Poster: an in-vehicle software defined network architecture for connected and automated vehicles

Vehicular network architectures are being reviewed and re-designed to meet requirements for increasing data rates and flexibility driven in part by the adoption of Connected and Automated Vehicles (CAV) strategies. A progressive move from traditional bus based networks, such as CAN, to Ethernet-based network architectures is being considered across the automotive industry. In this paper we advocate for an in-vehicle Software-Defined Network architecture and discuss benefits that such a shift in the architectural paradigm will bring by strengthening cyber-security defences and enabling a fail-operational capability through network programmability and provably correct networks

Surveillance arbitration in the era of digital policing

This article analyses adoptions of innovative technology into police surveillance activities. Extending the nascent body of empirical research on digital policing, the article draws on qualitative interview data of operational police uses of advanced surveillance technologies. Separate illustrative examples are drawn from social media intelligence gathering, digital forensics and covert online child sexual exploitation investigations. Here, surveillance governance mechanisms, often authored in the ‘pre-digital’ era, are deemed ill-fitting to the possibilities brought by new technologies. This generates new spaces of interpretation, where regulatory frameworks become renegotiated and reinterpreted, a process defined here as ‘surveillance arbitration’. These deliberations are resolved in myriad ways, including perceived licence for extended surveillance and, conversely, more cautious approaches motivated by perceived exposure to regulatory sanction

xEV thermal system control

The increasing electrification of vehicle powertrains has resulted in complex thermal systems. The performance of the thermal system has a strong impact on the electric range. This paper will discuss different thermal system concepts and the related control strategies to optimise their performance. The control algorithm complexity has increased with multi-variable systems and temperature limits that result in a challenging control problem. This paper will discuss the use of model predictive control and optimal control approaches to develop the control solution for this problem. These techniques have the additional benefit of reducing calibration effort whilst delivering efficiency gains

Diesel engine optimisation, with emissions constraints, on a prescribed driving route

A method for evaluating driving technique and engine operation, with emissions constraints, on a real driving route, is presented. A single degree of freedom vehicle model with simple resistive forces is driven along the pre-planned route. Instead of the velocity trajectory being determined a priori, the optimisation can choose the optimal speed along the route as well as the engine operating point. When constraints on the total emissions are imposed, the engine no longer operates on the minimum fuel locus; the power delivery changes as the emissions constraints are tightened. The driving technique changes from a series of bang-bang switches, into a smoother application of the engine power. The route is driven in the absence of stops and traffic restrictions

Optimisation of geofencing for mobility solutions in smart cities

Mobility solutions are increasingly aware of their location and can be considered as elements within a connected transport system. In this context, geofencing refers to the use of a vehicle position to change its behaviour to improve the overall environment for the whole transport system. In this paper, we start with advances in simulation tools for optimizing the use of geofences for both existing and new vehicles, covering both vehicle performance and the overall impact on air quality. The results of the simulations have been demonstrated in a connected city environment resulting in significant reductions in emissions and energy consumption for two applications; a geofence enabled bus and a fleet of existing hybrid electric vehicles using a smartphone app to enable geofence zones

A neuro-fuzzy model of evaporator in organic rankine cycle

The Organic Rankine Cycle (ORC) is a propitious waste heat recovery (WHR) technology that allows recovery of wasted energy from low to medium temperature sources. This WHR method needs to be adopted as an Internal Combustion Engine (ICE) bottoming technology to mitigate its environmental effects and fulfil exhaust gas emission regulations. The evaporator is the most decisive element of the ORC cycle due to its high nonlinear behaviour and high thermal inertia. In this study, a neuro-fuzzy model of the evaporator is presented based on the data obtained from Finite Volume (FV) model of the evaporator. The simulation results are compared in terms of RMSE, error mean and standard deviation. The data obtained from ANFIS model reached a promising agreement with FV model. For prediction of the evaporator outlet temperature, RMSEs of 0.152 and 1.33 obtained for the training and test data, respectively. Furthermore, the ANFIS model was successfully able to predict the evaporator power with RMSE of 0.035 for the training and 0.2 for the test data. In addition, the ANFIS model compared to the FV model with twenty control volumes enhanced the simu lation time significantly. This clearly indicates the great potential of employing ANFIS model for real-time applications

In-Use Emissions Testing of Diesel-Driven Buses in Southampton:Is Selective Catalytic Reduction as Effective as Fleet Operators Think?

Despite the continuously tightening emissions legislation, urban concentrations of nitrogen oxides (NOx) remain at harmful levels. Road transport is responsible for a large fraction, wherein diesel engines are the principal culprits. Turbocharged diesel engines have long been preferred in heavy duty applications, due to their torque delivery and low fuel consumption. Fleet operators are under pressure to understand and control the emissions of their vehicles, yet the performance of emissions abatement technology in real-world driving is largely unquantified. The most popular NOx abatement technology for heavy duty diesel vehicles is selective catalytic reduction. In this work, we empirically determine the efficiency of a factory-fitted SCR system in realworld driving by instrumenting passenger buses with both a portable emissions measurement system (PEMS) and a custom built telematics unit to record key parameters from the vehicle diagnostics systems. Wefindthateveninrelativelyfavourableconditions, while there is some improvement due to the use of SCR, the vehicles operate far from the design emissions targets. The archival value of this paper is in quantification of real world emissions versus design levels and the factors responsible for the discrepancy, as well as in examination of technologies to reduce this difference

Nonlinear model predictive control applied to multivariable thermal and chemical control of selective catalytic reduction aftertreatment

Manufacturers of diesel engines are under increasing pressure to meet progressively stricter NOx emissions limits. A key NOx abatement technology is selective catalytic reduction (SCR) in which ammonia, aided by a catalyst, reacts with NOx in the exhaust stream to produce nitrogen and water. The conversion efficiency is temperature dependent: at low temperature, reaction rates are temperature limited, resulting in suboptimal NOx removal, whereas at high temperatures, they are mass transfer limited. Maintaining sufficiently high temperature to allow maximal conversion is a challenge, particularly after cold start, as well as during conditions in which exhaust heat is insufficient, such as periods of low load or idling. In this work, a nonlinear model predictive controller simultaneously manages urea injection and power to an electric catalyst heater, in the presence of constraints.<br/

A control-oriented anfis model of evaporator in a 1-kwe organic rankine cycle prototype

This paper presents a control-oriented neuro-fuzzy model of brazed-plate evaporators for use in organic Rankine cycle (ORC) engines for waste heat recovery from exhaust-gas streams of diesel engines, amongst other applications. Careful modelling of the evaporator is both crucial to assess the dynamic performance of the ORC system and challenging due to the high nonlinearity of its governing equations. The proposed adaptive neuro-fuzzy inference system (ANFIS) model consists of two separate neuro-fuzzy sub-models for predicting the evaporator output temperature and evaporating pressure. Experimental data are collected from a 1-kWe ORC prototype to train, and verify the accuracy of the ANFIS model, which benefits from the feed-forward output calculation and backpropagation capability of the neural network, while keeping the interpretability of fuzzy systems. The effect of training the models using gradient-descent least-square estimate (GD-LSE) and particle swarm optimisation (PSO) techniques is investigated, and the performance of both techniques are compared in terms of RMSEs and correlation coefficients. The simulation results indicate strong learning ability and high generalisation performance for both. Training the ANFIS models using the PSO algorithm improved the obtained test data RMSE values by 29% for the evaporator outlet temperature and by 18% for the evaporator outlet pressure. The accuracy and speed of the model illustrate its potential for real-time control purposes