Online Parameter Estimation of an Electric Vehicle Lithium-Ion Battery Using AFFRLS

Elmarghichi, Mouncef; Bouzi, Mostafa; Ettalabi, Naoufl

doi:10.1109/icecocs50124.2020.9314577

Cited by 6 publications

(2 citation statements)

References 18 publications

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“…The estimated capacity (C es ) is computed by incorporating a forgetting factor ( γ) Eq. ( 19), typically ranging from 0.9 to 1 [41][42][43][44], which facilitates rapid convergence of the NNA algorithm towards the true capacity value.…”

Section: Proposed Algorithmmentioning

confidence: 99%

Enhancing Battery Capacity Estimation Accuracy through the Neural Network Algorithm

El Marghichi,

Hilali,

Loulijat

2024

Period. Polytech. Elec. Eng. Comp. Sci.

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Accurate estimation of battery metrics, such as state of health (SOH), is crucial for effective battery management systems (BMS) due to capacity degradation over time. This paper proposes a methodology to enhance battery capacity estimation accuracy by addressing uncertainties related to state of charge (SOC) estimation and measurement. The methodology employs the Neural Network Algorithm (NNA), an optimization algorithm inspired by artificial neural networks (ANNs). The NNA generates an initial population of pattern solutions and iteratively updates them using a weight matrix, bias operator, and transfer function operator. By combining the advantages of ANNs and optimization techniques, the NNA aims to find an optimal solution considering interdependent variables and incorporating global and local feedbacks. Leveraging the capabilities of the NNA, our objective is to identify the candidate that minimizes a specified cost function, ensuring up-to-date cell capacity through a memory forgetting factor. The algorithm's precision was validated using NASA's Prognostic Data, demonstrating outstanding performance by surpassing two aggressive algorithms in terms of accuracy. In the most severe case scenario, the algorithm achieved a peak error of less than 0.4%. Furthermore, the algorithm consistently demonstrated predictive performance measures that were superior to those of the compared algorithms.

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Section: Proposed Algorithmmentioning

confidence: 99%

Enhancing Battery Capacity Estimation Accuracy through the Neural Network Algorithm

El Marghichi,

Hilali,

Loulijat

2024

Period. Polytech. Elec. Eng. Comp. Sci.

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“…With each subsequent x j and y j value, GBO proceeds to locate the highest candidate (SOH hat ) in an effort to keep Eq (5) (the loss function) to a minimum. We embedded a forgetting factor γ in Eq (15) (typically ranging from 0.9 to 1 [40][41][42][43][44]) to compute the SOH est (estimated health state), γ makes sure that GBO converges to the true value rapidly.…”

Section: Plos Onementioning

confidence: 99%

Improving accuracy in state of health estimation for lithium batteries using gradient-based optimization: Case study in electric vehicle applications

El Marghichi,

Dangoury,

zahrou

et al. 2023

PLoS ONE

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Significant improvements in battery performance, cost reduction, and energy density have been made since the advancements of lithium-ion batteries. These advancements have accelerated the development of electric vehicles (EVs). The safety and effectiveness of EVs depend on accurate measurement and prediction of the state of health (SOH) of lithium-ion batteries; however, this process is uncertain. In this study, our primary goal is to enhance the accuracy of SOH estimation by reducing uncertainties in state of charge (SOC) estimation and measurements. To achieve this, we propose a novel method that utilizes the gradient-based optimizer (GBO) to evaluate the SOH of lithium batteries. The GBO minimizes a cost with the aim of selecting the optimal candidate for updating the SOH through a memory-fading forgetting factor. We evaluated our method against four robust algorithms, namely particle swarm optimization-least square support vector regression (PSO-LSSV), BCRLS-multiple weighted dual extended Kalman filtering (BCRLS-MWDEKF), Total least square (TLS), and approximate weighted total least squares (AWTLS) in hybrid electric vehicle (HEV) and electric vehicle (EV) applications. Our method consistently outperformed the alternatives, with the GBO achieving the lowest maximum error. In EV scenarios, GBO exhibited maximum errors ranging from 0.65% to 1.57% and mean errors ranging from 0.21% to 0.57%. Similarly, in HEV scenarios, GBO demonstrated maximum errors ranging from 0.81% to 3.21% and mean errors ranging from 0.39% to 1.03%. Furthermore, our method showcased superior predictive performance, with low values for mean squared error (MSE) (<1.8130e-04), root mean squared error (RMSE) (<1.35%), and mean absolute percentage error (MAPE) (<1.4).

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Estimation of battery capacity using the enhanced self-organization maps

Marghichi

2023

Electr Eng

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Online Parameter Estimation of an Electric Vehicle Lithium-Ion Battery Using AFFRLS

Cited by 6 publications

References 18 publications

Enhancing Battery Capacity Estimation Accuracy through the Neural Network Algorithm

Enhancing Battery Capacity Estimation Accuracy through the Neural Network Algorithm

Improving accuracy in state of health estimation for lithium batteries using gradient-based optimization: Case study in electric vehicle applications

Estimation of battery capacity using the enhanced self-organization maps

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