Vehicle Concepts

Eco-driving algorithms optimize the speed profile to reduce the energy consumption of a vehicle. This paper presents an eco-driving algorithm for battery electric powertrains that applies a split loss integration approach to incorporate the component losses. The algorithm consistently uses loss models to overcome the drawbacks of efficiency maps, which cannot represent no-load losses at zero torque. The use of loss models is crucial since the optimal solution includes gliding, during which there are no-load losses. An analysis shows, that state-of-the-art nonlinear programming algorithms cannot represent these no-load losses at zero torque with a small modeling error. To effectively compute the powertrain losses with only a small error in comparison to the measurement data, we introduce a tailored combination of nonlinear inequality constraints that interleave two polynomial fits. This approach can properly represent reality. We parameterize the algorithm and validate the vehicle model used with real-world measurement data. Furthermore, we investigate the influence of the proposed interleaved fits by comparing them to a single continuous high-order polynomial fit and to the state of the art. The algorithm is published open source.

Despite the Paris Climate Agreement and other international pledges to reduce anthropogenic carbon-dioxide emissions, road transportation emissions are increasing. Therefore, the European Union has introduced fines for exceeding CO2-limits beginning in 2025, forcing European truck manufacturers to replace diesel-powered vehicles with low-emission vehicles. Thus, hybrid, battery, and fuel cell electric trucks are in the race to become the dominant technology. Giving recommendations to decision makers, our approach to eco-efficiency combines the two disciplines of ecological and economical assessment. The study’s unified cradle-to-grave system boundary for both disciplines ensures a comprehensive and holistic forecast. To account for and project the vehicles’ future technological potential, the evolutionary algorithm NSGA-II optimizes their design parameters with regard to environmental and economic performance. To further include user requirements, we have supplemented these eco-efficiency objectives by a tractive force reserve. The results indicate that battery electric trucks have competitive costs compared to diesel-powered vehicles. We find that with today’s electricity mix, the environmental impact of battery powered is 313% higher than diesel. However, with increasing renewable energy the battery electric vehicles outperform the diesel (−65%). Operating the fuel cell with green hydrogen decreases environmental impact (−27%). BEV and FCEV potentially perform at the same costs as today’s diesel. Our study shows the impact of renewable energy on long-haul transportation and quantifies the associated costs. With this, we compare eco-efficient vehicle concepts suitable for future transportation.

The megatrends of individualization and sharing will dramatically change our consumer behavior. The needs of a product’s users will be central input for its development. Current development processes are not suitable for this product development; thus, we propose a combination of a genetic algorithm and a fuzzy system for user-centered development. We execute our new methodological approach on the example of autonomous vehicle concepts to demonstrate its implementation and functionality. The genetic algorithm minimizes the required number of vehicle concepts to satisfy the mobility needs of a user group, and the fuzzy system transfers user needs into vehicle-related properties, which are currently input for vehicle concept development. To present this method, we use a typical family and their potential mobility behavior. Our method optimizes their minimal number of vehicle concepts to satisfy all mobility needs and derives the properties of the vehicle concepts. By integrating our method into the entire vehicle concept development process, autonomous vehicles can be designed user-centered in the context of the megatrends of individualization and sharing. In summary, our method enables us to derive an optimized number of products for qualitatively described, heterogeneous user needs and determine their product-related properties.

Increasing automation of road-bound vehicles will lead to autonomous, driverless vehicle concepts. Although vehicle manufacturers are showing visions of such concepts, their design process is still not largely researched. New degrees of freedom will not only eliminate the driver’s workplace, but will also make it possible to pursue secondary activities. Thus, an investigation is needed to determine which customer-relevant properties can be used to describe autonomous vehicle concepts. We present a method to address the new degrees of freedom. The method begins with a definition of the customer structure and leads to the vehicle concept. We also make a proposal for customer-relevant properties of autonomous vehicle concepts. For this, two expert surveys, first expert interviews and second an expert workshop, were conducted and connected with knowledge from the literature. The result is a portfolio of twelve whole vehicle properties. We assert that the developed method with the identified costumer-relevant properties can become the basis for the adaptation of the automotive product development process to autonomous vehicles.

