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

This paper summary is based on the article Automotive Lightweight Design: Simulation Modeling of Mass-Related Consumption for Electric Vehicles presented at the MDPI

1. Overview:

• Title: Automotive Lightweight Design: Simulation Modeling of Mass-Related Consumption for Electric Vehicles
• Author: Francesco Del Pero, Lorenzo Berzi, Andrea Antonacci, and Massimo Delogu
• Publication Year: 2020
• Publishing Journal/Academic Society: Machines (MDPI)
• Keywords: lightweighting; mechanical design; industrial engineering; automotive; simulation modeling; energy consumption; sustainability

2. Research Background:

• Social/Academic Context of the Research Topic:
  Energy-resources depletion and global climate change represent one of the major concerns for modern societies, leading to active industry and research developments to reduce fossil fuel consumption [1,2,3,4]. The road transportation sector accounts for a relevant quota of total energy demand and air emissions on a global scale [5]. Since operation is the most energy-consuming phase within a car's Life Cycle, lightweight design is emerging as a highly promising way to provide more sustainable mobility [6]. Lightweighting presents a very high potential to decrease use stage consumption because car mass and energy consumed during operation are strongly correlated. A 10% weight reduction entails a decrease in fuel consumption of about 3-6% at comparable functionality levels [10,11].
• Limitations of Existing Research:
  Existing research has primarily focused on lightweighting Internal Combustion Engine Vehicles (ICEVs) [32], with limited studies on advanced powertrain vehicles (electric, hybrid, and fuel cell) [38,39]. In particular, for Battery Electric Vehicles (BEVs), fuel-mass correlations are lower compared to ICEVs, attributed to the higher powertrain efficiency and lower energy consumption of BEVs [43]. While the study by Kim and Wallington [44] is the only work investigating FRVs for various powertrain technologies including BEVs, it is based on specific car models and does not provide guidance on estimating mass-consumption correlation for real-world vehicles. Furthermore, most existing simulation modeling activities are based on theoretical car models, lacking information on actual car model names and model years, and calculations are based on standardized driving cycles effective in specific geographical areas, making it difficult to secure global generality.
• Necessity of the Research:
  To overcome the limitations of existing research and accurately assess the energy and sustainability implications of BEV lightweighting, research is needed to analyze mass-induced energy consumption based on real-world car model data. In particular, studies are required that consider various driving patterns and perform sustainability assessments including the Impact Reduction Value (IRV).

3. Research Purpose and Research Questions:

• Research Purpose:
  The target of this work is to present an analytical calculation procedure for mass-induced energy consumption of pure Electric Vehicles and to provide support in the assessment of energy and sustainability implications of lightweighting within the BEV field.
• Key Research Questions:
• How does the Energy Reduction Value (ERV) vary across different BEV models and driving conditions?
• How does the environmental impact of lightweight design differ depending on the electricity grid mix?
• What modeling approach can accurately estimate ERV by considering the technical features of real-world car models?
• Research Hypotheses:
• ERV will vary significantly depending on vehicle size, driving cycle, and electricity grid mix.
• Technical features such as vehicle mass, maximum power, and power-to-mass ratio will influence ERV.
• An analytical model based on real-world vehicle data will enable a more accurate assessment of the energy and environmental benefits of BEV lightweighting.

4. Research Methodology

• Research Design:
  This study adopted a simulation modeling approach based on an analytical calculation procedure. A vehicle dynamics simulation model was developed using MATLAB-Simulink software, and energy consumption was calculated for various BEV models and driving conditions.
• Data Collection Method:
  Technical feature data of real BEV models sold in the 2019 European market were collected and used for case studies. Three driving cycles, NEDC, WLTP, and ALDC, were used to estimate energy consumption.
• Analysis Method:
  To analyze the change in energy consumption according to vehicle mass variation, the Energy Reduction Value (ERV) coefficient was calculated. ERV quantifies the specific consumption saving achievable through 100 kg mass reduction. In addition, the Impact Reduction Value (IRV) was calculated to assess the environmental impact of lightweight design. IRV was estimated for three distinct electricity grid mixes (Norwegian, average European, and Polish).
• Research Subjects and Scope:
  Ten BEV models belonging to classes A/B, C, and D/E were selected as case studies. Covering a wide spectrum of car sizes allowed us to model the correlation between mass and electricity absorption and to consider the strong variation of vehicle technical features.

