The worldwide transition to electric vehicles (EVs) has resulted in a substantial rise in the quantity of end-of-life (EoL) EVs that need effective recycling methods. This study examined a method of transfer learning that uses features to classify valuable electrical components from end-of-life electric vehicles. The study made use of a dataset consisting of high-resolution photographs of different electronic control units (ECUs). The photos were processed using pre-trained InceptionV3 convolutional neural network (CNN) models to identify distinctive features. The performance of four classifiers, namely the Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Random Forest (RF), and Naive Bayes (NB), was tested using the collected features. The dataset was partitioned into training, validation, and test sets using a 70:15:15 stratified split to guarantee an equitable distribution of all classes. The InceptionV3-SVM pipeline achieved the highest performance, with training, validation, and test accuracies of 100%, 97%, and 97%, respectively. Other classifiers also demonstrated strong performance, with validation and test accuracies exceeding 94%. The high accuracy and generalization capabilities of the InceptionV3-SVM pipeline indicate its potential for practical deployment in sustainable manufacturing processes. This study provides a foundation for further research in the automated sorting and recovery of high-value electronic components from EVs, potentially extending to a broader range of electronic components and applications. The findings highlight the effectiveness of transfer learning techniques in enhancing the efficiency and accuracy of recycling operations in the automotive industry.
Feature-Based Transfer Learning for High-Value Component Recovery in Electric Vehicles: An InceptionV3 Model Evaluation
Lect. Notes in Networks, Syst.
International Conference on Intelligent Manufacturing and Robotics ; 2024 ; Suzhou, China August 22, 2024 - August 23, 2024
Selected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024, 22-23 August, Suzhou, China ; Kapitel : 29 ; 368-375
01.04.2025
8 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Electric Vehicles (EVs) , End-of-Life (EoL) , Feature-Based Transfer Learning , Machine Learning , Deep Learning , Sustainable Manufacturing Engineering , Control, Robotics, Mechatronics , Artificial Intelligence , Industrial Chemistry/Chemical Engineering , Signal, Image and Speech Processing , Nanoscale Science and Technology , Computational Intelligence
DETERMINING FEATURE POSES OF ELECTRIC VEHICLES TO AUTOMATICALLY CHARGE ELECTRIC VEHICLES
Europäisches Patentamt | 2024
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