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Enhancing lithium-ion battery state of health estimation through data collection

  • The increasing demand for electric vehicles EV in recent years has led to a growing need for advanced BMS that can accurately estimate the state of health SOH of batteries. The SOH is a critical parameter that determines the performance and lifespan of batteries, and accurate estimation of these parameters is essential for optimizing battery utilization and improving the overall efficiency and reliability of EV. Accurately estimating the SOH of batteries in real driving conditions is a challenging task due to the dynamic nature of driving cycles, which can cause significant variations in battery behavior. Moreover, the accuracy of existing estimation techniques is often affected by factors such as battery degradation, temperature variations, and non-linearities in battery behavior. To address these challenges, researchers and engineers have developed a wide range of techniques and algorithms for estimating the SOH of batteries in real driving conditions. These techniques include model-based approaches, data-driven methods, andThe increasing demand for electric vehicles EV in recent years has led to a growing need for advanced BMS that can accurately estimate the state of health SOH of batteries. The SOH is a critical parameter that determines the performance and lifespan of batteries, and accurate estimation of these parameters is essential for optimizing battery utilization and improving the overall efficiency and reliability of EV. Accurately estimating the SOH of batteries in real driving conditions is a challenging task due to the dynamic nature of driving cycles, which can cause significant variations in battery behavior. Moreover, the accuracy of existing estimation techniques is often affected by factors such as battery degradation, temperature variations, and non-linearities in battery behavior. To address these challenges, researchers and engineers have developed a wide range of techniques and algorithms for estimating the SOH of batteries in real driving conditions. These techniques include model-based approaches, data-driven methods, and hybrid techniques that combine both model-based and data-driven approaches. The objective of this master thesis is to critically review the existing literature on estimation techniques for SOH in real driving cycles, identify the strengths and limitations of different approaches, and propose a novel estimation technique that can overcome the limitations of existing approaches. The proposed technique will be evaluated using real-world data obtained from a test vehicle. It is vital to do a precise assessment of the condition of these batteries in order to guarantee that they can be used safely and to prevent explosions that may possibly be catastrophic. The challenges that were discussed before could be solved with the assistance of prediction models. The purpose of this research is to evaluate the accuracy of predictions made by a variety of machine learning algorithms on the state of the battery. In order to achieve this result, time series forecasting techniques are used to data metrics. It was shown that Long Short-Term Memory LSTM models perform very well when it comes to the creation of forecasts that can be relied upon. An accurate forecast made with the aid of machine learning models may assist in increasing sales of electric vehicles and ensuring that these batteries are used in a secure manner.show moreshow less

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Metadaten
Author:Rupen Ashokbhai Sarvaiya
Language:English
Document Type:Master's Thesis
Reviewer(s) at THI:Hans-Georg Schweiger, Armin Arnold
Publishing Institution:Technische Hochschule Ingolstadt
Date of first Publication:2024/02/05
Year of first Publication:2024
Place of publication:Ingolstadt
Pages:72, XXII
URN:urn:nbn:de:bvb:573-45967
Faculties / Institutes / Organizations:Fakultät Elektro- und Informationstechnik
Degree Programmes:International Automotive Engineering (M. Eng.)
Licence (German):License Logo Urheberrechtsschutz
Release Date:2024/03/07