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Energy management of battery electric vehicle (BEV) is a very important and complex multi-system optimisation problem. The thermal energy management of a BEV plays a crucial role in consistent efficiency and performance of vehicle in all weather conditions. But in order to manage the thermal management, it requires a significant number of temperature sensors throughout the car including high voltage batteries, thus increasing the cost, complexity and weight of the car. Virtual sensors can replace physical sensors with a data-driven, physical relation-driven or machine learning-based prediction approach. This paper presents a framework for the development of a neural network virtual sensor using a thermal system hardware-in-the-loop test rig as the target system. The various neural network topologies, including RNN, LSTM, GRU, and CNN, are evaluated to determine the most effective approach.

Powertrain thermal encapsulation has the potential to improve fuel consumption and CO2 via heat retention. Heat retained within the powertrain after a period of engine-off, can increase the temperature of the next engine start hours after key-off. This in turn reduces inefficiencies associated with sub-optimal temperatures such as friction. The Ambient Temperature Correction Test was adopted in the current work which contains two World-wide harmonised Light duty Test Procedure (WLTP) cycles separated by a 9-hour soak period. A coupled 1D - 3D computational approach was used to capture heat retention characteristics and subsequent warm-up effects. A 1-D powertrain warm-up model was developed in GT-Suite to capture the thermal warm-up characteristics of the powertrain. The model included a temperature dependent friction model, the thermal-hydraulic characteristics of the cooling and lubrication circuits as well as parasitic losses associated with pumps.

With the continuing improvements to thermoelectric (TE) materials and systems, their potential for both energy recovery and thermal management is increasingly apparent. Recent developments in materials and notably Skutterudites have allowed materials to be matched much more closely to the working temperatures of a light duty power-train. The choice of TE materials remains a substantial question in the design of a thermoelectric generator (TEG). While the quest for improvements in materials performance continues, the work reported in this paper is characterized by the decision to focus on the refinement of one class of TE materials: Skutterudites. In parallel, the engineering work on the integration of the TE materials into a heat exchanger could continue and be focused on the properties of this class of material. Skutterudites offer the combination of a high working temperature and a competitive electrical output (defined by ZT, the figure of merit).

The importance of designing and sizing a thermal management system for electric vehicle powertrains cannot be overstated. Traditional approaches often rely on model-based system design using supplier reference component data, which can inadvertently lead to undisclosed errors arising from the interactions between the components and the environment. This paper introduces a novel test facility for battery electric vehicle thermal management technology, which has been designed for neural network virtual sensor and non-linear multi-in multi-out control development. The paper demonstrates how a digital twin of the test bench can used to support the development of such technology. Additionally, this paper presents preliminary results from the test bench revealing insights into the performance and interactions of key components. For instance, there is an observed 30% reduction in the maximum flow rate of the pump integrated into the test bench compared to the specified value.

PEMFC (proton exchange membrane or polymer electrolyte membrane fuel cell) is a potential candidate as a future power source for automobile applications. Water and thermal management is important to PEMFC operation. Numerical models, which describe the transport and electrochemical phenomena occurring in PEMFCs, are important to the water and thermal management of fuel cells. 3D (three-dimensional) multi-scale CFD (computational fluid dynamics) models take into account the real geometry structure and thus are capable of predicting real operation/performance. In this study, a 3D multi-phase CFD model is employed to simulate a large-scale PEMFC (109.93 cm2) under various operating conditions. More specifically, the effects of operating pressure (1.0-4.0 atm) on fuel cell performance and internal water and thermal characteristics are studied in detail under two inlet humidities, 100% and 40%.