​​​​​​​​​​​​​​Review 2023 - KULI​​​​​​​​​​​​

The transformation of the mobility industry has an deep impact on development methodologies and the way how we will use data over the entire life cycle of an product. How can utilize this new enabler technologies to create a smart and sustainable mobility for our future?

First time right or 100% design first pass yield (DFPY) means that after the virtual phase of the tractor development, only one physical confirmation test is needed. This level of prediction quality is the foundation to handle any more complex future thermal management problem. It requires robust simulation processes, a sound understanding of input data variability, a correlation process and rigorous identification and frontloading of new failure modes.  

After continuously achieving 100% DFPY for more than 15 years, we are ready to attack the next generation of tractor thermal challenges: autonomy and future propulsion systems. They will lead to new levels of system complexity, both for HVAC and cooling, require higher system reliability and drive for even more intense use of thermal analysis.” 

Sauber Motorsport, which competes as Alfa Romeo F1 Team, is a Formula 1 team that has been involved in the sport for over 30 years. With a team of around 500 people, the car is designed and developed in Hinwil (Switzerland). In 2022 the team showed strongly, finishing in 6th position in the 2022 World Championship. 

One of the main challenges in Formula 1 is the limited track and testing time available, coupled with the high level of competition across the grid. Teams must constantly strive to find more lap time in shorter development time to improve their results. This is made even more difficult by the introduction of a budget cap, which requires teams to find the ideal balance between spent resources and lap time potential. Precise simulation tools that allow efficient use of resources are key in gaining lap time. 

Due to the complex nature of the hybrid power units and the constraints from aerodynamic and packaging perspectives, the cooling system layout and simulative modelling are particularly challenging for teams in Formula 1. The cooling circuits include exhaust energy recovery systems, different charge air cooling circuits, multiple electric engines, batteries and the internal combustion engine. Additionally, teams must find ways to maximize car performance without compromising reliability, all the while working within the 3 free practice sessions available before the car setup and cooling configuration must be frozen on the race weekends, with the objective of maximizing car performance. 

To address these challenges, Sauber Motorsport has chosen to use KULI as their 1D simulation software. This software allows for data handling of large input and output files, moderate simulation times, and numerical stable algorithms to run complex cooling arrangements with thermal interaction between circuits. The cooling system simulation of Sauber Motorsport is centred around a 1D simulation model within KULI which contains all cooling circuits and heat exchangers to reproduce thermal interactions. 

To further reduce simulation time, Sauber Motorsport is using a semi-transient simulation approach. This allows the team to benefit from repeating patterns in temperature behaviour, occurring while running the cars on various racetracks. The model is fed by the outputs of various steady and transient sub-models. The team also uses modern data analysis techniques to improve the understanding of the thermal behaviour of components and improve the accuracy of the 1D simulation model. 

In addition to the cooling system layout and radiator design, the focus for Sauber Motorsport is to use the simulation to create data to make very quick decisions during the limited track time available on the race weekends, and to find the best radiator and aerodynamic configuration to maximize race car performance. With this approach, Sauber Motorsport aims to find more lap time benefit in a shorter development time, and to remain competitive in the highly demanding and challenging world of Formula 1 racing. 

Efficient heating and cooling of electric and electronic components is one of the key factors of a well-designed electric vehicle. In passenger cars, increasing fast-charging requirements lead to very high battery pack cooling demands and for sports cars critical temperatures can also occur during high performance driving. In commercial vehicle and off-highway equipment applications, very high load-peaks during operation pose significant challenges for battery cooling, too. One solution is to maximize heat transfer coefficients by immersing electric components directly into the cooling fluid. The French company Wattalps has developed such a modular and stackable battery concept. In an ongoing research activity between Wattalps, MAGMA Giessereitechnologie and Engineering Center Steyr  we are working on an efficient development workflow, where a combination of 3D coolant flow simulation (MAGMASOFT) and 1D system simulation (KULI) allows both the optimization of interior battery flow distributions and integration of the battery pack in the overall vehicle cooling system. In our presentation we want to share some first results from this cooperation.

Climatization of cars, i.e heating and cooling to adjust a desired comfort level for various ambient conditions, usually leads to  high energy consumption and impacts the design of the thermomanagement system: For battery electrical vehicles, energy consumption for heating cases highly decreases the range, and for cooling cases additionally the batterie cooling performance is reduced.  

