Abstract

We present an optimization model for the passenger car vehicle fleet transition—the time-dependent fleet composition—in Germany until 2050. The goal was to minimize the cumulative greenhouse gas (GHG) emissions of the vehicle fleet taking into account life-cycle assessment (LCA) data. LCAs provide information on the global warming potential (GWP) of different powertrain concepts. Meta-analyses of batteries, of different fuel types, and of the German energy sector are conducted to support the model. Furthermore, a sensitivity-analysis is performed on four key influence parameters: the battery production emissions trend, the German energy sector trend, the hydrogen production path trend, and the mobility sector trend. Overall, we draw the conclusion that—in any scenario—future vehicles should have a plug-in option, allowing their usage as fully or partly electrical vehicles. For short distance trips, battery electric vehicles (BEVs) with a small battery size are the most reasonable choice throughout the transition. Plug-in hybrid electric vehicles (PHEVs) powered by compressed natural gas (CNG) emerge as promising long-range capable solution. Starting in 2040, long-range capable BEVs and fuel cell plug-in hybrid electric vehicles (FCPHEVs) have similar life-cycle emissions as PHEV-CNG.

Highlights

  • The world community concurred to the goal of limiting global temperature rise to ideally 1.5 ◦ C compared with the pre-industrial age during the United Nations climate conference in Paris in 2015.According to this, the German government set the goals of reducing greenhouse gas (GHG) emissions by 40% in 2020

  • We focus on the minimum achievable GHG emission potential—hereinafter referred to as ecological potential—of passenger car vehicles in the vehicle fleet

  • Our model permits analyzing the yearly life-cycle emissions for all powertrain concepts for every given fleet transition scenario. This way, regardless of being introduced into the fleet, we can estimate the life-cycle emissions of any given powertrain concept. For this base scenario, we identify that BEV100 and FCPHEV20 are causing a similar amount of GHG emissions as PHEV20-compressed natural gas (CNG) when looking between 2040 and 2050

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Summary

Introduction

The world community concurred to the goal of limiting global temperature rise to ideally 1.5 ◦ C compared with the pre-industrial age during the United Nations climate conference in Paris in 2015. In contrast to this study, previous studies have made prognostics of the German vehicle fleet transition following different approaches without minimizing the GHG emissions. A series of studies have performed LCAs on series production vehicles, which is a common approach to assess the life-cycle GHG emissions of current state-of-the-art powertrain technologies [6–. We model the German fleet transition until 2050 and calculate the cumulative GHG emissions, including all life-cycle phases of all vehicles. The cumulative GHG emissions serve as objective value for an optimization problem, which enables us to determine the GHG-optimal fleet transition This way, we are able to make a precise time-based assessment of the GHG emissions of different powertrain concepts taking into account the dynamic interaction of the vehicle fleet with the energy sector.

Optimization Model of the Vehicle Fleet Transition
Meta-Analysis of Life-Cycle GHG Emissions
Meta-Analysis of the Life-Cycle GHG Emissions of Batteries
Meta-Analysis of the Life-Cycle GHG Emissions of Fuels
Emissions Factors of the German Energy Sector from Today till 2050
Scenarios for the Sensitivity-Analysis of Key Influence Parameters
Battery Production Scenarios
Energy Sector Scenarios
Hydrogen Production Path Scenarios
Mobility Trend Scenarios
Modelling the Vehicle Behavior and Its Life-Cycle GHG Emissions
Identification of the Optimal Vehicle Fleet Transitions
Optimal Vehicle Fleet Transition for the Base Scenario
Optimal Vehicle Fleet Transition for the Worst-Case Scenario
Optimal Vehicle Fleet Transition for the Best-Case Scenario
Sensitivity-Analysis of Key Influence Parameters
Findings
Conclusions
Full Text
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