Actuators for Intelligent Electric Vehicles

This book details the advanced actuators for IEVs and the control algorithm design. In the actuator design, the configuration four-wheel independent drive/steering electric vehicles is reviewed. An in-wheel two-speed AMT with selectable one-way clutch is designed for IEV. Considering uncertainties, the optimization design for the planetary gear train of IEV is conducted. An electric power steering system is designed for IEV. In addition, advanced control algorithms are proposed in favour of active safety improvement. A supervision mechanism is applied to the segment drift control of autonomous driving. Double super-resolution network is used to design the intelligent driving algorithm. Torque distribution control technology and four-wheel steering technology are utilized for path tracking and adaptive cruise control. To advance the control accuracy, advanced estimation algorithms are studied in this book. The tyre-road peak friction coefficient under full slip rate range is identified based on the normalized tyre model. The pressure of the electro-hydraulic brake system is estimated based on signal fusion. Besides, a multi-semantic driver behaviour recognition model of autonomous vehicles is designed using confidence fusion mechanism. Moreover, a mono-vision based lateral localization system of low-cost autonomous vehicles is proposed with deep learning curb detection. To sum up, the discussed advanced actuators, control and estimation algorithms are beneficial to the active safety improvement of IEVs

Predictive Braking With Brake Light Detection-Field Test

Driver assistance systems, such as adaptive cruise control, are an increasing commodity in modern vehicles. Our earlier experience of radar-based adaptive cruise control has indicated repeatable abrupt behavior when approaching a stopped vehicle at high speed, which is typical for extra-urban roads. Abrupt behavior in assisted driving not only decreases the passenger trust but also reduces the comfort levels of such systems. We present a design and proof-of-concept of a machine vision-enhanced adaptive cruise controller. A machine vision-based brake light detection system was implemented and tested in order to smoothen the transition from coasting to braking and ensure speed reduction early enough. The machine vision system detects the brake lights in front, then transmits a command to the cruise controller to reduce speed. The current paper reports the speed control system design and experiments carried out to validate the system. The experiments showed the system works as designed by reducing abrupt behavior. Measurements show that brake light-assisted cruise control was able to start deceleration about three seconds earlier than a cruise controller without brake light detection. Measurements also showed increased ride comfort with the maximum deceleration and minimum jerk levels improving from 5% to 31%.Peer reviewe

Automatic Code Generation of Real-Time Nonlinear Model Predictive Control for Plug-in Hybrid Electric Vehicle Intelligent Cruise Controllers

Control systems have always been a vital part of the novel technological advancements of human being in any industry, especially transportation. With the introduction of the idea of autonomous driving, classical control systems are not effective anymore and the need for intelligent control systems is inevitable. Advanced Driver Assistance Systems (ADASs), which are systems proposed to help drivers improve the process of driving, and Intelligent Transportation Systems (ITS), which are proposed to provide information that promotes more coordinated and more ecological driving, require novel intelligent controllers that are adaptive to driving conditions. Therefore, the development of different strategic vehicle control systems by employing state-of-the-art intelligent control methods has been an active field of research in recent years. The highly variant nature of transportation implies that an effective intelligent control technique must be able to handle a large multi-input multi-output (MIMO) system with nonlinear complex dynamics. It must also store and analyse a large amount of data and information about the vehicle, its environment and traffic conditions in the process of decision-making. Nonlinear Model Predictive Control (NMPC), as a unique optimal model-based approach to intelligent control systems design, is a promising candidate that comprises all of these characteristics. The ability to solve constrained multi-objective optimization problems with a predictive approach has made this technique powerful. However, NMPC controller developers face real-time implementation challenges as this method suffers from huge computational loads. Hence, fast Real-Time Optimization (RTO) methods are proposed to overcome this drawback. Optimization methods based on Generalized Minimum Residual (GMRES) method are examples of these RTO algorithms that have shown great potential for real-time applications such as vehicle control. This thesis investigates the potential of employing GMRES-based RTO algorithms to design intelligent vehicle control systems, in particular intelligent cruise controllers. Plug-in Hybrid Electric vehicles (PHEVs) are introducing themselves as the future solutions for green and ecological transportation, the thesis also introduces an intelligent cruise controller for the Toyota Prius 2013 PHEV. To this end, an automatic multi-solver NMPC code generator based on GMRES-based RTO algorithms is developed in MATLAB. The user-friendly environment of this code generation tool allows the user to easily generate NMPC controller codes for further model-in-the-loop (MIL) and hardware-in-the-loop (HIL) simulations. Simulations are performed for two different driving scenarios: driving on hilly roads and a car-following scenario. In the case of driving on hilly roads, a comparative study is conducted between different real-time optimizers and it is concluded that the Newton/GMRES algorithm is faster than the Continuation/GMRES algorithm. A novel adaptive prediction horizon length approach is also developed to enhance the performance of the NMPC controller. Simulation results demonstrate a minimum of 3.4% energy consumption improvement as compared to a PID controller performance as well as improvement of reference speed tracking when using an adaptive prediction horizon length. In case of the car-following scenario, the effect of several tuning parameters and adaptive gains on the performance of the proposed NMPC controller is studied. Then the ecological adaptive cruise controller was tested on urban and highway driving cycles, and resulted in 3.4% and 1.2%, respectively, improvement in the cost of the trip. Finally, the proposed NMPC controllers for both intelligent cruise control systems are tested on an HIL platform for rapid control prototyping. The HIL results on a dSPACE prototype Electronic Control Unit (ECU) indicate that the real-time optimizers and the proposed NMPC controllers are fast enough to be implementable on an actual ECU for a certain range of prediction horizon sizes