Driving maneuvers try to objectify user needs regarding the driving dynamics for a vehicle concept. As autonomous vehicles will not be driven by people, the driving style that merges the individual aspects of driving dynamics, like user comfort, will be part of the vehicle concept itself. New driving maneuvers are, therefore, necessary to objectify the driving style of autonomous vehicle concepts with all its interdependencies relating to the individual aspects. This paper presents a methodology to design such driving maneuvers and includes a pilot study and a user study. As ab example, the methodology was applied to the parameters of user comfort and travel time. The driven maneuvers resulted in statistical equations to objectify the interdependencies of these two aspects. Finally, this paper provides an outlook for needed maneuvers in order to tackle the entire driving style with its multidimensional facets.

Wie beinahe jede Industrie des letzten Jahrhunderts, ist auch die Nutzfahrzeugindustrie im Wandel. Dabei spielen, die aus dem Pkw-Bereich bekannten, Trends natürlich ebenso eine Rolle. Wir möchten uns hier allerdings auf zwei Trends beschränken, die maßgeblichen Einfluss auf die Treibhausgasemissionen und damit auf die Wahl des Antriebs haben. Zunächst ist das Transportaufkommen ein wesentlicher Unterschied zu Pkw. Diese Entwicklung hat nicht zuletzt zu der CO2-Regulierung der EU und damit unserem zweiten Trend, den CO2-Emissionen, geführt. Vor diesem Hintergrund diskutieren wir die Stärken und Schwächen alternativer Antriebstechnologien. Nach unserer Bewertung durch ein Gesamtkostenmodell, eine Life-Cycle CO2- und Primärenergiebetrachtung sehen wir zwei Möglichkeiten, um die Transformation des Nutzfahrzeugsektors zu bewerkstelligen. Der Erste Weg entspricht dem Konzept der elektrischen Postkutschen, bei dem wir das Logistiksystem auf die Reichweite von elektrischen Nutzfahrzeugen von 500-600km durch Hub-Lösungen und Schnellladekonzepte anpassen. Unsere Alternative ist eine umfassende Wasserstoffwirtschaft mit Brennstoffzellen oder Wasserstoffverbrennungsmotoren, die durch eine Sektorkopplung über das bestehende Erdgasnetz flächendeckend und wirtschaftlich wird. Welcher Weg beschritten wird, hängt von den politischen Entscheidungen in den nächsten Jahren und schlussendlich der Akzeptanz gegenüber den Technologien ab.

This is the code of the Verification Validation and Uncertainty Quantification Framework. For more details see the Readme. The Repository Release v1.0 is in line with the following publication: [1] Benedikt Danquah, Stefan Riedmaier, Yasin Meral, and Markus Lienkamp, “Statistical Validation Framework for Automotive Vehicle Simulations Using Uncertainty Learning”, Applied Sciences, Volume 11, Issue 5, 2021.

The modelling and simulation process in the automotive domain is transforming. Increasing system complexity and variant diversity, especially in new electric powertrain systems, lead to complex, modular simulations that depend on virtual vehicle development, testing and approval. Consequently, the emerging key requirements for automotive validation involve a precise reliability quantification across a large application domain. Validation is unable to meet these requirements because its results provide little information, uncertainties are neglected, the model reliability cannot be easily extrapolated and the resulting application domain is small. In order to address these insufficiencies, this paper develops a statistical validation framework for dynamic systems with changing parameter configurations, thus enabling a flexible validation of complex total vehicle simulations including powertrain modelling. It uses non-deterministic models to consider input uncertainties, applies uncertainty learning to predict inherent model uncertainties and enables precise reliability quantification of arbitrary system parameter configurations to form a large application domain. The paper explains the framework with real-world data from a prototype electric vehicle on a dynamometer, validates it with additional tests and compares it to conventional validation methods. It is published as an open-source document. With the validation information from the framework and the knowledge deduced from the real-world problem, the paper solves its key requirements and offers recommendations on how to efficiently revise models with the framework’s validation results.

