Sustainable Vehicle Design Considering Quality Level and Life Cycle Environmental Assessment (LCA)

Abstract

One of the global ecological problems is the excessive carbon dioxide emissions generated by vehicles in the transport sector, including passenger transport. Therefore, the objective of this investigation was to develop a model that supports the prediction of vehicle variants that will be satisfactory to the customer in terms of: (i) quality level and (ii) environmental impact throughout the life cycle. This model was developed with the following techniques: TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), LCA (Life Cycle Assessment), SMARTER (Specific, Measurable, Achievable, Relevant, and Time-bound), Pareto–Lorenz, and the Multi-Criteria Decision Method rule (7 ± 2). A model test was carried out for production variants of the electric vehicle BEV (battery electric vehicle) for which the quality level and life cycle assessment were estimated. Vehicle quality levels ranged from 0.15 to 0.69, with a weight of 0.75. However, vehicle life cycle scores were estimated in the range of 0.25 to 0.57, with a weight of 0.25. Ultimately, the level of the vehicles’ LCA ranged from 0.18 to 0.62. As a result, it was shown that on the basis of various modifications of the quality level of vehicle variants and the corresponding environmental impacts throughout their life cycle, it is possible to predict the vehicle variant that is most satisfactory for the customer and, at the same time, environmentally friendly. The originality of the model relies on supporting the making of sustainable design decisions and the planning of vehicle improvement actions according to customer expectations. Therefore, the model can be used to analyse different types of vehicles by producers and dealers of these products.

Improving Products Considering Customer Expectations and Life Cycle Assessment (LCA)

“…This is not the most beneficial approach for sustainable products that require significant changes during the improvement process (so-called rethinking design) (Lagerstedt et al, 2003). This is due to the lack of access to detailed data, which is available in LCA in subsequent stages and is then more reliable (Calado et al, 2019;El Badaoui and Touzani, 2022;Ulewicz et al 2023). Therefore, the aim of the article was to develop a method to help predict the direction of product improvement, taking into account its quality level and environmental impact during the life cycle (LCA).…”