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To optimize the fuel consumption, new concepts like the Thermo Electric Generator (TEG) may show potential. To analyze the possibilities of such system, the layout can be modeled in KULI and different concepts can be compared.
The model consists of an exhaust gas system (hot side) and a water circuit (cold side), whereby the electric power is generated out of this temperature difference. 

The modeling of new concept like the TEG is a challenging task for each engineer. This example shows how such a model can be easily created with KULI “on-board” components. 

Therefore a combination of calculation controllers, maps and circuits is used to describe the behavior of such a component. 

Basically the model consists of two circuits, whereby each of them contains a point mass (hot and cold side of the TEG). Both PM are connected by heat conduction, the area for the heat transfer is calculated by the overall number of elements, the area of such an element and the thickness. 

For the calculation of the TEGs performance, the characteristic is provided by a 2D curve which is based on the temperature difference between hot and cold side.

Due to the little loss heat of modern (diesel) engines during warm-up, PTC (Positve Temperature Coefficient) heaters are used to guarantee the fast warm up of the cabin. The inner resistance of the PTC increases for higher temperatures, therefore the heating power is automatically reduced for high temperatures (caused by high inlet temperatures, low mass flows, …). 

Usually the PTC is positioned next to the conventional cabin heater and is mainly used during the warm up phase of the engine. To reduce the fuel consumption, the PTC is deactivated if the heating power of the engine is sufficient. Due to the characteristic of the inner resistance which will reduce the current and therefore also the heating power for higher temperatures, no additional emergency shut of system is required. 

In this example the model of the PTC is reduced to an air-side point mass, including the thermal inertia and heat transfer coefficient. The amount of heat is calculated by the inner resistance (2D map) and a calculation controller. 

Additionally the heater will be turned off if the cabin (air ducts) temperature exceeds 60 deg.

For higher driving velocities, the fan acts like an additional resistance and reduces the effective cooling mass flow. To avoid that effect, air flaps are included in the shroud. These flaps open in case of an “overblown” fan and the cooling air flow can directly pass by this flaps. 

The air flaps are modeled by area resistances, their pressure loss characteristic is defined by a velocity depended map. To adjust the mass flow distribution between the air flaps and the shroud (fan), additional Build in Resistances (BiR) were used for the lower part of the radiator. The ratio of the resistances will directly influence the mass flow distribution. 

In case of a fan driven air mass flow, the air flaps are closed. Therefore the resistance of the flaps in comparison to the BiR is very high. In case of an overblown fan (usually at high driving speeds) the resistance of the BiR is very high, in contrast the resistance of the open air flaps (area resistance) is quite small.

Due to its thermal inertia, the charge air cooler will show a special transient behavior. Especially if the volume flow or the temperature changes, this influence can lead to deviations in the temperatures, pressure losses, …  The example demonstrates a possibility how the user can take care of this effect and easily include the model in an existing KULI model.

To take care of the charge air coolers (CAC) thermal inertia, point masses (PM) are included. Basically two different types of PM are used in the model:

 -    Point mass at the air side (mass ~ 40% of the CAC overall mass)
 -    Point mass in the circuit (mass ~ 60% of the CAC overall mass)

A separation of the CAC into two parts in combination with 3 point masses (inner side) and 2 point masses at the air side shows a very realistic behavior.
A separation of the inner circuits’ point mass of 10%, 70% and 20% (based on 60% of the overall mass) is recommended. The heat transfer area is calculated from the dimensions of the fins (inner side) and from the CACs’ net dimensions (air side). For the heat transfer coefficient a separation into inner and outer side is recommended.

This subsystem calculates the averaged consumption per 100 km. The input data is based on the current fuel consumption of the engine model and the track length. To convert the consumption from kg/s to a volume flow (liters per 100 km), it’s necessary to input the fuel density.

This subsystem demonstrates how easy the actual fuel consumption of the engine can be converted into fuel consumption in liters per 100 km. Therefore a PID controller containing an Integrator is used to get the overall consumption in kg. To convert the mass into a volume, the value is divided by the fuel density (user input). Also the track length – which is a necessary input for the simulation – can be calculated by integrating the driving velocity (if necessary this must be additionally added by the user). 

The output of that subsystem is the overall fuel consumption [kg] and the fuel consumption per 100 km.

This subsystem calculates the heat flows in the cabin model. The system provides the amount of heat to the outlet air, the effective cooling power and the Heat flow to the ambient.
The calculation is done in the subsystem, whereby some additional data like the heat transfer values to the ambient must be input by the user. 