Autonomous electric buses (AEB) have widely been envisioned in future public transportation systems due to their large potential to improve service quality while reducing operational costs. The requirements and specifications for AEBs, however, remain uncertain and strongly depend on the use case. To enable the identification of the optimal vehicle specifications, this paper presents a holistic design optimization framework that explores the impacts of implementing different AEB concepts in a given set of routes/network. To develop the design optimization framework, first, a multi-objective, multi-criteria objective function is formulated by identifying the attributes of bus journeys that represent overall value to the stakeholders. Simulation models are then developed and implemented to evaluate the overall performance of the vehicle concepts. A genetic algorithm is used to find the concepts with the optimal trade-off between the overall value to the stakeholders and the total cost of ownership. A case study is presented of a single bus line in Singapore. The results show an improvement in the waiting time with the use of a smaller sized AEB with a capacity of 20 passengers. However, the costs and emissions increase due to the requirement of a larger fleet and the increase in daily distance traveled compared to a 94-passenger capacity AEB.

The launch of both battery electric vehicles (BEVs) and autonomous vehicles (AVs) on the global market has triggered ongoing radical changes in the automotive sector. On the one hand, the new characteristics of the BEV powertrain compared to the combustion type have resulted in new central parameters, such as vehicle range, which then become an important selling point. On the other hand, electric components are as yet not optimized and the sensors needed for autonomous driving are still expensive, which introduces changes to the vehicle cost structure. This transformation is not limited to the vehicle itself but also extends to its mobility and the necessary infrastructure. The former is shaped by new user behaviors and scenarios. The latter is impacted by the BEV powertrain, which requires a charging and energy supply infrastructure. To enable manufacturers and researchers to develop and optimize BEVs and AVs, it is necessary to first identify the relevant parameters and costs. To this end, we have conducted an extensive literature review. The result is a complete overview of the relevant parameters and costs, divided into the categories of vehicle, infrastructure, mobility, and energy.

Electrification and automatization may change the environmental impact of vehicles. Current eco-driving approaches for electric vehicles fit the electric power of the motor by quadratic functions and are limited to powertrains with one motor and single-speed transmission or use computationally expensive algorithms. This paper proposes an online nonlinear algorithm, which handles the non-convex power demand of electric motors. Therefore, this algorithm allows the simultaneous optimization of speed profile and powertrain operation for electric vehicles with multiple motors and multiple gears. We compare different powertrain topologies in a free-flow scenario and a car-following scenario. Dynamic Programming validates the proposed algorithm. Optimal speed profiles alter for different powertrain topologies. Powertrains with multiple gears and motors require less energy during eco-driving. Furthermore, the powertrain-dependent correlations between jerk restriction and energy consumption are shown.

State of the art powertrain optimization compares the energy consumption of different powertrain configurations based on simulations with fixed driving cycles. However, this approach might not be applicable to future vehicles, since speed advisory systems and automated driving functions offer the potential to adapt the speed profile to minimize energy consumption. This study aims to investigate the potential of powertrain optimization with respect to energy consumption under optimal energy-efficient driving for electric buses. The optimal powertrain configurations of the buses under energy-efficient driving and their respective energy consumptions are obtained using powertrain-specific optimized driving cycles and compared with those of human-driven unconnected buses and buses with non-powertrain-specific optimal speed profiles. Based on the results, new trends in the powertrain design of vehicles under energy-efficient driving are derived. The optimized driving cycles are calculated using a dynamic programming approach. The evaluations were based on the fact that the buses under energy-efficient driving operate in dedicated lanes with vehicle-to-infrastructure (V2I) communication while the unconnected buses operate in mixed traffic. The results indicate that deviating from the optimal powertrain configuration does not have a significant effect on energy consumption for optimized speed profiles; however, the energy savings from an optimized powertrain configuration can be significant when ride comfort is considered. The connected buses under energy-efficient driving operating in dedicated lanes may reduce energy consumption by up to 27% compared to human-driven unconnected buses.

