State of Charge Estimation Model Based on Genetic Algorithms and Multivariate Linear Regression with Applications in Electric Vehicles

Manríquez-Padilla, Carlos G.; Cueva-Perez, Isaias; Domínguez-González, Aurelio; Elvira-Ortiz, David A.; Pérez-Cruz, Ángel; Saucedo-Dorantes, Juan José

doi:10.3390/s23062924

Cited by 10 publications

(2 citation statements)

References 30 publications

(34 reference statements)

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“…It is imperative to acknowledge that, although linear regression is a clear and understandable methodology, the association between SOC and influential parameters within a battery system may not consistently adhere to a strictly linear pattern. In instances where the relationship exhibits non-linearity, more sophisticated machine learning methodologies, including but not limited to dual linear regression, random forest regression, support vector machines, and deep learning techniques such as ANNs, could be contemplated to effectively capture the intricate non-linear associations present in the dataset [59][60][61][62][63][64][65].…”

Section: Linear Regression (Lr) Modelsmentioning

confidence: 99%

Comparative Study-Based Data-Driven Models for Lithium-Ion Battery State-of-Charge Estimation

Hussein,

Esoofally,

Donekal

et al. 2024

Batteries

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Batteries have been considered a key element in several applications, ranging from grid-scale storage systems through electric vehicles to daily-use small-scale electronic devices. However, excessive charging and discharging will impair their capabilities and could cause their applications to fail catastrophically. Among several diagnostic indices, state-of-charge estimation is essential for evaluating a battery’s capabilities. Various approaches have been introduced to reach this target, including white, gray, and black box or data-driven battery models. The main objective of this work is to provide an extensive comparison of currently highly utilized machine learning-based estimation techniques. The paper thoroughly investigates these models’ architectures, computational burdens, advantages, drawbacks, and robustness validation. The evaluation’s main criteria were based on measurements recorded under various operating conditions at the Energy Systems Research Laboratory (ESRL) at FIU for the eFlex 52.8 V/5.4 kWh lithium iron phosphate battery pack. The primary outcome of this research is that, while the random forest regression (RFR) model emerges as the most effective tool for SoC estimation in lithium-ion batteries, there is potential to enhance the performance of simpler models through strategic adjustments and optimizations. Additionally, the choice of model ultimately depends on the specific requirements of the task at hand, balancing the need for accuracy with the complexity and computational resources available and how it can be merged with other SoC estimation approaches to achieve high precision.

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Section: Linear Regression (Lr) Modelsmentioning

confidence: 99%

Comparative Study-Based Data-Driven Models for Lithium-Ion Battery State-of-Charge Estimation

Hussein,

Esoofally,

Donekal

et al. 2024

Batteries

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show abstract

“…The model has high accuracy, but the corresponding computational workload is also higher. The corresponding methods currently include a recursive least squares algorithm [68], an extended Kalman filtering algorithm [69], a particle swarm optimization algorithm [70], a genetic algorithm [71], etc.…”

Section: Model Parameters' Identificationmentioning

confidence: 99%

Advances in the Study of Techniques to Determine the Lithium-Ion Battery’s State of Charge

Liu,

Gao,

Marma

et al. 2024

Energies

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This study explores the challenges and advances in the estimation of the state of charge (SOC) of lithium-ion batteries (LIBs), which are crucial to optimizing their performance and lifespan. This review focuses on four main techniques of SOC estimation: experimental measurement, modeling approach, data-driven approach, and joint estimation approach, highlighting the limitations and potential inaccuracies of each method. This study suggests a combined approach, incorporating correction parameters and closed-loop feedback, to improve measurement accuracy. It introduces a multi-physics model that considers temperature, charging rate, and aging effects and proposes the integration of models and algorithms for optimal estimation of SOC. This research emphasizes the importance of considering temperature and aging factors in data-driven approaches. It suggests that the fusion of different methods could lead to more accurate SOC predictions, an important area for future research.

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Predictive machine learning in optimizing the performance of electric vehicle batteries: Techniques, challenges, and solutions

Naresh,

Ratnakara Rao,

Prabhakar

2024

WIREs Data Min & Knowl

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This research paper explores the importance of optimizing the performance of electric vehicle (EV) batteries to align with the rapid growth in EV usage. It uses predictive machine learning (ML) techniques to achieve this optimization. The paper covers various ML methods like supervised, unsupervised, and deep learning (DL) and ways to measure their effectiveness. Significant battery performance factors, such as state of charge (SoC), state of health (SoH), state of function (SoF), and remaining useful life (RUL), are discussed, along with methods to collect and prepare data for accurate predictions. The paper introduces an operation research model for optimizing the performance of EV Batteries. It also looks at challenges unique to battery systems and ways to overcome them. The study showcases ML models' ability to predict battery behavior for real‐time monitoring, efficient energy use, and proactive maintenance. The paper categorizes different applications and case studies, providing valuable insights and forward‐looking perspectives for researchers, practitioners, and policymakers involved in improving EV battery performance through predictive ML.This article is categorized under: Technologies > Classification Fundamental Concepts of Data and Knowledge > Explainable AI Technologies > Machine Learning

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State of Charge Estimation Model Based on Genetic Algorithms and Multivariate Linear Regression with Applications in Electric Vehicles

Cited by 10 publications

References 30 publications

Comparative Study-Based Data-Driven Models for Lithium-Ion Battery State-of-Charge Estimation

Comparative Study-Based Data-Driven Models for Lithium-Ion Battery State-of-Charge Estimation

Advances in the Study of Techniques to Determine the Lithium-Ion Battery’s State of Charge

Predictive machine learning in optimizing the performance of electric vehicle batteries: Techniques, challenges, and solutions

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