Techno-economic optimization and environmental evaluation of electric vehicles in commercial fleets

Die Einführung von batterieelektrischen Fahrzeugen (E-Pkw) gilt als eine wichtige Maßnahme zur Emissionsverringerung im Straßenverkehr. Gewerbliche Flotten in Deutschland bilden hierfür einen vielversprechenden Markt. Um dieses Potential zu realisieren, ist sowohl eine techno-ökonomische Optimierung als auch eine ökologische Bewertung über den Lebenszyklus erforderlich. Das Ziel der Dissertation ist es, hierfür ein methodisches Rahmenwerk zu liefern. Die kumulative Dissertation besteht aus fünf Artikeln, die sich den einzelnen Bestandteilen des Rahmenwerks widmen und großteils auf Technologie- und Nutzungsdaten aus eigenen Messungen aufbauen. Der erste Artikel, Schücking et al. (2016) [Paper I], ist eine technische Analyse. Sie untersucht den realen Energieverbrauch von E-Pkws im Vergleich zu konventionellen Fahrzeugen und identifiziert optimale Betriebspunkte. Die Ergebnisse heben den Einfluss verschiedener Faktoren auf den Energieverbrauch als wichtige Komponente detaillierter techno-ökonomischer und ökologischer Betrachtungen hervor. Der zweite und der dritte Artikel haben einen techno-ökonomischen Fokus. Sie beschäftigen sich mit der Frage, wie E-Pkws einen schnelleren wirtschaftlichen Break-even im Vergleich zu konventionellen Fahrzeugen erreichen können. Der zweite Artikel, Schücking et al. (2017) [Paper II], stellt Ladestrategien vor, welche eine höhere Auslastung der E-Pkw ermöglichen und damit zu geringen Gesamtkosten im Vergleich zu konventionellen Pkw führen können. Unsicherheiten in Fahrprofilen und Energieverbrauch begrenzen die Anwendbarkeit dieser Strategien. Der dritte Artikel, Schücking & Jochem (2020) [Paper III], knüpft hieran an. Er schlägt ein zweistufiges stochastisches Optimierungsmodell zur Minimierung der Investition und Betriebskosten eines E-Pkw unter Berücksichtigung dieser Unsicherheiten vor. Neben der stochastischen Betrachtung ist auch die Abwägung zwischen Batteriekapazität und Ladeleistung in der Investitionsentscheidung ein neuer Beitrag zur Forschung. Im Kontext der stochastischen Optimierung werden ein Hidden Markov Modell zur Generierung komplexer Fahrprofile und eine neue Szenario-Reduktionsheuristik als methodische Weiterentwicklungen angewandt. Artikel vier und fünf liefern eine ökologische Bewertung. Die empirischen Daten sowie der Fokus auf den deutsch-französischen Grenzverkehr zeichnen beide Artikel aus. Der vierte Artikel, Ensslen et al. (2017) [Paper IV], konzentriert sich auf die E-Pkw Nutzungsphase. Er verdeutlicht den Einfluss unterschiedlicher Strommixe und Ladezeitpunkte auf die CO2- Emissionen und Reduktionspotentiale. Der fünfte Artikel, Held & Schücking (2019) [Paper V], betrachtet verschiedene ökologische Wirkungskategorien (wie z.B. Klimawandel, Versauerung Eutrophierung) über den gesamten Lebenszyklus mittels eines modularen Screening-Modells. Die Ergebnisse unterstreichen den Einfluss der Batterie und der Nutzungsphase auf die ökologische Gesamtbilanz. Dem übergreifenden Forschungsziel folgend, zeigen die Ergebnisse der einzelnen Artikel in ihrer Kombination, dass die Optimierung des wirtschaftlichen Nutzens auch die ökologischen Vorteile erhöhen kann. Die ex-ante Ermittlung der optimalen Batteriekapazität sowie ein hoher Betriebsgrad erhöhen nicht nur die Wettbewerbsfähigkeit von E-Pkw, sondern beschleunigen unter bestimmten Voraussetzungen auch den ökologischen Break-even in einem Großteil der betrachteten Wirkungskategorien. Die Eigenschaften, die gewerbliche Anwendungen aus wirtschaftlicher Sicht zu einem vielversprechenden Einführungsmarkt für E-Pkws machen, können damit auch die angestrebten ökologischen Vorteile unterstützen

