Abstract

The government’s aim of one million environmentally friendly electric vehicles (EVs) on German roads by 2020 has been withdrawn due to the low demand for such vehicles. The rea- sons are different and range from unfavorable price-performance ratios to the actual battery of EVs itself. Against this background, successful industrial and policy measures are necessary in order to offer customer-oriented solutions for EVs in Germany. This dissertation contributes to the evaluation of battery electric vehicles (BEVs) from a consumer-oriented total cost of ownership (TCOC) and a behavioral decision making (vehicle choice) perspective, using three re- search approaches to derive potential industrial and policy measures for Germany. These three approaches are described below in detail and are interconnected as follows: (1) modeling of the TCOC followed by (2) analyzing of the TCOC information disclosure under a risk situation and (3) optimizing of the TCOC using an optimal cost package for BEVs. The first research paper to be published formed the basis for this cumulative dissertation. In the paper, a TCOC model was developed to identify the profitability, including potential cost risks, of selected passenger vehicles. This approach was achieved by extending existing TCOC models with often ignored ownership costs, such as the resale value for the battery second use and second life. We identified the profitability for the ten most frequently registered BEVs in Germany in 2014 and compared them with similar internal combustion engine vehicles (ICEVs) fueled with gasoline (ICEV-SI) or diesel (ICEV-CI). Here, we also considered full hybrid electric vehicles (HEVs), as they play an important role in the transformation phase between BEVs and ICEVs. We analyzed the results under various assumptions, including a Monte Carlo simulation with 500,000 draws per simulation for 2016 and 2021. Results show that four BEVs – BMWi3, TeslaS, Mercedes B250e, and TeslaX – and five HEVs – Honda Jazz, Toyota Auris, LexusIS, Mercedes E300h, and Mercedes S400h – are profitable despite cost risks in the elec- tricity/fuel and battery price compared to their equivalent ICEVs in 2016. The purchase-based incentive in the form of a subsidy shows a positive effect on the actual TCOC but not for all selected BEVs. The second research paper analyzed two potential use-based incentive measures in the form of TCOC information disclosure and a driving ban on ICEVs with regard to the purchasing decision for a BEV in order to determine the effectiveness of both influential factors. This approach was achieved by asking 379 potential consumers via an online experiment, including a post-experimental questionnaire throughout Germany. We analyzed the choice decision for a BEV by using a logistic regression with a linear generalized estimating equation (GEE) method. Results show that these two potential use-based incentives can have a positive impact on the choice decision for BEVs, as the threat of a driving ban on ICEVs and the TCOC information weighted with the TCOC value difference have a significant positive effect on the decision of whether to buy a BEV (p < 0.001). In addition, control variables with costs (purchase price, range), hedonic benefits (awareness, experience), and some demographics (education, annual mileage) show a significant impact on the choice decision for a BEV. Moreover, the three well known BEV attributes – range, infrastructure, and price – remain a problem. In a further step, we calculated the planned TCOC to make a clear statement about the financial consequences for ICEVs. The risk-related policy measure also has a positive impact on the planned TCOC, as the already profitable four BEVs become even more favorable. With the subsidy, in total the five BEVs (same as with actual TCOC) are favored over their ICEVs. The last research paper analyzed potential cost-benefit improvements for BEVs with regard to the purchasing decision in order to identify the optimum price-performance ratio when calculating the target TCOC. This approach was achieved by asking 86 early adopters via a discrete choice experiment, including a questionnaire throughout Germany. We analyzed the cost-benefit related attributes with regard to the choice decision for a BEV by using a mixed logit (MXL) regression. Results show that the selected vehicle-based attributes can have a significant impact on BEVs choice, as the monetary costs (initial purchasing price (IPP), battery payment option (BC), yearly maintenance and repair costs (MR) and electricity costs per 100 km for fast charging (EC): p < 0.001) have a significantly negative impact and non-monetary costs (range (R): p < 0.001) have a significantly positive impact on the choice decision for a BEV. On this basis, we determined the optimal cost package comprising a 15 % lower MR and 10 % lower IPP, including a battery purchase with an actual consumption of 17 kilowatt hour (kWh) and an actual range of 700 km. Following that, we calculated the target TCOC to identify the optimum price-performance ratio for BEVs. The identified cost-benefit improvements lead to roughly ten percent saving potential of specific BEVs. The already profitable four BEVs (same as with actual TCOC) become even more economical. Besides the Renault Zoe Z.E., with the subsidy the German key vehicle segment (medium segment) becomes profitable for BEVs (VW e-Golf and Nissan Leaf) over ICEVs. Consequently, determined cost-benefit improvements seem to be successful, as the results can have an essential impact on the German mass-market. In summary, we established with our overall research approach that all selected BEV segments, with the exception of the mini vehicle segment (e.g. Smart), can become profitable. Consequently, this dissertation provides valuable insights for research and for practice (industry and government) to enable the further development of BEVs in Germany.