The advancement of electric mobility as a measure to comply with international climate targets and sustain renewable resources in the future has led to an electrification of the mobility sector in recent years. This trend has not been spared in the logistics and commercial vehicle sector. Emerging electric powertrain concepts for long-haul vehicles have since been developed and adapted to different use cases and axle concepts. In this paper, the authors show the influence of the powertrain topology and the associated design of the electric machine on the efficiency and energy consumption of commercial vehicles. For this, existing series or prototype long-haul axle topologies are analyzed regarding their efficiency and operating points within four driving cycles. Additionally , a sensitivity analysis on the influence of the total gearbox ratio tests the assumed designs. We find that single-machine topologies offer efficiency advantages over multiple-machine topologies. However, this study highlights a joint consideration of application-specific machine design and to-pology to realize the full technological potential.

This is an original manuscript of an article published by Taylor & Francis in Vehicle System Dynamicx on 07th of December 2020, available online: http://www.tandfonline.com/10.1080/00423114.2020.1854317. During the peer review process, this article was restructured and the tables figures and citations were improved. The automotive development process is highly complex and is getting more diverse with increasing possibilities in modelling, computing and analysing. However, all models are just reflections of reality and can never be totally accurate. That is why since decades the core aim of validation is to have quantitative knowledge about the reliability of a simulation and the trustworthiness of its results. This information is important when selecting a suiting model, creating new knowledge or having support in decision making. The development process in the automotive domain however transforms. Increasing system complexity, increasing variant diversity and efforts to improve the efficiency leads to more complex simulation models and requires virtual vehicle development, testing and approval. This transformation requires more precise reliability quantification. Conventional validation methods are not sufficient, because they cannot take uncertainties of models, parameters, measurements or models into account, which are inevitably involved in the modelling and simulation process. In an extensive analysis this paper shows that statistical verification and validation has a high potential to increase the knowledge about the model reliability. Therefore, more than thirty literature sources considering verification and validation in automotive vehicle dynamics simulation are analysed. Based on this analysis the paper justifies in detail that statistical model validation is necessary, important and crucial to meet the future requirements. Since automotive statistical methods are still at the beginning, the aim is to encourage and enable further investigation by creating a basis through showing their potential and giving deeper knowledge about this topic.

The modelling, simulation and analysis methods in automotive development are in the process of transformation. Increasing system complexity, variant diversity and efforts to improve efficiency lead to more complex simulations and depend on virtual vehicle development, testing and approval across a large application area. Consequently, the new key requirements of modern validation involve more precise reliability quantification of large application areas, achieved with reasonable effort of cost and time. This paper identifies that the neglection of uncertainties, low information in validation results, low extrapolation capability and the resulting small application area are preventing the state-of-the-art validation meeting those new requirements. In an extensive analysis examining more than twenty frameworks in detail, this paper shows that statistical methods exhibit a high potential to remedy these four key insufficiencies. The paper justifies comprehensively that consistent statistical validation is necessary, important and crucial for precise reliability quantification, which enables accurate model selection, knowledge building and decision making in modern automotive vehicle-dynamics simulations. An example is given explaining the basic principle and benefit of consistent statistical validation. Since automotive statistical methods are still at the beginning, the aim is to enable further investigation by showing their potential and providing deeper knowledge about this topic.