Optimal Vehicle Dynamics and Powertrain Control for Connected and Automated Vehicles

The implementation of connected and automated vehicle technologies enables opportunities for a novel computational framework for real-time control actions aimed at optimizing energy consumption and associated benefits. In this paper, we present a two-level control architecture for a connected and automated plug-in hybrid electric vehicle to optimize simultaneously its speed profile and powertrain efficiency. We evaluate the proposed architecture through simulation in a network of vehicles.Comment: 6 pages, 2 figures, 1 table, conferenc

Developing and Evaluating the driving and powertrain systems of automated and electrified vehicles (AEVs) for sustainable transport

In the transition towards sustainable transport, automated and electrified vehicles (AEVs) play a key role in overcoming challenges such as fuel consumption, emissions, safety, and congestion. The development and assessment of AEVs require bringing together insights from multiple disciplines such as vehicle studies to design and control AEVs and traffic flow studies to describe and evaluate their driving behaviours. This thesis, therefore, addresses the needs of automotive and civil engineers, and investigates three classes of problems: optimizing the driving and powertrain systems of AEVs, modelling their driving behaviours in microscopic traffic simulation, and evaluating their performance in real-world driving conditions. The first part of this thesis proposes Pareto-based multi-objective optimization (MOO) frameworks for the optimal sizing of powertrain components, e.g., battery and ultracapacitor, and for the integrated calibration of control systems including adaptive cruise control (ACC) and energy management strategy (EMS). We demonstrate that these frameworks can bring collective improvements in energy efficiency, greenhouse gas (GHG) emissions, ride comfort, safety, and cost-effectiveness. The second part of this thesis develops microscopic free-flow or car-following models for reproducing longitudinal driving behaviours of AEVs in traffic simulation, which can support the needs to predict the impact of AEVs on traffic flow and maximize their benefits to the road network. The proposed models can account for electrified vehicle dynamics, road geometric characteristics, and sensing/perception delay, which have significant effects on driving behaviours of AEVs but are largely ignored in traffic flow studies. Finally, we systematically evaluate the energy and safety performances of AEVs in real-world driving conditions. A series of vehicle platoon experiments are carried out on public roads and test tracks, to identify the difference in driving behaviours between ACC-equipped vehicles and human-driven vehicles (HDVs) and to examine the impact of ACC time-gap settings on energy consumption

A framework for user- and system-oriented optimisation of fuel efficiency and traffic flow in Adaptive Cruise Control

Fully automated vehicles could have a significant share of the road network traffic in the near future. Several commercial vehicles with full-range Adaptive Cruise Control (ACC) systems or semi-autonomous functionalities are already available on the market. Many research studies aim at leveraging the potential of automated driving in order to improve the fuel efficiency of vehicles. However, in the vast majority of those, fuel efficiency is isolated to the driving dynamics between a single follower-leader pair, hence overlooking the complex nature of traffic. Consequently fuel efficiency and the efficient use of the roadway capacity are framed as conflicting objectives, leading to fuel-economy control models that adopt highly conservative driving styles. This formulation of the problem could be seen as a user-optimal approach, where in spite of delivering savings for individual vehicles, there is the side-effect of the deterioration of traffic flow. An important point that is overlooked is that the inefficient use of roadway capacity gives rise to congested traffic and traffic breakdowns, which in return increases energy costs within the system. The optimisation methods used in these studies entail high computational costs and, therefore, impose a strict constraint on the scope of problem. In this study, the use of car-following models and the limitation of the search space of optimal strategies to the parameter space of these is proposed. The proposed framework enables performing much more comprehensive optimisations and conducting more extensive tests on the collective impacts of fuel-economy driving strategies. The results show that, as conjectured, a “short-sighted” user-optimal approach is unable to deliver overall fuel efficiency. Conversely, a system-optimal formulation for fuel efficient driving is presented, and it is shown that the objectives of fuel efficiency and traffic flow are in fact not only non-conflicting, but also that they could be viewed as one when the global benefits to the network are considered

On optimal mission planning for conventional and electric heavy duty vehicles

Ever-growing energy consumption and CO2 emissions due to the increase in road transport are major challenges that attract international attention, especially policy makers, logistic service providers and customers considering environmental, ecological and economic issues. Other negative side-effects caused by the growth of the road transport are the extensive economic and social costs because of traffic congestion. Thus, there is a strong motivation to investigate possible ways of improving transport efficiency aiming at achieving a sustainable transport, e.g. by finding the best compromise between resource consumption and logistics performance. The transport efficiency can be improved by optimal planning of the transport mission, which can be interpreted as optimising mission start and/or finish time, and velocity profile of the driving vehicle. This thesis proposes a bi-layer mission planner for long look-ahead horizons stretched up to hundreds of kilometers. The mission planner consists of logistics planner as its top level and eco-driving supervisor as its bottom level. The logistics planner aims at optimising the mission start and/or finish time by optimising energy consumption and travel time, subject to road and traffic information, e.g. legal and dynamic speed limits. The eco-driving supervisor computes the velocity profile of the driving vehicle by optimising the energy consumption and penalising driver discomfort. To do so, an online-capable algorithm has been formulated in MPC framework, subject to road and traffic information, and the pre-optimised mission start and/or finish time. This algorithm is computationally efficient and enables the driving vehicle to adapt and optimally respond to predicted disturbances within a short amount of time. The mission planner has been applied to conventional and fully-electric powertrains. It is observed that total travel timeis reduced up to 5.5 % by optimising the mission start time, when keeping anaverage cruising speed of about 75 km/h. Also, compared to standard cruise control, the energy savings of using this algorithm is up to 11.6 %