Multi-Criteria Decision Making using TOPSIS Method for Battery Type Selection in Hybrid Propulsion System

Bayraktar, Murat; Nuran, Mustafa

doi:10.7225/toms.v11.n01.w02

Cited by 6 publications

(2 citation statements)

References 18 publications

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“…In the study focused on maritime transport, researchers used the TOPSIS method to evaluate energy storage systems for ship propulsion engines. The optimal solution was a lithium-iron phosphate battery [28]. The integration of renewable energy and fossil fuel-generated energy is essential for the eventual transition to "green energies".…”

Section: Hazardous Risksmentioning

confidence: 99%

Reviewing the Cost–Benefit Analysis and Multi-Criteria Decision-Making Methods for Evaluating the Effectiveness of Lithium-Ion Batteries in Electric Vehicles

Barić,

Grabušić,

Jakara

et al. 2023

Sustainability

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Lithium-ion batteries (LIBs) have a wide range of applications in different fields, starting with electronics and energy storage systems. The potential of LIBs in the transportation sector is high, especially for electric vehicles (EVs). This study aims to investigate the efficiency and effectiveness of, and justification for, the application of LIBs in the field of transport, primarily in EVs. The research focuses on single and multi-criteria evaluations of the efficiency of LIBs. Previous studies in which LIBs were evaluated using cost–benefit analysis (CBA) and multi-criteria decision-making methods (MCDM) were analysed. An electronic literature search of the Web of Science, Scopus, and other relevant databases was performed. The literature was searched using the keywords: “lithium-ion batteries”; “multi-criteria decision-making”; “cost-benefit analysis”; “energy storage”; “vehicles”; “PROMETHEE” (or other MCDM method)”. A total of 40 scientific articles concerning the application of CBA (of which are 20%) and MCDM methods between 1997 and 2023, worldwide, were analysed. The results show multiple applications of both CBA and MCDM methods. The main findings of the areas of application were summarised and future research was discussed.

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Section: Hazardous Risksmentioning

confidence: 99%

Reviewing the Cost–Benefit Analysis and Multi-Criteria Decision-Making Methods for Evaluating the Effectiveness of Lithium-Ion Batteries in Electric Vehicles

Barić,

Grabušić,

Jakara

et al. 2023

Sustainability

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“…The power is produced by the ship's engines-very often diesel engines. The crucial element of the reliability structure of the vessel power system is the engine (or the cylinders of the engine) [1]; therefore, its technical condition should be the subject of continuous diagnostics.…”

Section: Introductionmentioning

confidence: 99%

Ship Diesel Engine Fault Diagnosis Using Data Science and Machine Learning

Pająk,

Kluczyk,

Muślewski

et al. 2023

Electronics

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One of the most important elements of the reliability structure of a motor vessel is its power subsystem, with the most crucial component being the engine. An engine failure excludes the ship from operation or significantly limits its operation. Therefore, accurate fault diagnosis should be a crucial issue for modern maintenance strategies. In mechanical engineering, the vibration and acoustic signals recorded during the operation of the device are the most meaningful data used to identify the reliability state. In this paper, a novel system-oriented method of reliability state identification is proposed. The method consists of the analysis of the vibration and noise signals collected on each of the engine cylinders using supervised machine learning. The main novelty of this method is data augmentation application and SVM classifier implementation. Due to these aspects, the method becomes robust in the case of poor-quality data or a limited and incomplete learning dataset. The quality of the proposed identification method was evaluated by addressing a new industrial issue (Sulzer 6AL20/24 marine engine reliability state identification). During the tests, the efficiency of the method was analyzed in the case of a complete learning data set (all types of inability states were presented in the learning data set) and an incomplete learning data set (in the testing data set, there were new types of inability states). As a result, in both cases, a very high (100%) identification accuracy of the reliability state and the type of inability state was obtained. This is a significant increase in accuracy (4.6% for the complete and 22% for the incomplete learning data set) in comparison to the efficiency of the same method without the use of machine learning and data science.

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Method for sizing and selecting batteries for the energy storage system of an electric vehicle

Lira Júnior,

Gomes,

da Silva

et al. 2023

Electr Eng

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Multi-Criteria Decision Making using TOPSIS Method for Battery Type Selection in Hybrid Propulsion System

Cited by 6 publications

References 18 publications

Reviewing the Cost–Benefit Analysis and Multi-Criteria Decision-Making Methods for Evaluating the Effectiveness of Lithium-Ion Batteries in Electric Vehicles

Reviewing the Cost–Benefit Analysis and Multi-Criteria Decision-Making Methods for Evaluating the Effectiveness of Lithium-Ion Batteries in Electric Vehicles

Ship Diesel Engine Fault Diagnosis Using Data Science and Machine Learning

Method for sizing and selecting batteries for the energy storage system of an electric vehicle

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