Abstract

The electrification of transport is considered to be a promising way for reducing negative environmental impacts. It has a high priority for many European nations but its dispersion is far behind expectations. One cause for this lag is the insufficient charging infrastructure. When charging infrastructure is established, the question arises where to place new charging stations and/or how to extend existing ones. We present the results of a study commissioned by the Asfinag (Austrian motorway operator) which addresses this question. Further we propose a method for an optimized siting and sizing of fast charging stations. The objective is to establish a fast charging infrastructure for battery electric vehicles on motorways to enable long distance trips beyond cruising ranges."br" Charging demand is estimated based on traffic flows between origins and destinations. Predicted penetration rates for battery electric vehicles for the years 2020 and 2025 are used to estimate a traffic demand, described by path flows, for battery electric vehicles. Path flows beyond maximum cruising range are selected, and a sub-route is determined, bounded by the first possible and latest necessary location for charging."br" Finally, a facility location problem is formulated in order make trips beyond maximum cruising range on motorways in Austria feasible. In the optimization problem, only rest areas are considered as possible locations for charging stations. Existing charging stations on motorways are included. In addition to determining locations, the number of CPs per charging station to be build is investigated.

Abstract

Battery models have gained great importance in recent years, thanks to the increasingly massive penetration of electric vehicles in the transport market. Accurate battery models are needed to evaluate battery performances and design an efficient battery management system. Different modeling approaches are available in literature, each one with its own advantages and disadvantages. In general, more complex models give accurate results, at the cost of higher computational efforts and time-consuming and costly laboratory testing for parametrization. For these reasons, for early stage evaluation and design of battery management systems, models with simple parameter identification procedures are the most appropriate and feasible solutions. In this article, three different battery modeling approaches are considered, and their parameters’ identification are described. Two of the chosen models require no laboratory tests for parametrization, and most of the information are derived from the manufacturer’s datasheet, while the last battery model requires some laboratory assessments. The models are then validated at steady state, comparing the simulation results with the datasheet discharge curves, and in transient operation, comparing the simulation results with experimental results. The three modeling and parametrization approaches are systematically applied to the LG 18650HG2 lithium-ion cell, and results are presented, compared and discussed. Document type: Article

Abstract

The successful transition of fully electric vehicle into automotive market is plagued with expensive product prices and limited drive range. While manufacturers point to fuel saving benefits, the actual cost savings after the first battery replacement presents negative economics. Hence it is necessary to maximise the fuel saving costs and to prolong the battery life as much as possible. The situation calls for an assistant system which takes into consideration the inherent propulsion system dynamics of electric vehicle in two typical situations – namely city and highway. Here we propose a combination of two systems, first a dynamic programming based acceleration controller for city cycle and second, an eHorizon based ACC system for maximum recuperation on highways. This paper is an extension of papers [1,2] and forms a series which is attributed to the development of a partial or complete “Safe and energy efficient longitudinal vehicle controller”. Such a controller is named “SAGA” - Smart and Green Automated Cruise Control. It is an ecological driver assistance system (eDAS) that adapts the vehicle speed over all its speed range according to a forward vehicle and to road events in a near horizon (legal speed, curves, etc…) with an aim to reduce the energy consumption without compromising on safety. Document type: Article