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Estimation of battery capacity using the enhanced self-organization maps

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Abstract

Accurate knowledge of capacity is crucial for battery management systems as it enables them to predict future failures, specifically through the state of health metric. The primary focus of this study is to estimate battery capacity using the enhanced self-organization maps (EASOM) algorithm. The EASOM is employed here to minimize uncertainties in state of charge estimation and measurements. It identifies the most suitable candidate using an objective function and incorporates a fading memory forgetting factor to improve battery capacity estimation. To validate the precision of the proposed algorithm, six tests were conducted for hybrid electric vehicle and electric vehicle applications. EASOM demonstrated excellent performance, with a maximum error of 1.25%. Additionally, all predictive performance metrics (the mean absolute percentage error, the root-mean-square error, and the mean squared error) were within 1% in all tests.

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Data availability

The datasets generated during and/or analyzed during the current study are available in the “Chapter 4: Algorithms for capacity estimation” repository, http://mocha-java.uccs.edu/BMS2/index.html.

Abbreviations

EASOM:

Enhanced self-organization maps

BMS:

Battery management system

SOH:

State of health

NiCd:

Nickel–cadmium

H∞:

H-infinity filters

ICA:

Incremental capacity analysis

AEKF:

Adaptive EKF

ECM:

Equivalent circuit models

FFRLS:

Forgetting factor recursive least squares

EV:

Electric vehicle

PHEV:

Plug-in hybrid electric vehicle

MKRVM:

Multiple kernel relevance vector machine

DNN:

Deep neural network

DCNN:

Deep convolution neural network

SOC:

State of charge

EKF:

Extended Kalman filter

RMSE:

Root-mean-square error

SRCKF:

Square root cubature Kalman filter

EK:

Extracting knowledge

TLS:

Total least squares

AWTLS:

Approximate weighted total least squares

WTLS:

Weighted total least squares

WLS:

Weighted least squares

DVA:

Differential voltage analysis

EIS:

Electrochemical impedance spectroscopy

LSSVM:

Least square support vector machine

RLU:

Remaining useful life

EM:

Electrochemical models

NN:

Neural network

DEKF:

Dual extended Kalman filter

EMD:

Empirical mode decomposition

PCoE:

Prognostics center of excellence

APE:

Absolute percentage error

MSE:

Mean squared error

MAPE:

Mean absolute percentage error

SOE:

State of energy

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  1. Faculty of Sciences and Technology, Hassan First University, FST of Settat, Km 3, B.P: 577, Road to Casablanca, Settat, Morocco

    Mouncef El marghichi

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The paper conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing—original draft preparation, writing—review and editing, visualization, the supervision, and project administration have been done by the corresponding author (MEM).

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El marghichi, M. Estimation of battery capacity using the enhanced self-organization maps. Electr Eng 106, 1549–1567 (2024). https://doi.org/10.1007/s00202-023-01966-5

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