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This paper presents a simulation approach to assess the impact of changes to the charge point infrastructure and policies on Electric Vehicle (EV) user satisfaction, combining both market drivers with the practicalities of EV usage. An agent-based model (ABM) approach is developed where a large number of EVs, that represent the user population, drive within a region of interest. By simulating the driver’s response to their charging experience, the model allows large scale trends to emerge from the population to guide infrastructure policies as the number of EVs increases beyond the initial early adopter market. The model incorporates a Monte Carlo approach to generate EV and driver agent instances with distinct characteristics, including battery size, vehicle type, driving style, sensitivity to range. The driver model is constructed to respond to events that may increase range anxiety, e.g. increasing the likelihood of charging as the driver becomes more anxious.

For electric vehicles (EVs), driving range is one of the major concerns for wider customer acceptance and the cabin climate system represents the most significant auxiliary load for battery consumption. Unlike internally combustion engine (ICE) vehicles, EVs cannot utilize the waste heat from an engine to heat the cabin through the heating, ventilation and air conditioning (HVAC) system. Instead, EVs use battery energy for cabin heating, this reduces the driving range. To mitigate this situation, one of the most promising solutions is to optimize the recirculation of cabin air, to minimize the energy consumed by heating the cold ambient air through the HVAC system, whilst maintaining the same level of cabin comfort. However, the development of this controller is challenging, due to the coupled, nonlinear and multi-input multi-output nature of the HVAC and thermal systems.

The introduction of transient test cycles and the focus on real world driving emissions has increased the importance of ensuring the NOx and soot emissions are controlled during transient manoeuvres. At the same time, there is a drive to reduce the number of calibration variables used by engine control strategies to reduce development effort and costs. In this paper, a control orientated combustion model, [1], and model predictive control strategy, [2], that were developed in simulation and reported in earlier papers, are applied to a Diesel engine and demonstrated in a test vehicle. The paper describes how the control approach developed in simulation was implemented in embedded hardware, using an FPGA to accelerate the emissions calculations. The development of the predictive controller includes the application of a simplified optimisation algorithm to enable a real-time calculation in the test vehicle.

Setting up engines to meet emissions limits often involves extensive steady-state calibration activities combined with ad-hoc strategies to compensate for transient operation. As engines become more complex and acceptable emissions levels ever lower, this task is becoming increasingly time consuming and expensive. The inclusion of models in the engine control units offers a way to reduce some of this calibration effort. Model-based control is an active area of research with advanced approaches now being proposed. One example is the use of real-time models to regulate the burn angle during transient manœuvres. This paper describes the application of a control-orientated combustion model to control directly emissions during transients. The model is used to optimize and constrain the NOx emissions directly, rather than controlling an inferred variable such as the burn angle. This has the benefit that calibration engineers will be able to set the emissions trade-off directly.

Future demands for very low emissions from diesel engines, without compromising fuel economy or driveability, require Engine Management Systems (EMS) capable of compensating for emissions dispersion caused by production tolerances and component ageing. The Advanced Diesel Engine Control (ADEC) Project, a collaboration between Ricardo and General Motors, is aimed at reducing engine-out emissions dispersion and enabling alternative combustion modes, such as Highly Premixed Cool Combustion (HPCC), in real-world scenarios. This is being achieved by high-level co-ordination of fuel, air and EGR in order to meet the conflicting performance requirements of current and future diesel engines. A sensor feasibility study was undertaken which included a number of new sensing technologies appropriate for future mass production. Two sensor types, namely cylinder pressure and accelerometer sensors, were then selected to demonstrate varying degrees of benefits versus sensor technology cost.

For fuel cell vehicles to become a commercial reality, a number of challenges must be met including fuel infrastructure, durability, cost, performance, and efficiency. To help address these challenges, hybrid systems that combine fuel cells with an electrical energy storage system such as batteries or ultracapacitors is a potentially important configuration to increase efficiency and extend fuel cell life times by mitigating transient loads. However, the successful implementation of a hybrid fuel cell system requires achieving performance and efficiency benefits that offsets the additional costs, weight, and complexity of the energy storage system. The key to achieving the levels of efficiency required to justify the hybrid system lies in the power management control strategy. To this end, this paper examines the extension of a novel cost based control strategy developed for conventional internal combustion engine hybrid vehicles to a fuel cell platform (Patent WO 02/42110).

This paper follows on from a previous publication [1] and describes the continued development of a generic Integrated Powertrain (IPT) model. Simulation tools have been used for many years in engine and vehicle development programmes, to predict fuel consumption and emissions over various drive cycles. The concept phase of these programmes typically considers the overall layout and sizing of the components, with the detailed control strategies developed later. Today, the increased integration of vehicle sub-systems requires a high degree of overall control early in the programme, firstly, to allow the sub-systems to function, and secondly, to apply a similar quality of system control to each hardware iteration. To address this issue, a control hierarchy has been applied comprising of a supervisor controller and multiple local controllers.