the next years. The optimal operation of BES by an energy storage management system is usually predictive and based strongly on the knowledge about the state of charge (SOC) of the battery. The SOC depends on many factors (e.g. material, electrical and thermal state of the battery), so that an accurate assessment of the battery SOC is complex. The SOC intermediate prediction methods are based on the battery models. The modeling of BES is divided into three types: fundamental (based on material issues), electrical equivalent circuit (based on electrical modeling) and balancing (based on a reservoir model). Each of these models requires parameterization based on measurements of input/output parameters. These models are used for SOC modelbased calculation and in battery system simulation for optimal battery sizing and planning. Empirical SOC assessment methods currently remain the most popular because they allow practical application, but the accuracy of the assessment, which is the key factor for optimal operation, must also be strongly considered. This scientific contribution is divided into two papers. Paper part I will present a holistic overview of the main methods of SOC assessment. Physical measurement methods, battery modeling and the methodology of using the model as a digital twin of a battery are addressed and discussed. Furthermore, adaptive methods and methods of artificial intelligence, which are important for the SOC calculation, are presented. In paper part II, examples of the application areas are presented and their accuracy is discussed.

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“…In this paper, we employ the online adaptive algorithm, MAFF-RLS, used in Ref. [36] which is a combined one of adaptive forgetting factor in Ref. [30] and MFFs in Ref.…”

Section: Multiple Adaptive Forgetting Factors Rls Estimationmentioning

confidence: 99%

Online state of charge and model parameters estimation of the LiFePO4 battery in electric vehicles using multiple adaptive forgetting factors recursive least-squares

Duong

Bastawrous

Lim

et al. 2015

Journal of Power Sources

158

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SOC estimation for LiFePO<inf>4</inf> battery in EVs using recursive least-squares with multiple adaptive forgetting factors

Cited by 6 publications

References 3 publications

Hybrid Methods Using Neural Network and Kalman Filter for the State of Charge Estimation of Lithium-Ion Battery

Hybrid Methods Using Neural Network and Kalman Filter for the State of Charge Estimation of Lithium-Ion Battery

Archives of Electrical Engineering

Online state of charge and model parameters estimation of the LiFePO4 battery in electric vehicles using multiple adaptive forgetting factors recursive least-squares

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