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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.

Wet Clutches are used in automotive powertrains to enable compact designs and efficient gear shifting. During the slip phase of engagement, significant flash temperatures arise at the friction disc to separator interface because of dissipative frictional losses. An important aspect of the design process is to ensure the interface temperature does not exceed the material temperature threshold at which accelerated wear behavior and/or thermal degradation occurs. During the early stages of a design process, it is advantageous to evaluate numerous system and component design iterations exposed to plethora of possible drive cycles. A simulation tool is needed which can determine the critical operational conditions the system must survive for performance and durability to be assured. This paper describes a time-efficient multiphysics model developed to predict clutch disc temperatures with a runtime in the order of minutes.

Closed-loop electronic control is a proven and efficient way to optimize spark ignition engine performance and to control pollutant emissions. In-cylinder pressure sensors provide accurate information on the quality of combustion. The conductivity of combustion flames can alternatively be used as a measure of combustion quality through ion-current measurements. In this paper, combustion diagnostics through ion-current sensing are studied. A single cylinder research engine was used to investigate the effects of misfire, ignition timing, air to fuel ratio, compression ratio, speed and load on the ion-current signal. The ion-current signal was obtained via one, or both, of two additional, remote in-cylinder ion sensors (rather than by via the firing spark plug, as is usually the case). The ion-current signals obtained from a single remote sensor, and then the two remote sensors are compared.

Vehicle development involves the design and integration of subsystems of different domains to meet performance, efficiency, and emissions targets set during the initial developmental stages. Before a physical prototype of a vehicle or vehicle powertrain is tested, engineers build and test virtual prototypes of the design(s) on multiple stages throughout the development cycle. In addition, controllers and physical prototypes of subsystems are tested under simulated signals before a physical prototype of the vehicle is available. Different departments within an automotive company tend to use different modelling and simulation tools specific to the needs of their specific engineering discipline. While this makes sense considering the development of the said system, subsystem, or component, modern holistic vehicle engineering requires the constituent parts to operate in synergy with one-another in order to ensure vehicle-level optimal performance.

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.

Proton exchange membrane fuel cell (PEMFC) provides a promising future low carbon automotive powertrain solution. The catalyst layer (CL) is its core component which directly influences the output performance. PEMFC performance can be greatly improved by the effective optimization of CL composition. This work demonstrates a deep optimization of CL composition for improving the PEMFC performance, including the platinum (Pt) loading, Pt percentage of carbon-supported Pt and ionomer to carbon ratio of the anode and the cathode,. The simulation results by a PEMFC three-dimensional (3D) computation fluid dynamics (CFD) model coupled with the CL agglomerate model is used to train the artificial neural network (ANN) which can efficiently predict the current density under different CL composition. Squared correlation coefficient (R-square) and mean percentage error in the training set and validation set are 0.9867, 0.2635% and 0.9543, 1.1275%, respectively.

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.

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 control of Homogeneous Charge Compression Ignition (HCCI) (also known as Controlled Auto Ignition (CAI)) has been a major research topic recently, since this type of combustion has the potential to be highly efficient and to produce low NOx and particulate matter emissions. Ion current has proven itself as a closed loop control feedback for SI engines. Based on previous work by the authors, ion current was acquired through HCCI operation too, with promising results. However, for best utilization of this feedback signal, advanced interpretation techniques such as artificial neural networks can be used. In this paper the use of these advanced techniques on experimental data is explored and discussed. The experiments are performed on a single cylinder cam-less (equipped with a Fully Variable Valve Timing (FVVT) system) research engine fueled with commercially available gasoline (95 ON).

Modern thermal propulsion systems (TPS) as part of hybrid powertrains are becoming increasingly complex. They have an increased number of components in comparison to traditionally powered vehicles leading to increased demand in packaging requirements. Many of the components in these systems relate to achieving efficiency gains, weight saving and pollutant reduction. This includes turbochargers and diesel or gasoline particulate filters for example and these are known to be very sensitive to inlet boundary conditions. When overcoming packaging requirements, sub-optimal flow distributions throughout the TPS can easily occur. Moreover, the individual components are often designed in isolation assuming relatively flat and artificially quiescent inlet flow conditions in comparison to those they are actually presented with. Thus, some of the efficiency benefits are lost through reduced component aerodynamic efficiency.

This work describes the application of Non-Linear Autoregressive Models with Exogenous Inputs (NLARX) in order to predict the NOx emissions of heavy-duty diesel engines. Two experiments are presented: 1.) a Non-Road-Transient-Cycle (NRTC) 2.) a composition of different engine operation modes and different engine calibrations. Data sets are pre-processed by normalization and re-arranged into training and validation sets. The chosen model is taken from the MATLAB Neural Network Toolbox using the algorithms provided. It is teacher forced trained and then validated. Training results show recognizable performance. However, the validation shows the potential of the chosen method.

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.