Multi-disciplinary design optimization of life cycle eco-efficiency for heavy-duty vehicles using a genetic algorithm

Sebastian Wolff, Moritz Seidenfus, Matthias Brönner, Markus Lienkamp

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

Despite the Paris Climate Agreement and other international pledges to reduce anthropogenic carbon-dioxide emissions, road transportation emissions are increasing. Therefore, the European Union has introduced fines for exceeding CO2-limits beginning in 2025, forcing European truck manufacturers to replace diesel-powered vehicles with low-emission vehicles. Thus, hybrid, battery, and fuel cell electric trucks are in the race to become the dominant technology. Giving recommendations to decision makers, our approach to eco-efficiency combines the two disciplines of ecological and economical assessment. The study's unified cradle-to-grave system boundary for both disciplines ensures a comprehensive and holistic forecast. To account for and project the vehicles’ future technological potential, the evolutionary algorithm NSGA-II optimizes their design parameters with regard to environmental and economic performance. To further include user requirements, we have supplemented these eco-efficiency objectives by a tractive force reserve. The results indicate that battery electric trucks have competitive costs compared to diesel-powered vehicles. We find that with today's electricity mix, the environmental impact of battery powered is 313% higher than diesel. However, with increasing renewable energy the battery electric vehicles outperform the diesel (−65%). Operating the fuel cell with green hydrogen decreases environmental impact (−27%). BEV and FCEV potentially perform at the same costs as today's diesel. Our study shows the impact of renewable energy on long-haul transportation and quantifies the associated costs. With this, we compare eco-efficient vehicle concepts suitable for future transportation.

Original languageEnglish
Article number128505
JournalJournal of Cleaner Production
Volume318
DOIs
StatePublished - 10 Oct 2021

Keywords

  • Eco-efficiency
  • Fuel cell electric
  • Genetic algorithm
  • Heavy-duty
  • Life cycle assessment
  • Total cost of ownership

Fingerprint

Dive into the research topics of 'Multi-disciplinary design optimization of life cycle eco-efficiency for heavy-duty vehicles using a genetic algorithm'. Together they form a unique fingerprint.

Cite this