This subsystem demonstrates how the heat flow in a cabin can be calculated. Three different types of heat flow are calculated:

 - Heat to outlet air: The amount of heat  lost via the discharged air 

 - Effective cooling power: How much heat is effectively used for the cool down of the air flow

 - Heat flow to ambient: The amount of heat exchanged between the cabin and the ambience 

Due to the fact that some of the values are defined directly in the component, they are not available as sensor. Therefore external controller inputs can be used to set these values by user defined constants. All other necessary information is connected by using the sensor path.
Several calculation controllers (in combination with media components) are used to calculate the amount of exchanged heat.

In KULI the user can choose between different ways how to model a battery. They mainly differ in the necessary amount of input data, the effort for the creation of the model and also in the level of detail of the results.
This example shows in a detailed way how to model an energy storage. The battery contains cells & modules, as well as the multi-dimensional housing. The electric model is based on a cell level R-C model, which also considers the capacitive behavior. 

This example demonstrates the advanced modeling concept for a thermal-electric battery model. Each of the 8 modules is housed in the battery and includes 12 cells (overall 96 li-Ion pouch cells including the cooling plates). Beside the thermal inertias of the cells themselves, also a multi-directional model of the housing is included. Each side of the housing as well as the cells can be connected by heat conduction. To simulate the electric behavior, a cell based R-C model is used. On the one hand this model describes the electric properties (like SOC, open cell voltage, ..), on the other hand  the thermal behavior of the cells. Beside averaged values, each cell temperature can be accessed. For that reason the model shows excellent opportunities for battery layout design and of course also for the development of the battery cooling system.

In KULI different ways how to model a battery exist. They mainly differ in the necessary amount of input data, the effort for the creation of the model and also in the level of detail of the results.
This example shows the most reduced way how to model a battery. Therefore all cells, modules and housings of the battery are reduced to one single lumped mass model for the use in KULIs cell model.  

This example loosely describes a liquid cooled Li-Ion traction battery with around 290 cells and an overall weight of 180kg. By the reduction to a lumped mass / cell model, the whole battery is reduced to a single mass with an averaged thermal heat capacity (cp value). The heat transfer surface is the sum of all single heat transfer surfaces (each cell is in contact with a liquid cooling plate), the heat transfer coefficient is modeled as function of the flow velocity. To convert the mass flow to a flow velocity, the overall cross section must be defined in the component parameters window. 

Due to simplicity, the electric properties are reduced to a constant value resistance model. The values are based on the battery characteristics. 

This way of modeling a battery is very fast and effective, with the limitation of getting averaged values. Therefore it’s very useful for the estimation of the (transient) heat input in the cooling system and for simulating the influence of the batteries’ inertia, but not for designing the batteries’ inner layout.

This example shows a quite simple way how hysteresis can be modeled. The calculation controller differs between two cases, with respect on the previous operating state. To avoid that the temperature / fan RPM is oscillating around a certain value, a function is used. 

To avoid a highly oscillating fan RPMs, a hysteresis in the controlling strategy is included. This is modeled by a calculation controller. If the temperature exceeds a certain value, the fan switches to the maximum RPM mode.  Additionally this high RPM mode is also used, if the temperature is between the two temperature limits and the high RPM cooling mode is already active. In case of underestimating the lower activation border or in case of an active min. RPM mode & actual temperature between the limits, minimal RPM mode is selected. To avoid a logical loop, a delay controller is used for sensing the input speed of the fan.

One possibility to set a target temperature in the cabin is to control the RPM of the blower (fan).
This example demonstrates how a subsystem including several calculation controllers can easily be added to an existing HVAC simulation system.

By adjusting the fan RPM, the cool down (heat up) performance of the cabin is influenced. This controlling strategy is included in a subsystem which mainly consists of calculation controllers.

As a necessary input, the user has to define a required cabin temperature and the upper and lower limit for the fan RPM. The calibration coefficient is a kind of RPM offset for the controller, used in each simulation time step. 

If the average cabin temperature exceeds the upper temperature limit plus the temperature tolerance, the max. fan RPM is used. 

In all other cases the fan RPM is reduced or increased by the calibration coefficient. Due to the change of the fan RPM in each simulation time step, a smooth control characteristic is created.

One possibility to set a target temperature in the cabin is to control the amount of recirculation.
This example demonstrates how a subsystem including several calculation controllers can easily be added to an existing HVAC simulation system.

By adjusting the recirculation rate, the cool down ( heat up) performance of the cabin is influenced. This controlling strategy is included in a subsystem which mainly consists of calculation controllers. 

As a necessary input, the user has to define a required cabin temperature and the upper and lower limit for the recirculation rate. The calibration coefficient is a kind of recirculation offset for the controller, used in each simulation time step. 