For the design of a thermomanagement system it is thus inevitable to quantify the energy consumption of a car cabin. Therefore, a simple model is developed in Matlab and compared to the scalable model contained in KULI to systematically analyze the model behaviour with respect to mass flow, solar radiation and other ambient conditions. The overall goal of the method is to define the energy demand needed for the system design based on a prescribed thermal comfort. 

The presented study will also contain the essential background in order to asses the human thermal sensation based on state of the art measurement equipment and the  involved physics. 

Thermal management in modern vehicles (especially BEVs) is a crucial aspect of today's automotive design, as it impacts vehicle performance, battery life, and passenger comfort. The state-of-the-art HVAC systems, among other things, regulate temperature and humidity levels within the cabin and manage battery thermal management to ensure optimal battery performance. 

In this talk we will discuss some modern-day problems of HVAC systems like: 

• Subcooling controlled EXV 
• Combined Heat pump and AC system 
• Hot gas bypass 

We will also give insights on how to tackle these problems with our KULI software to find useful and elegant modelling solutions.  

Hotel loads, such as the HVAC system, can reduce the autonomous range of such a fuel cell multiple unit train by up to 50 %, which reduces train’s availability. The metal hydride refrigerator uses the pressure energy between pressure tank and fuel cell system to drive a heat pump and therefore provides cooling and heating power. The system absorbs hydrogen in an exothermic reaction and desorbs hydrogen in an endothermic reaction in two alternating batch reactors to provide a continuous hot and cold flow.

However, the system’s benefits were only calculated in a passenger car simulation in KULI so far and the advantages in railway use cases are currently unknown.

Here we show a first energy reduction potential by using metal hydrides with calculating the annual energy demand with a co-simulation of the existing and validated KULI metal hydride refrigeration model in combination with a generic car body model in the modelica language.

The KULI model is first exported in a functional mock up model and fresh air precooling and mixed air precooling are investigated as two integration options. The evaluation shows, that a significant reduction in energy demand of the HVAC system by 9 % can be achieved in climate zone 1. Furthermore, additional improvements in the hydrogen pressure levels and an optimized selection of metal hydrides show efficiency increasing potentials are foreseen.

Control system and especially algorithms of controls are essential part of every Battery Thermal Management system that can affect overall vehicle performance and define sizing needed for components utilized. Well-designed control system can benefit on system thermal inertia and reduce weight and power necessary to be secured for operation of system that ensures battery thermal comfort. In our work we utilize KULI to create calculation models of our systems, the component models are based on real measurement of all components made by BSPL . Those models are calibrated to correspond with real machines and help us to predict operation in various ambient conditions that are hard to emulate in laboratory surrounding like i.e., high altitudes. At the beginning Kuli was used to implement simple controls utilizing various operation points and transient loads. This approach worked well, but with time we come to conclusion that more advanced model is needed for controls where we can test how system can utilize thermal inertia, and various more complicated control approach. This is where we decided to utilize Mathworks Simulink ® especially for development for control system. Using Mathworks Simulink ®has several advantages – starting from free design of various control models, through system identification to create mathematical models, finishing with possibility to generate C code that can be utilized directly in Thermal Management ECU.   

In our presentation we will show results of our work in developing control system using co-simulation Kuli & Mathworks Simulink ®. We are going to present models we have created – with some explanation why certain components and relations are modeled the way we have done them. We are goingto present steps that we have taken to calibrate models and resulting simulation values.  

We will also present Mathworks Simulink ®part with explanation of selected approach and possible ways how we can develop created models even further. Additionally, we will be able to present some comparison between model and co-simulation based control system development, and trial and error approach that we had used in the past. 

Public transport is essential in providing efficient and clean mobility to residents and commuters in urban areas. Energy-efficient operation of the related vehicle fleets (buses, tramways) is not only important from an environmental perspective but in the face of rising energy costs also has important financial applications. Energy optimization is complicated because typical vehicle pools have a very heterogeneous structure (different vehicles from different brands and a lot of individual versions and adaptations even for the same vehicle type). Tracking and optimization of vehicle operations therefore requires standardized interfaces to very different vehicles and tools, which allow unified interpretation of drive data.

Magna has developed an integrated solution for these tasks. We are currently performing a field test for the Magna DriveInsights toolchain in cooperation with the local public transport and service provider Linz AG. In our presentation, we want to highlight first results ranging from hardware integration of our vehicle interface to data transfer and analysis in our cloud service. And, of course, we will take a close look at data records and show how energy savings can be achieved by optimized vehicle operation.

