Effect of advanced thermal management systems on hybrid electric drive units

Nessim, Waleed; Zhang, Fujun; Zhao, Changlu; Zhu, Z. P.

doi:10.1109/ifost.2012.6357678

Cited by 2 publications

(2 citation statements)

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“…In fact, optimal control problem and optimal sizing problem are largely studied. 15–20 Park 15 presents a methodology to optimize the pump and radiator sizing for a given cooling architecture under packaging and thermal constraints. Park introduced in the optimal problem definition a scaling factor for the pump and the radiator as the variable optimization.…”

Section: Introductionmentioning

confidence: 99%

“…Control temperature of each HEV components define the thermal constraints. Nessim and Zhang 18 have used a PID controller in a 1D GT-SUITE cooling system simulation model to reduce the actuators consumption (fan and pump) and the ICE efficiency of a specific cooling architecture, by keeping the engine close to its optimal operational temperature. Tao and Wagner 20 have defined a non-linear optimal control for a fixed cooling system architecture in order to minimize the different actuators consumption (pump, fan, and valve) and to track an Internal Combustion Engine (ICE) reference temperature.…”

Section: Introductionmentioning

confidence: 99%

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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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