If the average cabin temperature exceeds the upper temperature limit plus the temperature tolerance, the max. recirculation rate. 

In all other cases the recirculation rate is reduced or increased by the calibration coefficient. Due to the change of the recirculation in each simulation time step, a smooth control characteristic is created.

These two subsystems can be used to calculate the amount of heat exchanged by a heat exchanger. For HVAC components (evaporator), the subsystem “EVP heat calculation” computes the amount of latent and sensible heat. For all other heat exchangers, the subsystem “Heat calculation” can be used.
Due to their modeling as a subsystem, both models could easily be used in the users KULI simulation. 

Both subsystems can be included in any existing KULI file. To insert it, use the subsystem import function. It might be possible that the signal receivers included in the subsystem must be adapted. Therefore double-click the signal receiver symbol and change the linked component. 

Once successfully included, the system calculates the latent and sensible heat (subsystem “EVP heat calculation”). In case of not using it in an HVAC system, subsystem “Heat calculation” can be used to calculate the sensitive heat (which is also directly available by the component). 

 The calculation of the heat is done by several calculation controllers, the material properties (for the cp value) are computed by the Medium controller. To get the input values for the calculation controllers (mass flow, in- and outlet pressure / temperature), signal receivers are used.

This cavitation alert shows if the critical pressure is underestimated and cavitation can occur. This simple example does not consider local effects in the pump, but the inlet pressure.

Cavitation is a very critical parameter for the pump, because it can lead to its mechanical destruction. Therefore it’s important to investigate the critical parameters. This simple submodel compares the inlet pressure at the pump with the critical pressure for the used medium. In case of underestimating this value, the result of the calculation controller will show that there is the risk of cavitation. Due to the 1D model, this alert does not consider local effects in the pump itself.

This simulation model demonstrates how a HVAC system featuring different operating modes (cool down, heat pump mode) can be realized in one simulation model.

To combine both conditioning modes in one simulation model, it is necessary to split the system into two branches. Therefore the system consists of two condensers and two evaporators.

Basically there are two ways to select the conditioning mode:

• By setting one branch containing these HVAC components to an inactive state (locking the path), the conditioning mode can be chosen. This is basically done by reducing the pressure in the branch to zero.
• Another possibility is to set a very small mass flow through a controlled orifice, which will cause a closed txv.

The mode itself can be chosen in the Simulation parameter window.

Phase change material can store a high amount of energy due to its very high thermal inertia. This energy can e.g. used for a fast engine warm-up, to provide cooling performance in the HVAC system (evaporator) while no compressor is available, … 

The high amount of enthalpy for the phase change is modeled by changing the point masses cp value (thermal capacity) during the melting / solidification process. 

The heat transfer to the PCM element depends on the amount of mass flow. By adding an additional medium component and a calculation controller, a speed / volume flow dependency can be modeled.

One possibility to set a target temperature in the cabin for a fixed displacement compressors is to control the compressors RPM.
This example demonstrates how a subsystem including several calculation controllers can easily be added to an existing HVAC simulation system.

By adjusting the compressor RPM, the performance of the compressor is controlled. This controlling strategy is included in a subsystem which mainly consists of calculation controllers.

As a necessary input, the user has to define a required cabin temperature and the upper and lower limit for the compressor RPM. The calibration coefficient is a kind of RPM offset for the controller, used in each simulation time step.

If the average cabin temperature exceeds the upper temperature limit plus the temperature tolerance, the max. RPM of the compressor is used.

In all other cases the RPM is reduced or increased by the calibration coefficient. Due to the change of the RPM in each simulation time step, a smooth control characteristic is created.

This subsystem takes care that the cabin temperature is kept between specified borders and if the temperature in the cabin is reached, then the entire AC circuit is turned off. Additionally the inertia of evaporator is taken also into account.

This simulation features a control system in which is the cabin temperature kept in specific borders by turning on and turning off the entire AC systems.

When the AC system compressor is turned off and the air still flows through an evaporator the inertia of the evaporator causes the air to cool itself by rejecting heat and at the same time warming the evaporator until it reaches the ambient temperature. In order to take this phenomenon into account, this system was created.

Also in this case is the transient behavior of an evaporator simulated by a point masses. In order to reach a smoother outlet temperature curve a heat conduction coefficient is positioned between the evaporator air side point mass and the evaporator refrigerant side point mass.

KULI software for energy management optimization gives you the opportunity to efficiently investigate different concepts for EV/HEV batteries.

A possible concept  is a nickel metal hydride battery which can be cooled by passenger cabin air.

