Battery Charge and Health Evaluation for Defective UPS Batteries via Machine Learning Methods

Çelik, M.S.; Tanağardigil, İ̇brahim; Soydemir, Mehmet Uğur; Şahin, Savaş

doi:10.1007/978-3-031-31956-3_25

Cited by 1 publication

(1 citation statement)

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“…This statistical technique seeks to model the linear association between a dependent variable denoted as (Y) and one or more independent variables (X), as illustrated in Figure 4a. The fundamental premise of the linear regression model posits that the dependent variable can be expressed as the sum of the independent variables, each multiplied by respective weights, as in Figure 4b, and further augmented by an associated error term as follows [54][55][56]:…”

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%