The demand for autonomous electric public transport is increasing globally. New vehicle models and variants are increasing the development complexity and hence the overall development time. Therefore, there is a requirement for a vehicle-packaging tool that can translate user inputs into an optimised vehicle package and can be visualised instantly. The aim of this paper is to develop a parametric tool for designing different concepts for electric autonomous buses. The scope of application is intended in the early phase of the vehicle development process to enable a fast and efficient creation of a flexible bus concept. Through a graphical user interface (GUI), the user is able to size and select all the required components for the autonomous vehicle concepts. The vehicle specification is initiated by selection of one of the three classes of vehicle, the desired number of passengers and then the bus interior’s seat arrangement design. An appropriate powertrain and chassis will then automatically be configured in the next steps. The HVAC simulation allows for the design of different components of the refrigeration circuit to ensure proper cabin temperature. For evaluating the concept, the energy consumption is analysed through simulations, and an estimated initial cost of vehicle concepts and individual systems completes the concept. A spider chart summarises all characteristics and offers an overview of the vehicle concept, providing the possibility to compare with other concepts simultaneously. The tool can create 9600 different bus concepts and provides interfaces for expansion.

The demand for autonomous electric public transport is increasing globally. The operational requirement of these autonomous vehicles differs widely. Hence, there is an increase in the demand for different vehicle sizes and configurations. This has led to a number of methods and improvements in the vehicle package development process. This article presents the development of a holistic parametric packaging tool for autonomous vehicles called Autonomous Electric Vehicle Tool (AEV tool). The tool is designed with MATLAB, and via a Graphical User Interface (GUI), the user can input parameter data, which directly adjusts a parametric Computer-Aided Design (CAD) model developed with CATIA software. The overall vehicle dimensions, as well as the size of single components, can be changed, and different topology configurations can be chosen. Moreover, it is possible to visualize the CAD models compare them with each other according to the vehicle’s specifications, such as the external dimensions and weight. The tool is able to develop vehicle concepts for 4 m to 12 m long autonomous electric buses. The developed tool is validated with respect to two autonomous electric buses and ten electric buses. The weight distribution of the major subsystems for different vehicle concepts is analyzed as well. The validation results confirm the accuracy of the assessed weight of the generated concept buses.

Electrification and automation are attracting interest from the public-transportation sector for their potential to improve energy efficiency, cost efficiency, and environmental performance. Singapore is planning to integrate autonomous buses/minibuses into its transportation system by 2030. However, before the island-wide deployment of autonomous vehicles, there is a need to evaluate their effects on sustainability. A study was therefore conducted in Singapore to evaluate the costs and environmental impacts of autonomous electric minibuses, and the results are revealed and discussed here. This paper presents a case study to demonstrate the impacts of replacing human-driven diesel buses with electrified and automated minibuses on life-cycle costs and greenhouse gas (GHG) emissions for seven routes. The vehicles evaluated were a 12‑m human-driven diesel bus, a 6‑m electrified human-driven minibus, and a 6‑m electrified autonomous minibus. First, the impacts of the vehicle concepts on the scheduling were analysed to obtain the operational strategy and passenger occupancy along the route. A life-cycle assessment (LCA) and a total cost of ownership (TCO) analysis were then conducted to compare the fleet-level costs and GHG emissions. The results showed a 43% reduction in total life-cycle cost for the autonomous electric minibus, compared with the 12‑m diesel bus. The life-cycle GHG emissions of the 6‑m autonomous electric minibus were also reduced by 47% compared with the 12‑m diesel bus, despite the fact that a larger number of the former vehicle were required in the fleet.

The electrification of bus-based public transportation contributes to the goal of reducing the adverse environmental impacts caused by urban transportation. However, the penetration of electric vehicles has been slow due to their lower vehicle range and total costs in comparison to vehicles driven by internal combustion engines. By improving the powertrain efficiency, the total costs can be reduced for the same vehicle range. Therefore, this paper proposes a holistic design exploration approach to investigate and identify the optimal powertrain concept for electric city buses based on the component costs and energy consumption costs. The load profiles of speed, slope, and passenger occupancy profiles are derived for a selected bus route in Singapore, which is used in a powertrain design exploration for a 30-passenger vehicle. Six different powertrain architectures are analyzed, together with single and multi-speed gearbox configurations, to identify the optimal powertrain architecture and the resulting component sizes. The powertrain configurations are further analyzed in terms of their influence on the vehicle characteristics and total costs. Multi-motor configurations were found to have better vehicle characteristics and lower total costs in comparison to single rear motor configurations. Concepts with motors on the front and a rear axle could shift the load points to a higher efficiency region, resulting in lower energy consumption and energy costs. The optimal powertrain concept was a fixed-speed two-motor configuration, with a booster motor on the front axle and a motor on the rear axle.

