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More stringent Federal emission regulations and fuel economy requirements have driven the automotive industry toward more efficient vehicle thermal management systems to best utilize the heat produced from burning fuel and improve driveline efficiency. The greatest part of the effort is directed toward the hybridization of automotive transmission systems. The efficiency and durability of hybrid powertrain depends on the heat generation in electric motors and their interactions among each other, ambient condition, the cooling system and the transmission component configuration. These increase the complexity of motor temperature prediction as well as the computational cost of running a conjugate heat-transfer based CFD analysis. In this paper, 1D physics based thermal model is developed which allows rapid and accurate component-wise temperature estimation of the electric motor during both steady-state and transient driving cycles.

More stringent Federal emission regulations and fuel economy requirements have driven the automotive industry towards more sophisticated vehicle thermal management systems to best utilize the waste heat and improve driveline efficiency. The final drive unit in light and heavy duty trucks usually consists of geared transmission and differential housed in a lubricated axle. The automotive rear axle is one of the major sources of power loss in the driveline due to gear friction, churning and bearing loss affecting vehicle fuel economy. These losses vary significantly with lubricant viscosity. Also the temperatures of the lubricant are critical to the overall axle performance in terms of power losses, fatigue life and wear. In this paper, a methodology for modeling thermal behavior of automotive rear axle with heat exchanger is presented. The proposed model can be used to predict the axle lubricant temperature rise.

Stringent requirements for high fuel economy and energy efficiency mandate using increasingly complex vehicle thermal systems in most types of electrified vehicles (xEVs). Enabling the maximum benefits of such complex thermal systems under the full envelope of their operating modes demands designing complex thermal control systems. This is becoming one of the most challenging problems for electrified vehicles. Typically, the thermal systems of such vehicles have several modes of operation, constituting nonlinear multiple-input/multiple-output (MIMO) dynamic systems that cannot be efficiently controlled using classical or rule based strategies. This paper covers the different steps towards the design of a model-based control (MBC) strategy that can improve the overall performance of xEV thermal control systems. To achieve the above objective, the latter MBC strategy is applied to control cooling of the cabin and high voltage battery.