Two-Stage Stochastic Program Optimizing the Total Cost of Ownership of Electric Vehicles in Commercial Fleets

The possibility of electric vehicles to technically replace internal combustion engine vehicles and to deliver economic benefits mainly depends on the battery and the charging infrastructure as well as on annual mileage (utilizing the lower variable costs of electric vehicles). Current studies on electric vehicles’ total cost of ownership often neglect two important factors that influence the investment decision and operational costs: firstly, the trade-off between battery and charging capacity; secondly the uncertainty in energy consumption. This paper proposes a two-stage stochastic program that minimizes the total cost of ownership of a commercial electric vehicle under uncertain energy consumption and available charging times induced by mobility patterns and outside temperature. The optimization program is solved by sample average approximation based on mobility and temperature scenarios. A hidden Markov model is introduced to predict mobility demand scenarios. Three scenario reduction heuristics are applied to reduce computational effort while keeping a high-quality approximation. The proposed framework is tested in a case study of the home nursing service. The results show the large influence of the uncertain mobility patterns on the optimal solution. In the case study, the total cost of ownership can be reduced by up to 3.9% by including the trade-off between battery and charging capacity. The introduction of variable energy prices can lower energy costs by 31.6% but does not influence the investment decision in this case study. Overall, this study provides valuable insights for real applications to determine the techno-economic optimal electric vehicle and charging infrastructure configuration

Two-Stage Stochastic Program Optimizing the Total Cost of Ownership of Electric Vehicles in Commercial Fleets

The possibility of electric vehicles to technically replace internal combustion engine vehicles and to deliver economic benefits still mainly depends on the battery size and the charging infrastructure costs as well as on annual mileage (utilizing the lower variable costs of electric vehicles). Current studies on electric vehicles’ total cost of ownership often neglect two important factors that influence the investment decision and operational costs: firstly, the trade-off between battery and charging capacity; secondly the uncertainty in energy consumption. This paper proposes a two-stage stochastic program that minimizes the total cost of ownership of a commercial electric vehicle under uncertain energy consumption and available charging times induced by mobility patterns and outside temperature. The optimization program is solved by sample average approximation based on mobility and temperature scenarios. A hidden Markov model is introduced to predict mobility demand scenarios. Three scenario reduction heuristics are applied to reduce computational effort while keeping a high-quality approximation. The proposed framework is tested in a case study of the home nursing service. The results show the large influence of the uncertain mobility patterns on the optimal solution. In the case study, the total cost of ownership can be reduced by up to 3.9% by including the trade-off between battery and charging capacity. The introduction of variable energy prices can lower energy costs by 31.6% but does not influence the investment decision in this case study. Overall, this study provides valuable insights for real applications to determine the techno-economic optimal electric vehicle and charging infrastructure configuration

Influencing factors on specific energy consumption of EV in extensive operations

The sensitivities of electric vehicle (EV) energy consumption become significant when operating at long distances. This study analyzes these sensitivities based on empirical data of seven EV over 2.75 years with individual monthly mileages above 3,000 km and a specifically adopted energy consumption model. The results underline the influence of average speed, the distribution of speed and the auxiliaries as well as their opposing effects. It is demonstrated that the point of lowest specific energy consumption is not necessarily identical to the point where EV are most competitive compared to conventional internal combustion engine vehicles

Empirical carbon dioxide emissions of electric vehicles in a French-German commuter fleet test

