Online Parameter Estimation of a Lithium-Ion Battery based on Sunflower Optimization Algorithm

Elmarghichi, Mouncef; Bouzi, Mostafa; Ettalabi, Naoufl

doi:10.1109/gpecom49333.2020.9247936

Cited by 7 publications

(4 citation statements)

References 21 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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“…The sunflower optimization algorithm is considered as a new optimization algorithm [21][22][23][24], it is a population-based algorithm suggested in [24]. SFO mimics the sunflowers motion toward the sunlight by considering the pollination between adjacent sunflowers, if the distance between sunflowers and sun increases, the radiation intensity will decrease and vice versa according to following:…”

Section: Sunflower Optimization Algorithmmentioning

confidence: 99%

Online parameter estimation of a lithium-ion battery based on sunflower optimization algorithm

Elmarghichi¹,

Bouzi²,

Ettalabi³

2021

Bulletin EEI

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For techniques used to estimate battery state of charge (SOC) based on equivalent electric circuit models (ECMs), the battery equivalent model parameters are affected by factors such as SOC, temperature, battery aging, leading to SOC estimation error. Therefore, it is necessary to accurately identify these parameters. Updating battery model parameters constantly also known as online parameter identification can effectively solve this issue. In this paper, we propose a novel strategy based on the sunflower optimization algorithm (SFO) to identify battery model parameters and predict the output voltage in real-time. The identification accuracy has been confirmed using empirical data obtained from CALCE battery group (the center for advanced life cycle engineering) performed on the Samsung (INR 18650 20R) battery cell under one electric vehicle (EV) cycle protocol named dynamic stress test. Comparative analysis of SFO and AFRRLS (adaptive forgetting factor of recursive least squares) is carried out to prove the efficiency of the proposed algorithm. Results show that the calibrated model using SFO has superiority compared with AFFRLS algorithm to simulate the dynamic voltage behavior of a lithium-ion battery in EV application.

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Online Parameter Estimation of a Lithium-Ion Battery based on Sunflower Optimization Algorithm

Cited by 7 publications

References 21 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

Online parameter estimation of a lithium-ion battery based on sunflower optimization algorithm

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