Growing vehicle variant diversity, legal requirements to reduce fleet CO₂ emissions and innovations in the area of drive train technologies, coupled with the increasing pressure to cut costs, pose new challenges for parties in the automotive sector. An implementation of optimized development and production processes supports the effective handling of these challenges. One important aspect includes engineering efficiency improvement by optimizing the entire automotive bodywork development process and the involved data management. Research activities focus on the data exchange processes between design, simulation and production engineering within various CAx environments. This concerns constantly changing boundary conditions and requirements in the area of automotive body development, including but not limited to the introduction of new materials and material combinations and new types of joining technologies. From the viewpoint of an automotive engineering supplier, additional challenges caused by different customer-related development environments have to be considered. To overcome these challenges, various data exchange strategies between OEMs (Original Equipment Manufacturer), automotive suppliers and the use of different data management tools need to be investigated. In this context, the paper presents an approach of an optimized data exchange process of CAD-based data between different CAD (Computer-Aided Design) and CAM (Computer-Aided Manufacturing) environments that supports the entire body development, including data provision for manufacturing engineering. In addition, an optimization of data exchange processes saves development costs and improves the product quality.

The lifetime of traction battery systems is an essential feature of the economy of battery electric urban bus fleets. This paper presents a model for the analysis and prediction of the lifetime of urban electric bus batteries. The parameterization of the model is based on laboratory measurements. The empirical ageing model is an integral part of a three-stage battery model, which in turn is an important component of the methodology for the overall system design, evaluation and optimisation of battery electric urban bus fleets. In an equidistant closed simulation loop, the electrical and thermal loads of the traction battery are determined, which are then used in the ageing model to calculate the SOH (state of health) of the battery. The closed simulation loop also considers the effects of a constantly changing SOH on the driving dynamics of the vehicles. The model for lifetime analysis and prognosis is presented in the paper, placed in the context of the overall system design and demonstrated by means of a practice-oriented example. The results show that the optimal system design depends, among other things, on whether an ageing simulation was used. Taking battery aging into account, system costs in the example presented can be reduced by up to 17 %.

Hybrid electric powertrains in automotive applications aim to improve emissions and fuel economy with respect to conventional internal combustion engine vehicles. Variety of design scenarios need to be addressed in designing a hybrid electric vehicle to achieve desired design objectives such as fuel consumption and exhaust gas emissions. The work in this paper presents an analysis of the design objectives for an automobile powertrain with respect to different design scenarios, i. e. target drive cycle and degree of hybridization. Toward these ends, four powertrain configuration models (i. e. internal combustion engine, series, parallel and complex hybrid powertrain configurations) of a small vehicle (motorized three wheeler) are developed using Model Advisor software and simulated with varied drive cycles and degrees of hybridization. Firstly, the impact of vehicle power control strategy and operational characteristics of the different powertrain configurations are investigated with respect to exhaust gas emissions and fuel consumption. Secondly, the drive cycles are scaled according to kinetic intensity and the relationship between fuel consumption and drive cycles is assessed. Thirdly, three fuel consumption models are developed so that fuel consumption values for a real-world drive cycle may be predicted in regard to each powertrain configuration. The results show that when compared with a conventional powertrain fuel consumption is lower in hybrid vehicles. This work led to the surprisingly result showing higher CO emission levels with hybrid vehicles. Furthermore, fuel consumption of all four powertrains showed a strong correlation with kinetic intensity values of selected drive cycles. It was found that with varied drive cycles the average fuel advantage for each was: series 23 %, parallel 21 %, and complex hybrids 33 %, compared to an IC engine powertrain. The study reveals that performance of hybrid configurations vary significantly with drive cycle and degree of hybridization. The paper also suggests future areas of study.

The paper presents test methods (mechanical, electrical and optical) for the fuel spray research in combustion engines. Optical methods, imaging and non-imaging can be used in laboratory and engine tests. Imaging methods include flash photography and holography. Their use is limited to testing droplet dimensions larger than 5 µm. Imaging methods have an advantage over non-imaging ones because they allow the droplet to be seen at the point and time where its measurement is required. Non-imaging methods can be divided into two groups: the first, which counts and measures, individual droplets one at a time, and the second, which measures a large number of droplets simultaneously. Exemplary results of research of droplet size distribution in fuel sprays are shown. In tests of atomized fuel spray, in conditions reflecting the conditions of the internal combustion engine, the size of droplets, their distribution in the spray and the velocity of individual droplets are presented. To determine the quality of the fuel spray, two substitute diameters Sauter (D₃₂) and Herdan (D₄₃) were selected, the first of which refers to heat transfer and the second to combustion processes. Laser research equipment including Particle Image Velocimetry laser equipment (PIV), Laser Doppler Velocimeter (LDV) and Phase Doppler Particle Analyzer (PDPA) were applied for testing fuel spray distribution for two kind of fuel. The atomization process from the point of view of combustion and ignition processes, as well as emission levels, is characterized by the best substitute diameter D₄₃, which value is close to the median volume. The most harmful droplets of fuel in the spray are large droplets. Even a few such droplets significantly change the combustion process and emission of toxic exhaust components, mainly NO_x.

