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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.

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.

As lithium-ion electric vehicle (EV) batteries are sensitive to the conditions they are exposed to during charging and discharging, operational control has been an important research area. While an understanding of the effects current load and operation temperature has on the ageing stability of a battery has been established, associated control strategies are yet to be fully optimized. Most battery charging studies utilize controlled ambient temperatures and basic defined cycles, which may only apply to a small subset of real-world EV consumers. This leads to control strategies that do not consider electrical grid price fluctuation, user driving habits or local weather conditions. This paper looks to propose improved smart charging strategies of EVs to reduce consumer costs while also increasing the battery longevity. To accomplish the primary objective, A model has been generated that simulates the standard charge cycle of a battery.

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.

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%.

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 TC48 project is developing a state-of-the-art, exceptionally low cost, 48V Plug-in hybrid electric (PHEV) demonstration drivetrain suitable for electrically powered urban driving, hybrid operation, and internal combustion engine powered high speed motoring. This paper explains the motivation for the project, and presents the layout options considered and the rationale by which these were reduced. The vehicle simulation model used to evaluate the layout options is described and discussed. The modelling work was used in order to support and justify the design choices made. The design of the vehicle's control systems is discussed, presenting simulation results. The physical embodiment of the design is not reported in this paper. The paper describes analysis of small vehicles in the marketplace, including aspects of range and cost, leading to the justification for the specification of the TC48 system.

In 2012 MAHLE Powertrain developed a range-extended electric vehicle (REEV) demonstrator, based on a series hybrid configuration, and uses a battery to store electrical energy from the grid. Once the battery state of charge (SOC) is depleted a gasoline engine (range extender) is activated to provide the energy required to propel the vehicle. As part of the continuing development of this vehicle, MAHLE Powertrain has developed control software which can intelligently manage the use of the battery energy through the combined use of GPS and road topographical data. Advanced knowledge of the route prior to the start of a journey enables the software to calculate the SOC throughout the journey and pre-determine the optimum operating strategy for the range extender to enable best charging efficiency and minimize NVH. The software can also operate without a pre-determined route being selected.

The performance of energy flow management strategies is essential for the success of hybrid electric vehicles (HEVs), which are considered amongst the most promising solutions for improving fuel economy as well as reducing exhaust emissions. The heavy duty HEVs engaged in cycles characterized by start-stop configuration has attracted widely interests, especially in off-road applications. In this paper, a fuzzy equivalent consumption minimization strategy (F-ECMS) is proposed as an intelligent real-time energy management solution for heavy duty HEVs. The online optimization problem is formulated as minimizing a cost function, in terms of weighted fuel power and electrical power. A fuzzy rule-based approach is applied on the weight tuning within the cost function, with respect to the variations of the battery state-of-charge (SOC) and elapsed time.

Considerable engineering effort is now being applied to the design and development of Soapboxes entered in the Goodwood Festival of Speed Gravity Challenge. With average speeds of 18 ms-1 (40 mph) from a standing start along the 0.7 mile course and maximum speeds of around 27 ms-1 (60 mph), the aerodynamic contribution to performance is significant. This paper discusses the aerodynamic considerations given to the design of the leading Soapboxes and to the racing conditions experienced. Analysis and test techniques which may also be employed are also described.