The mobility of people and the transport of goods, as key pillars of modern society, require the existence of energy-efficient powertrains with minimal negative impact on the environment. In the foreseeable future, a massive shift from powertrains based on turbocharged internal combustion engines to electric powertrains is expected due to a number of factors. These electric powertrains will be equipped with various subsystems, each of which plays a key role in fulfilling their primary functions. The research objective is to investigate new subsystem concepts for future powertrains. Activities focus in particular on high-speed rotating machines that can serve as compact powertrains, electric compressors for fuel cells, cooling systems for electric vehicles, power concentrators or transmissions for electric vehicles. The aim is to investigate oil-free rotor fits and their effects on entire subsystems, losses in lubrication and sealing systems, stability and vibro-acoustic behaviour of high-speed rotating machines, the use of working fluids for cooling and lubrication of rotating machines including seals, and methods to describe the interactions of moving bodies in working fluids. These results will provide new capabilities that can be applied in the development of passenger and commercial vehicles using electric motors with batteries or fuel cells. The research findings will be openly presented in scientific journals and at conferences so that they have a significant impact on the development of low-emission mobility. 

Most of the emissions footprint associated with today's mobility comes from the energy used to power transport vehicles, which is mainly derived from fossil fuels. The aim of the research activities under the VZ2 research project is to strengthen the application of renewable energy sources and optimise energy storage to support clean mobility in the Czech Republic. Clean mobility is only as clean as the energy sources it uses. Research activities are focused on the use of advanced computer models and simulations to integrate renewable energy sources into clean mobility systems and to integrate energy storage into practical applications for clean mobility. Efforts are focused on innovations in energy storage and exploring possible combinations with alternative energy storage methods such as hydrogen and synthetic fuels. In the context of the use of renewable energy sources to achieve clean mobility, the aim is to identify the most suitable renewable energy sources in the Czech Republic and to analyse in detail their potential and storage options. With an emphasis on eco-efficiency, the entire energy cycle from energy production, transformation and storage to its use in the propulsion systems of transport vehicles will be investigated. The applications considered will involve real innovations that will contribute to a significant reduction of the mobility footprint and promote sustainable development.

VZ4 activities are aimed at the detailed identification of non-exhaust particulate emissions from driving cars. Specific attention will be paid to the characterisation of particles (morphology, size, quantity, composition) generated from abrasively significant wear components of cars. The main attention will be paid to the identification of particle emission from car brakes, which will be carried out both in laboratory conditions taking into account the characteristics of the braking process (initial speed, deceleration dynamics) and by indirect procedures in real traffic. On the basis of the identification of particulate emissions from cars, the development of a simulation model allowing predictive quantification of particulate emissions depending on traffic flow parameters and the structure of the car fleet will be proceeded. The solution will include the development of detailed computational models for the assessment of the dispersion of particulate matter generated by road traffic in geometrically complex areas. The focus will be on the development of computational models to capture the impact of car movements and driving dynamics on the dispersion of the observed emissions. Furthermore, modelling of the resuspension of particles deposited on surface areas will be included, as one of the most important emission contributions of traffic to the concentration of particulate matter in the air. Computational modelling will provide information on the particle concentration fields around characteristic urban transport features. By combining the identification of concentration fields with models of the movement of people around the transport infrastructure, it will be used to assess the exposure of the population to emitted particulates in the urban configurations under consideration, depending on the nature of the traffic flow and its management.

The research activities will develop methods and procedures that can be subsequently implemented in a portfolio of software solutions and that will contribute to the refinement of the prediction of particulate matter emissions from vehicles.

The aim of the project is to increase efficiency in the research and development not only of areas related to electromobility, but also of the electric vehicle as a whole. With the rapid increase in the popularity of electric vehicles and the need to optimize their energy consumption, extend driving range, improve the efficiency of the entire energy process, and enhance crash safety, a comprehensive approach to these issues is becoming increasingly urgent and necessary. Compared to conventional vehicle designs with internal combustion engines, it is possible to implement fundamental changes that can significantly influence the attractiveness of electric vehicles. In addition to the currently used numerical models, which capabilities are continuously improving and substantially contribute to reducing vehicle development time, advanced technologies such as deep learning and virtual environments can also be applied.  

Within VZ3, a sophisticated system will be developed, capable of monitoring and managing the energy flow in an electric vehicle using advanced deep learning algorithms. This system will be able to respond to various factors, such as current operating conditions, battery status, or the surrounding environment, and will be able to optimize energy usage to achieve maximum efficiency and driving range. To determine the situation around the vehicle, communication between the vehicle and its surroundings, referred to as V2X (Vehicle-to-everything), is essential. This enables, for example, the transmission of information on the availability and type of charging stations, or the current traffic situation around the vehicle for optimal route planning. V2X communication also offers the possibility of remote control of certain vehicle functions (such as battery preheating for faster charging or cabin pre-cooling), allows remote software updates, and enables the transfer of diagnostic data on the condition of various vehicle components to the manufacturer or service center. This provides a large amount of data for the development of more advanced algorithms for optimizing energy flow. Another benefit of V2X communication is early warning of hazardous conditions or impending collisions. At the same time, developing an electric vehicle without relying on platforms originally designed for combustion engines brings new opportunities for cabin layout, in-cabin activities, and battery placement, while still maintaining or even increasing safety requirements. Closely related to this is the use of computational modeling, which significantly accelerates the design and verification process. However, due to the lack of experience in this area, there is an enormous need for a high number of sensitivity analyses, which cannot be performed manually. For this reason, the use of artificial intelligence is an attractive solution, enabling the identification of concepts that maintain or even improve safety levels inside the vehicle and in its immediate surroundings.

Another important aspect is the creation of a virtual environment that will simulate real conditions for the electric vehicle or its components. This virtual world will enable extensive tests and simulations without the need for physical test drives, reducing costs and accelerating the development of new technologies in the field of electric vehicles.

Research activities focus on the development of intelligent autonomous transport systems to process data from different sensors and optimise vehicle routes. They focus on improving the description of the environment of autonomous vehicles using different types of sensors while moving in different environments and also in areas where GPS signals may be insufficient or unreliable. The goal of this work is to design and implement features and techniques that will provide more reliable and faster mapping of vehicle environments.

The newly proposed features will build on a combination of depth perception, visual processing and advanced machine learning algorithms, enabling vehicles to identify and interpret their surrounding environment with high accuracy. This should lead to a significant improvement in the safety of autonomous vehicles. Accurate environmental mapping will enable vehicles to better understand and react to their surroundings, minimising the risk of collisions and ensuring safer operation. This enhanced capability of autonomous vehicles to operate with accurate ambient mapping will be key to maximising safety for the vehicles themselves as well as for other road users and the environment in which they operate.

Vehicle environment mapping can also be used to improve the behaviour of autonomous vehicles at intersections. Examples for testing can include selecting different types of intersections, investigating the traffic situation, segmenting intersection areas, creating a safe model for vehicle passage, and investigating the effect of external factors on vehicle handling characteristics. Through this integration, we expect to improve the safety and integration of autonomous vehicles into complex transport environment.

The second part of the intelligent autonomous transport system research will focus on route planning. The aim is to design a system that is able to automatically and dynamically suggest optimal routes for different types of vehicles, taking into account their specific parameters, e.g., current traffic conditions or current road clearance. The system is intended to maximize efficiency, travel safety and to provide users with a reliable and flexible way to navigate in real time.

In cooperation with the partner CDV, it will be possible to use the open source Test Area Catalogue, both for testing and for supplementing it with the information obtained. It is anticipated that the results of the research project will be refined in other projects submitted and commercialised by other companies.