During the early development of a new vehicle project, the uncertainty of parameters should be taken into consideration because the design may be perturbed due to real components’ complexity and manufacturing tolerances. Thus, the numerical validation of critical suspension specifications, such as durability and ride comfort should be carried out with random factors. In this article a multi-objective optimization methodology is proposed which involves the specification’s robustness as one of the optimization objectives. To predict the output variation from a given set of uncertain-but-bounded parameters proposed by optimization iterations, an adaptive chaos polynomial expansion (PCE) is applied to combine a local design of experiments with global response surfaces. Furthermore, in order to reduce the additional tests required for PCE construction, a machine learning algorithm based on inter-design correlation matrix firstly classifies the current design points through data mining and clustering. Then it learns how to predict the robustness of future optimized solutions with no extra simulations. At the end of the optimization, a Pareto front between specifications and their robustness can be obtained which represents the best compromises among objectives. The optimum set on the front is classified and can serve as a reference for future design. An example of a quarter car model has been tested for which the target is to optimize the global durability based on real road excitations. The statistical distribution of the parameters such as the trajectories and speeds is also taken into account. The result shows the natural incompatibility between the durability of the chassis and the robustness of this durability. Here the term robustness does not mean “strength”, but means that the performance is less sensitive to perturbations. In addition, a stochastic sampling verifies the good robustness prediction of PCE method and machine learning, based on a greatly reduced number of tests. This example demonstrates the effectiveness of the approach, in particular its ability to save computational costs for full vehicle simulation.

This paper proposes a way to determine a rational connection between a two-section or three-section brake valve and the main brake system circuits of a wheeled vehicle equipped with simplex or duo-duplex brake gears. The aim is to determine a rational option to connect main brake system circuits with a brake valve to guarantee the maximum effectiveness of an emergency brake system if any device of the brake actuator of a multiaxle wheeled vehicle is out of order, using the methods of combinatorics. The feature of the proposed way, determining the rational connection of a brake valve with the main brake system circuits of a multiaxle wheeled vehicle, is the application of combinatorics methods, taking into account the features of a brake valve design. New mathematical interconnections between the quantities of the connection points of circuits and simplex and duo-duplex brake gears enable us to determine the rational scheme of the application of the brake gear circuits of a multiaxle wheeled vehicle. Mathematical expressions that enable us to compute the number of possible connection points of two-section and three-section brake gears and simplex and duo-duplex brake gears, which are installed on the axles of a multiaxle wheeled vehicle, have been determined. It is proposed to use a universal mathematical dependence to determine the number of rational options to connect the brake actuator circuits of a multiaxle wheeled vehicle. The diagram of the calculated scheme of the connection between circuits and the two-section or three-section brake valve of simplex or duo-duplex brake gears is presented. It simplifies the determination of the number of rational options to connect brake actuator circuits with the brake gears and brake valve of a multiaxle wheeled vehicle. In review the analysis of requirements to be fulfilled in accordance with international standards is provided and the evaluation of the effectiveness of the emergency brake system of some wheeled vehicles is carried out. The general concept to determine the rational layout of the emergency brake system of a multiaxle wheeled vehicle on the basis of the arrangement schemes of the connection between two-section or three-section brake valves and simplex and duo-duplex brake gears is proposed.

The paper describes the process and results of the development of the light commercial electric vehicle. In order to ensure maximum energy efficiency of the developed vehicle the key parameters of the original electric motor. The article also presents the results of power electronic thermal calculation. For the mathematical model of the vehicle, the driving cycle parameters of the electric platform were determined in accordance with UNECE Regulations No 83, 84. The driving cycle was characterized by four successive urban and suburban cycles. The mathematical model also takes into account the time phases of the cycle, which include idling, vehicle idling, acceleration, constant speed movement, deceleration, etc. The model of the electric part of the vehicle was developed using MatLab-Simulink (SimPowerSystems library) in addition to the mechanical part of the electric car. The electric part included the asynchronous electric motor, the motor control system and the inverter. This model at the output allows to obtain such characteristics of the electric motor as currents, flows and voltages of the stator and rotor in a fixed and rotating coordinate systems, electromagnetic moment, angular speed of rotation of the motor shaft. The developed model allowed to calculate and evaluate the performance parameters of the electric vehicle. Technical solutions of the electric vehicle design were verified by conducting strength calculations. In conclusion, the results of field tests of a commercial electric vehicle are presented.

With the large-scale application of electric vehicles (EV) in the world and also in China, the contradiction between the EV and charging stations has become more and more prominent. People always cannot easily find the charging stations or when they find them finally found they do not work. To connect the vehicle, charging station/pile and end-users for making the charging simple, convenient, efficient and visible is becoming very important. People need a platform to tell them where, when and how to charge for their EV. Matrix Mobility is focusing on realizing this comprehensive charging solution together with OEM, charging point operator (CPO), electric power company and parking lots by using big data analysis. Matrix Mobility installs the charging solution into the car unit before cars go off production line and meanwhile integrates the same function into OEM’s own APP with opening API to help end-users increase their charging experience.

Roundabouts provide safe and fast circulation as well as many environmental advantages, but drivers adopting unsafe behaviours while circulating through them may cause safety issues, provoking accidents. In this paper we propose a way of training an autonomous vehicle in order to behave in a human and safe way when entering a roundabout. By placing a number of cameras in our vehicle and processing their video feeds through a series of algorithms, including Machine Learning, we can build a representation of the state of the surrounding environment. Then, we use another set of Deep Learning algorithms to analyze the data and determine the safest way of circulating through a roundabout given the current state of the environment, including nearby vehicles with their estimated positions, speeds and accelerations. By watching multiple attempts of a human entering a roundabout with both safe and unsafe behaviours, our second set of algorithms can learn to mimic the human’s good attempts and act in the same way as him, which is key to a safe implementation of autonomous vehicles. This work details the series of steps that we took, from building the representation of our environment to acting according to it in order to attain safe entry into single lane roundabouts.