Automotive Lightweight Design: Simulation Modeling of Mass-Related Consumption for Electric Vehicles

• Main Authors: Francesco Del Pero, Lorenzo Berzi, Andrea Antonacci, Massimo Delogu
• Format: Article
• Language: English
• Published: MDPI AG 2020-09-01
• Series: Machines
• Subjects: lightweighting; mechanical design; industrial engineering; automotive; simulation modeling; energy consumption
• Online Access: https://www.mdpi.com/2075-1702/8/3/51

A thorough assessment of Life-Cycle effects involved by vehicle lightweighting needs a rigorous evaluation of mass-induced consumption, on which energy and sustainability benefits during use stage directly depend. The paper proposes an analytical calculation procedure to estimate the weight-related energy consumption of pure Electric Vehicles (EVs), since existing literature leaves considerable room for improvement regarding this research area. The correlation between consumption and mass is expressed through the Energy Reduction Value (ERV) coefficient, which quantifies the specific consumption saving achievable through 100 kg mass reduction. The ERV is estimated for a number of heterogeneous case studies derived from real 2019 European market EV models and according to three drive cycles, to consider different driving behaviors. For the case studies under consideration, ERV ranges from 0.47 to 1.17 kWh/(100 km × 100 kg), with the variability mainly depending on vehicle size and driving cycle. Given the high uncertainty of mass-related consumption on car size, an analytical method is refined to estimate accurately the ERV for any real-world EV model, starting from vehicle technical features. Along with energy assessment, the research also evaluates the environmental implications of lightweight design by means of the Impact Reduction Value (IRV), which is estimated for three distinct electricity grid mixes. Finally, the ERV/IRV modeling approach is applied to a series of comparative lightweight case studies taken from the literature. Such an application demonstrates the effective utility of the work to reduce the uncertainty for all cases where no physical tests or computer-aided simulations are available.