According to many governments electric vehicles are an efficient mean to mitigate carbon dioxide emissions in the transport sector. However, the energy charged causes carbon dioxide emissions in the energy sector. This study demonstrates results from measuring time-dependent electricity consumption of electric vehicles during driving and charging. The electric vehicles were used in a French-German commuter scenario between March 2013 and August 2013. The electric vehicles travelled a total distance of 38,365 kilometers. 639 individual charging events were recorded. Vehicle specific data on electricity consumption are matched to disaggregated electricity generation data with time dependent national electricity generation mixes and corresponding carbon dioxide emissions with an hourly time resolution. Carbon dioxide emission reduction potentials of different charging strategies are identified. As carbon dioxide emission intensities change over time according to the electric power systems, specific smart charging services are a convincing strategy to reduce electric vehicle specific carbon dioxide emissions. Our results indicate that charging in France causes only about ten percent of the carbon dioxide emissions compared to Germany, where the carbon intensity is more diverse

Nutzerakzeptanz von Elektrofahrzeugen: Berufspendlerfahrgemeinschaften als Anwendungsfall

This three-year social-scientific accompanying research of six carpooling groups investigates the driving factors and barriers of using electric vehicles (EV) for commuting purposes. It analyzes to what extent the usage of EV in a field test (intervention: organized carpooling groups, cost reductions, guaranty of availability) increases user-acceptance and to what extent acceptance is correlated with frequency of use and if the participants’ environmental awareness has a positive effect on acceptance. To identify the driving factors and barriers as well as to assess the intervention, qualitative interviews with the participants before and at the end of the field test were conducted. To analyze potential correlations between acceptance and frequency of use as well as between acceptance and environmental awareness, a cross-sectional quantitative comparison based on empirical online survey data from EV users of other field-tests was carried out. The results demonstrate that a high usage frequency can result in a sustainable increase of EV acceptance. The accompanying intervention measures were able to overcome EV specific barriers. Despite facing frequent technical deficiencies acceptance of the EV commuter service was observed. According to our results, there is a strong correlation between environmental awareness and the acceptance of EV

Charging strategies for economic operations of electric vehicles in commercial applications

When substituting conventional with electric vehicles (EV) a high annual mileage is desirable from an environmental as well as an economic perspective. However, there are still significant technological limitations that need to be taken into consideration. This study presents and discusses five different charging strategies for two mobility applications executed during an early stage long-term field test from 2013 to 2015 in Germany, which main objective was to increase the utilization within the existing technological restrictions. During the field test seven EV drove more than 450,000 km. For four out of five presented charging strategies the inclusion of DC fast charging is indispensable. Based on the empirical evidence five key performance indicators (KPI) are developed. These indicators give recommendations to economically deploy EV in commercial fleets. The results demonstrate that the more predictable the underlying mobility demand and the more technical information is available the better the charging strategies can be defined. Furthermore, the results indicate that a prudent mix of conventional and DC fast charging allows a high annual mileage while at the same time limiting avoidable harmful effects on the battery

Empirical carbon dioxide emissions of electric vehicles in a French-German commuter fleet test

According to many governments electric vehicles are seen as an efficient mean to mitigate carbon dioxide emissions in the transport sector. However, the energy charged causes carbon dioxide emissions in the energy sector. This study demonstrates results from measuring time-dependent electricity consumption of electric vehicles during driving and charging. The electric vehicles were used in a French-German commuter scenario between March and August 2013. The electric vehicles ran a total distance of 38,365 km. 639 individual charging events were recorded. Vehicle specific data on electricity consumption are matched to disaggregated electricity generation data with time-dependent national electricity generation mixes and corresponding carbon dioxide emissions with an hourly time resolution. Carbon dioxide emission reduction potentials of different charging strategies are identified. As carbon dioxide emission intensities change over time according to the electric power systems, specific smart charging services are a convincing strategy to reduce electric vehicle specific carbon dioxide emissions. Our results indicate that charging in France causes only about ten percent of the carbon dioxide emissions compared to Germany, where the carbon intensity is more diverse