In the “digital age”, leadership—in the meaning of the skill for “working on a system/business model”—is of paramount importance. Rapid technological change coming with digitalization enters all aspects of life, so businesses are forced to undertake extensive transformation processes. A strategic and pervasive positioning of information technology (IT) as business enabler and fundament of business models is of essence. Cultural business conflicts with the positioning of other operational units are unavoidable. Traditional methods of management are certainly being tested. The “digital society” needs executives with new competencies and businesses based upon fundamentally changed business cultures.

Address: Am Pöstlingberg 14, 4040 Linz, Austria

Global megatrends not only in mobility but also in the energy system and in society in general have a significant impact on energy conversion and heat treatment. The heat exchanger industry has become the key industry to make these trends happen. 

This presentation highlight five actual megatrends and its impacts on and solutions provided by the heat exchanger industry. 

First ongoing trend is the global population increase, which requires higher food production.  
Second trend is the global prosperity growth, which is followed by more comfort (washing machines, cars, heating and AC system, refrigerators). 
Third megatrend is emission reduction, mainly in CO2 but also in noise emissions, particles and others. This impacts the whole society, domestic, mobile, social and industrial applications. 
The forth megatrend are alternative solutions for mobile applications, e.g. BEVs, FCEVs but also alternative fuels for all kind of transportation, marine, aerospace, onroad and offroad. 
​​​​​​​The fifth megatrend is digitalization which leads to a significant increase in data storage and transfer and will obviously consume a lot of energy. 

In the present world with depleting fossil fuels and growing concern about the environmental impacts, the dependency on alternate energy sources is increasing. Stringent norms on exhaust emissions implies, very complex after treatment systems in conventional IC Engine models and increasing cost of procurement and ownership. Already many OEMs have refined their development strategies towards phasing out of IC Engines and bringing in new technologies like, Fuel Cell vehicles, Battery Electric Vehicles and Hydrogen powered vehicles. Prime focus is on Hydrogen for usage in many ways. Attention is on Hydrogen fueled Internal combustion engine and Fuel cell. In this paper detailed cooling system design is done for Fuel Cell, Battery and Power Electronics system in a Heavy Duty Fuel Cell Truck. Radiator and Fans are selected based on the overall heat rejection and Coolant inlet temperature requirements of components. Cooling system circuit and pump is decided to meet the coolant flow rate targets. The entire cooling circuit along with pipes, bends, pumps, component resistances and heat dissipation characteristics are simulated using simulation software (KULI). Vehicle is tested and values recorded. A good correlation is achieved between the KULI prediction and test result, thus validating the design methodology followed. 

• In partnership with the Hochschule München we have fully electrified a nearly one hundred year old Bugatti 35T with a 48 V electric drive system.  
• The YMER Thermal Management System is cooling the driveline as well as the batteries actively 
• First time a disc valve with variable position has been used 
• YMER is developing and producing Thermal Management Systems for Off-Highway Machines based on a modular platform 
• Kuli is used for full machine simulations in order to reduce the testing effort 

The purpose of the presentation is to demonstrate the benefits of using variable pitch fans. The basis for that are real data measurements (use case). A propriate KULI model will be explained (set-up, 
modeling, parameters,…).

Reversible Fans keep Radiators clean. For that reason, the fan turns the blades from “full cooling” into “full cleaning”. Debris will be blown away, the engine keeps operating in its optimum temperature range.

As a leading company in reversing fan technology, Flexxaire introduced the variable pitch technology. It is called IVP = Infinite Variable Pitch. By varying the pitch (angle), the airflow can be fully controlled and adjusted to the actual load and heat data of the engine. This leads to a significant decrease of power draw of the fan. This IVP-function is the focus of the presentation

The railway construction industry faces a major challenge due to the reduction in CO2 emissions decided by the EU. On the one hand, the use of alternative drive technologies (catenary battery hybrid, diesel battery hybrid, fuel cell battery hybrid, etc.) should contribute to achieving this goal in the long term; on the other hand, the additional costs of these new technologies must be economically feasible. In addition to alternative drive technologies, the optimization of the operating strategy of diesel-electric power trains also represents potential that needs to be exploited. 

In order to guarantee the lowest possible use of resources and the reduction of life cycle costs (LCC), it is necessary to understand the behavior of the drive train system and the individual components in their mutual interaction. 

