AI for Sustainable Automotive Lifecycle Optimization
Keywords:
AI-powered lifecycle management,Sustainable automotive manufacturing,Smart factory optimization,Digital twin for production systems,Predictive maintenance using AI,Green manufacturing technologies,Circular economy in automotive,Intelligent energy management systems,Lifecycle assessment automation,Carbon footprint reduction in manufacturing,Machine learning for process optimization,Eco-efficient production planning,Industry 4.0 sustainability solutions,AI-enabled quality control,Data-driven sustainable operations.Abstract
Analysing sustainability at all levels of the automotive manufacturing lifecycle requires AI methods that can fuse data from multiple sources. Considerable progress is being made in planning, scheduling, control, and maintenance. With the right data acquisition and governance, the environmental, economic, and social performance of manufacturing companies can be optimised concurrently. Ultimately, those companies that integrate these capability improvements in a seamless, intuitive way stand to gain the most. AI systems can help achieve this—provided they are implemented wisely, with sufficient understanding of limitations. Considered action can promote quicker adoption of manufacturing AI and a more sustainable sector. Attention must focus on the right problems and solutions, with key emerging technologies applied effectively to positive effect.
A detailed analysis of the complex interactions shaping automotive manufacturing enables better decisions in multiple areas—including resource use, EMissions Control, quality, safety, and total cost. AI methods form a powerful toolbox for addressing such concerns. However, there is ultimately no universal solution. Instead, the appropriate selection, development, and integration of solutions is vital. Consequently, action is required to widen the scope of AI applications beyond the obvious use cases of predictive maintenance and real-time anomaly detection, making it possible to identify and analyse emerging, less obvious problems.
References
[1] Abadi, M. M. K. F., Liu, C., Zhang, M., Hu, Y., & Xu, Y. (2025). Leveraging AI for energy-efficient manufacturing systems: Review and future prospectives. Journal of Manufacturing Systems, 78, 153–177. doi:10.1016/j.jmsy.2024.11.017
[2] Bahmutsky, S., Turner, I., Arulnathan, V., & Pelletier, N. (2025). Advancing life cycle assessment through data science: A critical review of algorithms, tools, and data challenges. Sustainable Production and Consumption, 56, Article (Advance online publication). doi:10.1016/j.spc.2025.10.007
[3]Bai, H., Chen, Y., Bai, H., Liu, M., & Fan, Y. (2025). Energy optimization and efficiency improvement model for enterprise production process based on deep learning under the background of carbon peak and carbon neutrality. International Journal of Computational Intelligence Systems, 18, Article 169. doi:10.1007/s44196-025-00901-9
[4]Gkousis, S., Vasilaki, V., & Katsou, E. (2026). Machine learning and large language models for life cycle inventory compilation: Current situation and future developments. Renewable and Sustainable Energy Reviews, 228, 116577. doi:10.1016/j.rser.2025.116577
[5]Neupane, B., Belkadi, F., Formentini, M., Rozière, E., Hilloulin, B., Faraji Abdolmaleki, S., & Mensah, M. (2025). Machine learning algorithms for supporting life cycle assessment studies: An analytical review. Sustainable Production and Consumption, 56, 37–53. doi:10.1016/j.spc.2025.03.015
[6]Piron, M., Wu, J., Fedele, A., Manzardo, A., & Scipioni, A. (2024). Industry 4.0 and life cycle assessment: Evaluation of the technology applications as an asset for the life cycle inventory. Science of the Total Environment, 947, 172955. doi:10.1016/j.scitotenv.2024.172955
[7]Romeiko, X., Besseau, R., Pognant-Gros, P., & Guillaume, B. (2024). Machine learning in life cycle assessment: A comprehensive review and guide for predicting environmental impacts. Science of the Total Environment, 947, 172955. doi:10.1016/j.scitotenv.2024.172955
[8]Martínez-Ramón, J. P., Amor, B., & Rodríguez-Mesa, M. (2024). Integrating machine learning with life cycle assessment: A comprehensive review and guide for predicting environmental impacts. Cleaner Environmental Systems, 10, 100141. doi:10.1016/j.cesys.2024.100141
[9]Ghoroghi, M., Eggers, J., & Thiede, S. (2022). Environmental assessment of machine-learning-based LCA: Review and methodological considerations. The International Journal of Life Cycle Assessment, 27, 1167–1195. doi:10.1007/s11367-021-01969-w
[10]Salla, C., D’Antonio, G., & Sala, S. (2025). Dynamic life cycle assessment to support decision-making at the product, building, and urban scales. The International Journal of Life Cycle Assessment, 30, (Advance online publication). doi:10.1007/s11367-025-02455-4
[11]Luo, R., Zhou, S., Nie, L., & Dong, B. (2025). Quality prediction method for automotive body resistance spot welding based on digital twin technology. Scientific Reports, 15, Article 24391. doi:10.1038/s41598-025-09959-z
[12]Moussa, M., Abbas, M., & ElMaraghy, H. (2025). Industry 4.0 in automotive manufacturing: A digital twin approach. Procedia CIRP, (Advance online publication). doi:10.1016/j.procir.2025.02.197
[13]Haupt, J., Cerdas, F., & Herrmann, C. (2024). Derivation of requirements for life cycle assessment-related information to be integrated in digital battery passports. Procedia CIRP, 122, 300–305. doi:10.1016/j.procir.2024.01.044
[14]Pohlmann, A., Popowicz, M., Schöggl, J.-P., & Baumgartner, R. J. (2025). Digital product passports for electric vehicle batteries: Stakeholder requirements for sustainability and circularity. Cleaner Production Letters, 8, 100090. doi:10.1016/j.clpl.2024.100090
[15]Tabata, T., & Tsai, P. (2025). The role of life cycle assessments in digital product passport implementation for building a plastic circular economy. Circular Economy and Sustainability, 5, 3145–3157. doi:10.1007/s43615-025-00552-0
[16]Kühn, A. (2025). A review of digital twin integration in circular manufacturing for sustainable industry transition. Sustainability, 17(16), 7316. doi:10.3390/su17167316
[17]Zhang, Z., Zhang, Y., Liao, X., & Li, X. (2024). Digital product passport requirements and implementation pathways for electric-vehicle batteries: A review. Batteries, 10(4), 115. doi:10.3390/batteries10040115
[18]Wang, Z., Liu, J., & Yang, X. (2025). Integrating machine learning into life cycle assessment: A pathway to more efficient environmental impact prediction. PLOS Climate, 4, e0000460. doi:10.1371/journal.pclm.0000460
[19]Naser, M., Xu, Y., Yang, L., & Liu, S. (2023). Automating life cycle assessment for additive manufacturing with machine learning: A framework and case study. Journal of Manufacturing Systems, 71, 504–526. doi:10.1016/j.jmsy.2023.10.009
[20]Andrei, M., (2025). Advancing maturity in the adoption of digital technologies for energy efficiency in manufacturing: A maturity model approach. Journal of Manufacturing Technology Management, (Advance online publication). doi:10.1108/JMTM-09-2024-0482
Additional Files
Published
Data Availability Statement
None