The electrification of the bus-based public transportation contributes to the goal of reducing the environmental impact caused by mobility in urban areas. This paper proposes a holistic design exploration approach to investigate and identify the optimal powertrain concept for electric city buses based on the component costs and the energy consumption costs. A 30-passenger vehicle with a pre-defined driving cycle is investigated to identify the optimal electric powertrain architecture and the resulting component sizes and type. The most cost-effective concept was found to be one with a four-wheel drive configuration with a permanent magnet synchronous motor and a fixed gear transmission on each axle with a power distribution of 22 % at the front axle and 78 % at the rear axle.

Sustainability requires localization of manufacturing sites, especially in developing countries – even when products are previously designed globally. However, the establishment of manufacturing/production locations in these countries poses crucial challenges for companies that do not yet have facilities or experience in developing countries. For this reason, production transfer planning based on verifiable facts is essential. Therefore, we present a framework to develop production transfer requirements based on the methodic proceedings of requirements engineering – discovery, classification, prioritization and specification. We focus on the identification of stakeholders within the transfer processes. In addition, the classification of the requirements, their correlation, and prioritization are included. The application is conducted exemplarily as part of the aCar mobility project, which plans the transfer of an electric vehicle to developing countries. In conclusion, we present a framework to support production transfer planning and show applicability executing the framework on a current project.

Production locations in least developed countries need their own indicators to evaluate sustainability due to prevailing conditions. For this reason, we present a selection and extension of indicators for social and economic sustainability evaluation for production locations in least developed countries. These indicators are identified and tested based on existing sustainability evaluation methods and representative case studies. Since the battery electric vehicle is seen as the future, the case studies results are used to derive recommendations for an all-wheel lightweight commercial electric vehicle production in sub-Saharan Africa. This vehicle was developed to fit the needs of people living in rural regions of sub-Saharan Africa as well as to add local value to economically support the region.

All automotive manufacturers have a process for developing vehicle concepts to create the initial idea of a new vehicle. These processes have been proven over decades. Nevertheless, they are manual processes to design new vehicle concepts. With this paper and the corresponding tool, we would like to show a possibility to integrate customer-relevant properties automatically into the process. On the one hand, this should demonstrate the possibility of process optimization for the automotive manufacturers and on the other hand, it is a useful teaching tool for becoming familiar with the vehicle concept development challenges.

Heavy Duty Truck Platooning promises to reduce the fuel consumption in long haul road transportation. This paper analyzes possible fuel savings, taking different driving scenarios and the energy demand of the vehicles’ cooling system into account. To estimate the aerodynamic drag resistance and radiator airflow, a platoon of European flat nose box trucks with varying gaps has been simulated using 3D computational fluid dynamics. A longitudinal dynamics simulation coupled with a 1D cooling system model was used to determine the fuel consumption. The platoon’s fuel economy in comparison to a reference vehicle travelling alone was evaluated for different road inclinations and frequencies of highway exits. The results show a strong dependency of the fuel consumption on the distance between highway exits, thus limiting the amount of road sections, platooning can be performed on. The study estimates the breakeven distance between platoon formation and breakup for heavy duty truck platooning in order to reduce the overall fuel consumption.

Die Corona-Krise wird den Wandel in der Automobilindustrie beschleunigen und der Elektromobilität noch schneller zum Durchbruch verhelfen. Wer früh begonnen hat, erntet nun die Früchte. Leider wird es aber viele Verlierer geben.