Focus on

• Temperature distribution among cells

Other results

• Battery SOC
• Battery discharge time
• Finding optimum strategy for blower 

Input Data

• Dimension of cells and battery design
• Battery internal resistance over SOC and temperature
• Initial charge
• Electric Current
• Ambient air flow

Input data loosely based on Honda Insight

• www.insightcentral.net/encyclopedia/enbattery.html

• KULI base and KULI drive required for Simulation

The Subsystem can be used for the calculation of the operating distance of a vehicle. Basically it can directly be used for electric vehicles, but with slight modification also for conventional combustion engines.
The calculation is done in every time step. Due to the fact that the Operating distance is based on averaged values, the accuracy of the result increases with the amount of simulation steps.

Necessary input values are:

• Current SOC (State of ChargeI
• Initial SOC
• Minimal SOC
• Velocity of the vehicle

Output:

• Expected remaining range
• Expected total range of vehicle
• Driven distance (up to now)

The gearbox delivers a significant amount of heat to the gearbox oil, depending on the efficiency of the gearbox in the current operating conditions. This model demonstrates how the amount of heat can be calculated and put into the appropriate locations in the circuits.

The central element is an efficiency map, based on gear, torque, rpm, and temperature. The conversion from mean eff. pressure to torque is included with the help of calculation controllers. The heat of the gearbox is put into a point mass in an oil circuit. The point mass is also connected to another point mass via a heat conduction component with which it is possible to consider the heat transfer to the ambient.

The COM interface, developed by Microsoft®, provides a standardized interface for programs to communicate with each other. KULI components has a set of built-in COM commands, which allows external programs to create KULI component files.

The part of KULI components that allows to save component files is implemented as a dynamic link library (DLL). This KuliCompInterface.dll can be called by any other application that supports COM. The current example is a simple demonstration of the KULI compinterface. An Excel datasheet for KULI input of a radiator component has an integrated button that allows to store the data directly as an *.kulirad-file.

Only transient simulation allows using the full potential of computer aided engineering regarding component sizing & packaging.
For the simulation of realistic temperature profiles the thermal capacities of the engine should be considered.

In this example the existing engine component from KULI drive is remodeled using the primitive components point mass and heat conduction. The engine can be modeled as two direct masses and two indirect masses.The direct masses are heated by combustion processes, and exchange heat with each other, the oil and the water circuit respectively, the ambient air and with the indirect masses. The indirect masses exchange heat via conduction with their respective direct mass only.

In this example we will investigate, how to model a hybrid passenger car with KULI. We will especially focus on the electric components and their integration into the overall cooling system.

Focus on

• Battery model (module level)
• Module temperatures
• Coolant temperatures
• State of charge Simulation
• Electric motor and power electronics
• Cooling system integration

The aim of a decent fan control strategy is to provide adequate air flow for the cooling system at minimum fan power and noise.

The KULI controller objects enable to implement an arbitrary  control strategy for system optimization. 

In the subsystem Fan control the controlling information is set up. For comfortable use you can change the values for the temperatures for changing the fan stage and the fan stages itself in the inner circuit window. 

Based on the fan (electrical or mechanical fan) the fan stage or the fan speed can be used as the controlled parameter. In the provided model an electrical fan switches on as the air temperature rises above 60°C and switches off as the temperature falls below 55°C. The transient aspect of the example is the hysteresis which can be modeled using a delay controller. For better system overview the control strategy is packed into a KULI subsystem.

In the coolant circuit the thermostatic valve is one of the most important control units to maintain the system’s desired set point temperature. Due to its mechanical technology usually the thermostat has some delay in its reaction, which should be considered in transient cycle simulation. This example is based on the tutorial example ExEngine. The major modification is that it includes a more detailed model for the thermostat, placed in the subsystem “Thermostat”.

The main idea is that a fluid point mass models the wax element including the metal housing of the thermostat. This point mass is responsible for the hysteresis of the thermostat. The mass of the point mass can be adjusted to fit the current application; moreover, also the heat transfer coefficient from the coolant to the mass can be adjusted, even depending on the flow rate. A corresponding characteristic line is prepared (but contains only a single value in this demo example). 

The temperature of the mass (i.e., of the wax element) is then taken into a characteristic line in which the lift opening (between 0 and 100%) of the thermostat is calculated. Based on this lift opening two fluid resistances are calculated that have opposite behavior: If the temperature is still low, then the resistance of the bypass will be low, the resistance of the exit to the radiator branch will be high. If the temperature is high, then it is vice versa. 

The example is given for a thermostat working as a branch; the method would work in the same way for a thermostat working as a confluence.