In this lecture we present a simulation model that represents the system behavior of a diesel-electric powertrain and its components. Based on the results and variant analyses, an operating strategy for the two engines was derived that optimizes the fuel consumption and still fulfills all thermal requirements even under extreme conditions. 

In battery electric vehicles (BEV), the ongoing development of thermal management is of crucial importance to improve energy efficiency and lifetime of the vehicle. The optimal temperature ranges of battery and electric motor are at different levels, resulting in multiple fluid circuits, where methods for waste heat recovery contribute to increase efficiency. These aspects lead to thermal management systems with complex architectures and control strategies. Currently, automotive manufacturers use various cooling concepts with numerous architectures. The investigations at the Institute of Thermodynamics and Sustainable Propulsion Systems are intended to provide expertise regarding control and design of efficient thermal management systems for BEVs. These insights are expected to contribute to the development of superior system architectures for different vehicle requirements.

Due to the interactions between the various fluid circuits, optimization of control strategy and architecture requires an overall vehicle approach. Thus, a full vehicle simulation is set up, analyzing the Tesla Model 3 as a representative vehicle. This simulation allows an optimization of the control strategy of the thermal management system to reduce energy consumption for multiple operating points. The simulation is set up as a co-simulation in KULI and MATLAB Simulink. In KULI, the included libraries of vehicle components based on characteristic maps generally contribute to efficient thermo-hydraulic modeling, while MATLAB Simulink offers a high degree of freedom in mathematical-physical modeling.

The focus of the simulation set up at ITnA is less on modeling efficiency and more on generating in-depth knowledge about abstraction and modeling of thermal management at system and component level. Therefore, components like battery and electric motor are not based on the KULI library but are modeled thermally in MATLAB Simulink with a high level of detail. For instance, the battery is modeled starting from inner cell structure up to pack level in MATLAB. The interface to the coolant circuit in KULI is implemented by transferring the temperature at the contact surface between cooling channel and coolant from the MATLAB model to KULI, while transferring the heat flow between channel and coolant from the KULI model to MATLAB.

As the HVAC system represents a significant auxiliary consumer in BEVs, another focus is on the implementation of a realistic, fully automatic air-conditioning control system. The air path of the HVAC system in KULI represents the "real" air conditioning unit installed in the vehicle, with components such as evaporator and heaters being controlled via a MATLAB control strategy. For this, a feedforward control is implemented, where simultaneously to the "real" air path of the HVAC unit in KULI, mathematical-physical models of individual components are also calculated in MATLAB. These MATLAB models receive target values of the control strategy and sensor signals of the KULI components, from which a feedforward value is calculated and superposed with a feedback control value. This value is transferred to the KULI component as an actuating signal. That methodology is used for the control of heat outputs in evaporator and heaters and also for the position of temperature mixing flaps.

This co-simulation highlights the complexity of thermo-hydraulic modeling of thermal management systems. The scientific approach leads to complex modeling processes. Consequently, the option of using components from KULI library provides a much more efficient modeling, especially in industrial applications. Furthermore, the simulation illustrates the enormous range of functions offered by KULI in the context of co-simulation, and the capability to couple multiple simulation tools as a key strength of KULI.

Some great features have been added to the new KULI versions, which simplify working with KULI and makes it significantly faster to use. We will give you an overview of the extensions and how they make your daily work easier. 

In this presentation we will introduce the Python Controller, which is a component that allows the user to effortlessly integrate Python scripts into thermal simulations with KULI. Then, in order to showcase the power of this component, we will present a wide variety of applications, including co-simulation possibilities, optimization, access to external resources, representations via graphs, and much more. 

While KULI already is the benchmark for easy modelling of VTM systems, we are currently working on the next steps towards even faster model set up and a generally more modern “look and feel”. We will show you, where the journey with KULI is going and how you can benefit from the innovations. 

Address: Landstraße 31, 4020 Linz, Austria

Agenda:

• 09:00 - 10:00 | Part 1
• 10:00 - 10:15 | Coffee break
• 10:15 - 12:00 | Part 2
• 12:00 - 12:45 | Lunch break
• 12:45 - 14:30 | Part 3
• 14:30 - 14:45 | Coffee break
• 14:45 - 16:30 | Part 4

Address: Steyrer Straße 32, 4300 St. Valentin, Austria