Hybrid vehicles have additional heat sources, and their components operate over different temperature ranges. The cooling system must be able to regulate the temperature of the various vehicle components, which often requires multiple cooling loops. There is no universal architecture for cooling systems, but it is possible to identify macroscopic trends, such as the use of liquid cooling solution for batteries. According to the system modeling approach, the system here is the HEV powertrain system and the cooling system is one of the subsystems.
The image below illustrates the decomposition into system and subsystem of an HEV vehicle as used in Dessia's system modeling approach described in this document. At the Powertrain system level, the choice of its functional architecture, as well as its dimensioning and the control of its components, allow the definition of the requirements for the cooling subsystem. From these requirements, a functional architecture of the cooling subsystem is identified, as well as the dimensions and control of the different components (pumps, radiators, etc.). The functional architecture defines the arrangement of the different components and the number of independent loops (i.e., the topology).
Figure 1 : System modeling used in this chapter
The results of the powertrain and cooling subsystem design allow for the evaluation of the main vehicle level objectives, i.e. energy consumption, CO2 emissions and cost, while respecting the integration constraints. Control of the actuators (i.e. hydraulic valve(s), pump(s), etc.) is important to meet the requirements and optimize the objectives.
With Dessia's system modeling, the cooling system of an HEV vehicle can be broken down as follows:
components to be cooled or heated :
internal combustion engine
components capable of supplying or capturing heat :
aerotherm (exchanger allowing to regulate the temperature of the passenger compartment)
components capable of moving a heat transfer fluid :
components capable of transporting heat :
hydraulic junction Tee
The definition of the interfaces (number of possible hydraulic inputs and outputs) of each of these components allows to generate, via a generative algorithm, the set of possible combinations of connections between these components. Each combination can be represented as a graph. The image below shows three different possible graphs allowing to connect the different electrical components of the hybrid drive train.
Solutions (a) and (b) represent architectures where the components are connected in series with different permutations. In solution (c) the two components battery and charger are positioned in parallel, which can improve the permeability of the circuit.
Figure 2: Examples of possible graphs for electrical components - low temperature circuits
High temperature circuit architectures regrouping the thermal components of the HEV drive train are presented in the illustration below. It is possible to play on the number of pumps present in the circuit to optimize the thermal management. Solution (c), for example, uses a second pump around the heater to heat the passenger compartment in the electrical phase (no heat input from the ICE) via an immersion heater (PTC).
Figure 3 : Examples of possible graphs for thermal components - High Temperature circuits
For each functional architecture generated, the choice of sizing and control of the components characterizing the thermal system (especially pumps and radiators) allows to estimate the energy performance as well as the cost of the solution.
Simplified sizing and control is generally preferred when looking for a design solution. Simplified hydraulic pump and radiator sizing is often based on energy performance maps.
A rule-based control is commonly used to define a simplified control. In an HEV vehicle cooling system application, a rule-based control can drive the flow of coolant through the various branches of the system.
The flow distribution can be based on thresholds defined by system state variables, such as:
battery state of charge
cabin heating demand
For example, for solution (c) different circuit configurations can be obtained depending on the cabin heating demand and the coolant temperature. The configuration c1 can meet a cabin heating demand when the coolant temperature is low (i.e. during the ICE warm-up phase). Then the configurations c2 and c3 allow to regulate the coolant temperature.
Figure 4: Examples of possible configurations for solution (c) - simplified controls for high temperature circuits
In conclusion, this section has described how Dessia's Generative Engineering method can be applied to the research and evaluation of functional architectures for HEV cooling systems. The evaluation step allows for the pre-selection of functional architectures to be retained for the rest of the design process of cooling systems for hybrid vehicles, namely the 3D architecture and the technical specifications of the components.
The search for 3D architectures consists in exploring the possibilities of placing the various components and hydraulic circuits in an allocated volume. The technical specification consists, for example, in defining the valves allowing the realization of the different circuit configurations.
Finally, the presented method can also be applied to the research of cooling systems for electric vehicles (BEV). Indeed, the large number of components to be cooled or heated (battery, DCDC, inverter, charger, electrical machines, etc.) as well as their authorized temperature range makes the design of a cooling system for a BEV very complex.
In order to make the engineering process more efficient and increase the performance of its automotive products, Renault has entered into a collaboration with the French startup Dessia Technologies to test and use its Generative Engineering software solution.
Renault has developed various Generative Engineering applications ("Bots") for the generation of electrical harness architectures, but also for the technical definition of the thermal system architecture of a platform with a hybrid powertrain. Thanks to these applications, it is now possible to produce a hundred eligible solutions with a time saving of more than 80%, using at least 3 times less resources. Regarding the architecture of the thermal system, the objective was to optimize the energy required for cooling, the layout of the main components, the length of the hoses and the other networks, both electrical and air conditioning.
Figure 5: Generative Engineering with functional modeling and 3D integration
For this, the first step consisted in a functional approach as described in the previous paragraph. The objective is to generate and optimize a panel of functional water circuit architectures based on the cooling system requirements. The hydraulic permeability of each solution is optimized on cycle in order to minimize the energy expenditure to meet the cooling requirements.
On the HEV vehicle studied, the study allowedto make impact analyses of several modifications of the water circuit architecture, such as, for example, to evaluate the impact of cooling the battery either by the air conditioning rather than by the water circuit. measure the difference in length and cost of the water circuit function in a few minutes without having to trace any hoses manually; a reduction in the cost of water circuit components up to 40%.
This paper describes how Dessia's Generative Engineering method can be used for the automatic search and evaluation of functional architectures for thermal systems. First, a generic approach is proposed to deal with any thermal system, then the approach is declined for an application to hybrid vehicle cooling systems. The paper also explains how this method has been applied at Renault to optimize the cooling system architecture of an HEV vehicle, resulting in about 100 eligible solutions with a time saving of more than 80% and a reduction in the cost of the water circuit components of up to 40%.
Once the candidates for the functional architectures are selected, the research of 3D architectures (layout/design) as well as the technical specification of the components are performed. These two activities of the design process can be conducted using Dessia's Generative Engineering method. The search for 3D architectures will be dealt within a part 2 to be published later.
Concerning the technical specification of the components of a system or subsystem, their design specifications are defined from the dimensioning of these components obtained at the level of the definition of the subsystem or the system. Thus the technical specification of components such as the battery pack, the electrical machine(s) is derived from the architecture study of the Powertrain system and that of the pump(s) and heat exchanger(s) is derived from the architecture study of the cooling subsystem. Generative Engineering can then be used to find the best technical definition concepts for these components.