Powertrain and Thermal System Simulation Models of a High Performance Electric Road Vehicle

Stellato, Massimo; Bergianti, Luca; Batteh, John

doi:10.3384/ecp17132171

Cited by 6 publications

(9 citation statements)

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“…In fact, powertrain cooling requirements are defined by the power required at the wheel and the powertrain component efficiency. Stellato et al 28 have defined a variable architecture to adapt the configuration to the cooling requirements during vehicle operation.…”

Section: Methodsmentioning

confidence: 99%

Optimal HEV cooling system design through a computer-aided virtual development chain

Baysset

Chessé

Chalet

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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To reduce CO2 and pollutant emissions, automotive manufacturers tend to turn increasingly toward electrified powertrains solutions, such as Hybrid Vehicles (HEV). Model Based System Engineering (MBSE) and computational tools are useful to support the development of HEV systems. System architectures need to be investigated to find optimal solutions regarding multiple possible goals constraints (fuel consumption, CO2 emissions, etc.). In this study, a novel global optimization framework is proposed to enable an automated cooling system optimization. The methodology starts with a set of components, design rules, and system requirements to search and automatically generate all the admissible cooling system architectures. System architectures are generated by a concept of decision tree. The decision tree algorithm is coupled to a design rules database to avoid exploring solutions that do not satisfy system requirements. Hydraulic models are automatically built for each system architecture generated to optimize head losses and minimize the pump consumption. For the most interesting solutions, 3D piping geometrical data (lengths, angles, and curve radius) are automatically drawn by a 3D optimization tool. Piping geometrical data are integrated in GT-SUITE thermo-hydraulic simulation models automatically built to evaluate the most interesting cooling system architectures generated. The best candidates are identified by a multi-criteria decision-making strategy. A first case of conventional vehicle cooling system is studied. Results on the given example indicate that the difference of energy consumption on WLTC cycle between the best performing architecture and the worst one is 69%.

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Section: Methodsmentioning

confidence: 99%

Optimal HEV cooling system design through a computer-aided virtual development chain

Baysset

Chessé

Chalet

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…The LCL is an important part of libraries required for making vehicle thermal management models for automobiles as shown in [2], [3] and [5]. This can cover a range of vehicles from conventional to electric and hybrid electric vehicles.…”

Section: Applications / Use Casesmentioning

confidence: 99%

Liquid Cooling Applications in Twin Builder- Industrial Paper

Pitchaikani

Prölß

Strandberg

et al. 2019

Linköping Electronic Conference Proceedings

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This industrial paper introduces the new Liquid Cooling Library (LCL) that is available in ANSYS Twin Builder Heating and Cooling Library bundle. This library is a multi-tool compatible Modelica Library which can help customers to deploy simulation of cooling systems in a new way. In general, LCL can be used for modeling cooling circuits across many industries like aerospace, automotive and process industry. The combination with the extensive electronics and electrical machine libraries in Twin Builder makes LCL a natural fit for cooling of power electronics and thermal management of electric vehicles.

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“…Powertrain with rigid gears & half shafts and yaw rate based torque vectoring control is used for handling study. The available powertrain model fidelities through different variants is assumed to be enough for simulating the selected attributes (Stellato, Bergianti & Batteh 2017;Griffin, Batteh & Andreasson 2012). Figure 2 shows powertrain architecture modeled and components involved.…”

Section: Vehicle System Models and Parametersmentioning

confidence: 99%

Multi‐objective modeling and analysis of an electric pickup vehicle for range, performance, drivability, handling and ride comfort attributes

Dharumaseelan¹,

Pitchaikani²,

Tamilselvam³

et al. 2019

INCOSE International Symp

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In today's electric vehicle arena, range, performance, drivability, handling safety and ride comfort are few important attributes an OEM would like to improve on a vehicle. However, they are almost mutually exclusive like greater range & extreme handling safety would need to compromise on drivability & ride comfort and vice versa respectively. Automotive engineers constantly focus on to find the right balance among these attributes by optimum design of the vehicle components. Often these components are multi-disciplinary involving different engineering domains like mechanical, thermal, electrical, hydraulic & electronics, etc. These complexities demand for model based and systems engineering approach from the start of the development. This emphasizes the need for high fidelity, dynamic vehicle system models suitable for co-development of different components and controls. In this study, detailed vehicle model representing an electric pickup truck including battery, traction motors, driveline, chassis (suspension, brakes, wheels & mounts) is created in Modelica platform using which multiple vehicle level attributes like range, performance, drivability, handling and ride comfort are studied. Further, it is shown that the simulation speed can be increased by varying fidelity of the subsystems based on study intended within the same model framework.

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Powertrain and Thermal System Simulation Models of a High Performance Electric Road Vehicle

Cited by 6 publications

References 0 publications

Optimal HEV cooling system design through a computer-aided virtual development chain

Optimal HEV cooling system design through a computer-aided virtual development chain

Liquid Cooling Applications in Twin Builder- Industrial Paper

Multi‐objective modeling and analysis of an electric pickup vehicle for range, performance, drivability, handling and ride comfort attributes

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