Article, 2023

Environmental optimization of the charge of battery electric vehicles

In: Applied Energy, ISSN 0306-2619, 1872-9118, Volume 329, Page 120259, 10.1016/j.apenergy.2022.120259

Contributors (4)

Zacharopoulos, Leon (0000-0003-2952-2630) (Corresponding author) [1] Thonemann, Nils (0000-0001-5966-2656) [2] Dumeier, Marcel (0000-0003-4090-5077) [1] Geldermann, Jutta (0000-0002-6437-0305) [1]

Affiliations

  1. [1] University of Duisburg-Essen
  2. [NORA names: Germany; Europe, EU; OECD]
  3. [2] Technical University of Denmark
  4. [NORA names: DTU Technical University of Denmark; University; Denmark; Europe, EU; Nordic; OECD]

Abstract

The battery electric vehicle’s environmental impacts are highly influenced by the emissions attributed to the electricity used to recharge the battery. The electricity generation’s environmental assessment, however, is mainly based on static yearly mean shares of primary energy sources. To overcome this uncertainty, we develop a mixed-integer linear programming model to couple variable, hourly environmental impacts of electricity generation with representative user behavior in Germany. The model is then used to quantify and optimize the mitigation potential of environmental impacts for 2019, 2025, 2030, and 2050. Focusing on one method that minimizes the overarching environmental impacts could lead to ambiguous results. Instead, we aim to optimize charging behavior regarding each midpoint category and reveal the conflicting objectives among all the environmental categories that arise when aiming to minimize environmental impacts holistically. Considering greenhouse gas emissions, a reduction of 38% can be achieved through optimized demand timing for 2019. This charging strategy, however, increases the depletion of material resources by 72% compared to an optimal reference charging profile. The results for the future energy generation scenarios show that deviation between environmental impact categories can deviate and differences increase over the investigated time horizon. Nevertheless, by analyzing the differences between all impact category pairs, we found six categories, including climate change, within which differences are found to be less than 10%.

Keywords

Germany, ambiguous results, assessment, batteries, battery electric vehicles, behavior, categories, category pairs, changes, charge, climate change, couple variables, depletion, deviation, differences, electric vehicles, electricity, electricity generation, emission, energy generation scenarios, energy sources, environmental assessment, environmental categories, environmental impact categories, environmental impacts, environmental optimization, gas emissions, generation, generation scenarios, greenhouse gas emissions, horizon, impact, impact categories, linear programming model, material resources, mean share, method, midpoint categories, mitigation potential, mixed-integer linear programming model, model, objective, optimal reference, optimization, pairs, potential, primary energy source, profile, programming model, reduction, reference, resources, results, scenarios, share, source, strategies, time horizon, timing, uncertainty, user behavior, variables, vehicles