5. Main Research Results:

• Key Research Results:
• ERV varies widely in the range of 0.47–1.17 kWh/(100 km × 100 kg) depending on vehicle model and driving cycle, with variability mainly depending on vehicle size and driving cycle.
• The ALDC driving cycle provides higher mass-induced consumption (ERVALDC) than NEDC and WLTP, which is analyzed to be due to the dynamic driving characteristics of ALDC.
• ERV increases with increasing vehicle size, implying that the energy-saving effect of lightweighting is greater for larger vehicles with higher energy demand.
• IRV varies significantly depending on the electricity grid mix, with the Norwegian grid (NO) showing the lowest IRV and the Polish grid (PL) showing the highest IRV.
• Vehicle mass (M) has the strongest correlation with ERV, showing a higher correlation coefficient (R2) than maximum power (Pmax) and power-to-mass ratio (P/M).
• Statistical/Qualitative Analysis Results:
• Table 1 reports the ERV and IRV coefficients for all vehicle case studies and driving cycles.
• Table 2 shows the analysis of ERV/IRV by car class/driving cycle in terms of minimum, maximum, range, arithmetic mean, and standard deviation values.
• Figure 4 shows the arithmetic mean of ERV over case studies per vehicle class and driving cycle.
• Figure 5 shows ERV for all case studies as a function of electricity consumption.
• Figure 6 shows the arithmetic mean of IRV over case studies per vehicle class and driving cycle.
• Figure 7 shows ERV for all case studies as a function of main vehicle technical features: regression lines. Vehicle mass (M) (a), maximum power (Pmax) (b), and power-to-mass ratio (P/M) (c).
• Figure 8 shows IRV for all case studies as a function of vehicle mass: regression lines.
• Figure 9 shows the Break-Even Point (BEP) for sustainability case studies (Front Module (FM), Front Hood (FH), Front Door (FD), Crash Dashboard Beam (CDB), and Suspension Arm (SA)).
• Figure 10 shows BEP as a function of car mass for sustainability case studies FM, FH, and SA. Norwegian grid mix (a), average European grid mix (b), Polish grid mix (c).
• Figure A1. Speed profile of the ALDC.
• Figure A2. Energy consumption in function of mass with regression lines and ERV coefficient.
• Data Interpretation:
• ERV varies greatly depending on various factors such as vehicle size, driving conditions, and electricity grid mix, so these factors should be comprehensively considered when evaluating the effectiveness of lightweight design.
• The energy-saving effect of lightweighting is more pronounced under dynamic driving conditions such as ALDC.
• The higher the carbon intensity of the electricity grid, the greater the environmental benefits of lightweighting.
• Vehicle mass is the most important technical feature for predicting ERV, and ERV can be effectively estimated using vehicle weight information.
• Figure Name List:
• Figure 1. Layout of MATLAB-Simulink model: driver (a), powertrain (b), driveline (c), and energy management (d).
• Figure 2. Look-up-tables for basic motor characteristics.
• Figure 3. Brake-blending criteria: typical braking repartition in absence of regenerative braking capabilities (a) and brake blending for vehicles capable of regenerative braking capabilities on front axle (b).
• Figure 4. Arithmetic mean of ERV over case studies per vehicle class and driving cycle.
• Figure 5. ERV for all case studies in function of electricity consumption.
• Figure 6. Arithmetic mean of IRV over case studies per vehicle class and driving cycle.
• Figure 7. ERV for all case studies in function of main vehicle technical features: regression lines. Vehicle mass (M) (a), maximum power (Pmax) (b), and power-to-mass ratio (P/M) (c).
• Figure 8. IRV for all case studies in function of vehicle mass: regression lines.
• Figure 9. Break-Even Point (BEP) for sustainability case studies (Front Module (FM), Front Hood (FH), Front Door (FD), Crash Dashboard Beam (CDB), and Suspension Arm (SA)).
• Figure 10. BEP in function of car mass for sustainability case studies FM, FH, and SA. Norwegian grid mix (a), average European grid mix (b), Polish grid mix (c).
• Figure A1. Speed profile of the ALDC.
• Figure A2. Energy consumption in function of mass with regression lines and ERV coefficient.

6. Conclusion and Discussion:

• Summary of Main Results:
  This study developed an analytical framework for evaluating the mass-induced energy consumption of BEVs and estimated ERV and IRV coefficients by considering real-world vehicle models and various driving conditions. The results showed that ERV varies significantly depending on vehicle model, driving cycle, and electricity grid mix, and that vehicle mass is the most influential factor on ERV. It was also shown that the environmental benefits of lightweight design can vary significantly depending on the carbon intensity of the electricity grid.
• Academic Significance of the Research: This study has the following academic significance in the field of BEV lightweighting research:
  • In-depth analysis and data provision on mass-induced energy consumption of BEVs.
  • Presentation of ERV/IRV estimation methodology based on real-world vehicle model data.
  • Development of a framework for analyzing lightweighting effects considering various driving conditions and electricity grid mixes.
• Practical Implications: The findings of this study provide the following practical implications for the automotive industry and policymakers:
  • Can be used to quantitatively evaluate the energy and environmental benefits of BEV lightweight design.
  • Supports the establishment of customized lightweighting strategies considering vehicle models, driving conditions, and electricity grid mixes.
  • Provides basic data for establishing lightweighting technology development and dissemination policies.
• Limitations of the Research: This study has the following limitations:
  • The simulation model may not perfectly reflect the complexity of real vehicles.
  • Case studies are limited to specific BEV models and driving conditions.
  • Only three representative scenarios were considered for the electricity grid mix.

7. Future Follow-up Research:

• Directions for Follow-up Research: Future research can proceed in the following directions:
  • Expansion of research to more diverse BEV models and powertrain technologies.
  • Model verification and improvement based on real road driving data.
  • Inclusion of cost and economic efficiency analysis of vehicle lightweighting technology.
  • Study on changes in lightweighting effects according to changes in electricity grid mix and expansion of renewable energy distribution.
• Areas Requiring Further Exploration:
• Impact of vehicle lightweighting on other vehicle characteristics such as vehicle performance, safety, and ride comfort.
• Life Cycle Assessment (LCA) of lightweighting technology and analysis of environmental impacts throughout the entire process.
• Research on strategies for disseminating lightweighting technology through consumer acceptance and marketability analysis.

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9. Copyright:

• This material is "Francesco Del Pero, Lorenzo Berzi, Andrea Antonacci, and Massimo Delogu"'s paper: Based on "Automotive Lightweight Design: Simulation Modeling of Mass-Related Consumption for Electric Vehicles".
• Paper Source: https://doi.org/10.3390/machines8030051

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