Open Access

ARTICLE

Machine Learning-Accelerated Materials Genome Design of Hybrid Fiber Composites for Electric Vehicle Lightweighting

Chin-Wen Liao^1,2,3, En-Shiuh Lin¹, Wei-Lun Huang^4,5,6, I-Chi Wang⁷, Bo-Siang Chen^8,*, Wei-Sho Ho^1,2,9,*

1 Department of Electrical and Mechanical Technology, National Changhua University of Education, Bao-Shan Campus, No. 2, Shi-Da Rd, Changhua City, Taiwan
2 Graduate Institute of Technological and Vocational Education, National Changhua University of Education, Bao-Shan Campus, No. 2, Shi-Da Rd., Changhua City, Taiwan
3 Center of Teacher Education, National Chung Hsing University, No. 145, Xingda Rd., South Dist., Taichung City, Taiwan
4 Medical Affairs Office, National Taiwan University Hospital, No. 7, Zhongshan S. Rd., Zhongzheng Dist., Taipei City, Taiwan
5 Department of Health Services Adminstration, China Medical University, No. 100, Sec. 1, Jingmao Rd., Beitun Dist., Taichung City, Taiwan
6 Department of Health Care Management, National Taipei University of Nursing and Health Sciences, No. 365, Mingde Rd., Beitou Dist., Taipei City, Taiwan
7 Liberal Education Center, College of General Education, National Chin-Yi University of Technology, No. 57, Sec. 2, Zhongshan Rd., Taiping Dist., Taichung City, Taiwan
8 Department of Vehicle Engineering, Nan Kai University of Technology, No. 568, Zhongzheng Rd., Caotun Township, Nantou City, Taiwan
9 NCUE Alumni Association, National Changhua University of Education Jin-De Campus, No. 1, Jinde Rd., Changhua City, Taiwan

* Corresponding Authors: Bo-Siang Chen. Email: ; Wei-Sho Ho. Email:

Journal of Polymer Materials 2026, 43(1), 16 https://doi.org/10.32604/jpm.2026.076807

Received 27 November 2025; Accepted 09 February 2026; Issue published 03 April 2026

Abstract

The demand for extended electric vehicle (EV) range necessitates advanced lightweighting strategies. This study introduces a materials genome approach, augmented by machine learning (ML), for optimizing lightweight composite designs for EVs. A comprehensive materials genome database was developed, encompassing composites based on carbon, glass, and natural fibers. This database systematically records critical parameters such as mechanical properties, density, cost, and environmental impact. Machine learning models, including Random Forest, Support Vector Machines, and Artificial Neural Networks, were employed to construct a predictive system for material performance. Subsequent material composition optimization was performed using a multi-objective genetic algorithm. Experimental validation demonstrated that an optimized carbon fiber/bio-based resin composite achieved a 45% weight reduction compared to conventional steel, while maintaining equivalent structural strength. The predictive accuracy of the models reached 94.2%. A cost-benefit analysis indicated that despite a 15% increase in material cost, the overall vehicle energy consumption decreased by 12%, leading to an 18% total cost saving over a five-year operational lifecycle, under a representative mid-size battery electric vehicle (BEV) operational scenario.

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Keywords

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