A state-of-the-art review on torque distribution strategies aimed at enhancing energy efficiency for fully electric vehicles with independently actuated drivetrains

© 2019, Levrotto and Bella. All rights reserved. Electric vehicles are the future of private passenger transportation. However, there are still several technological barriers that hinder the large scale adoption of electric vehicles. In particular, their limited autonomy motivates studies on methods for improving the energy efficiency of electric vehicles so as to make them more attractive to the market. This paper provides a concise review on the current state-of-the-art of torque distribution strategies aimed at enhancing energy efficiency for fully electric vehicles with independently actuated drivetrains (FEVIADs). Starting from the operating principles, which include the "control allocation" problem, the peculiarities of each proposed solution are illustrated. All the existing techniques are categorized based on a selection of parameters deemed relevant to provide a comprehensive overview and understanding of the topic. Finally, future concerns and research perspectives for FEVIAD are discussed

Multi-axle Vehicle Modeling and Stability Control: A Reconfigurable Approach

Multi-axle vehicles, such as trucks and buses, have been playing a vital role in trucking industry, public transportation system, and long-distance transport services. However, at the same time, statistics suggest more than one million lives are lost in road accidents each year over the world. The high adoption and utilization of multi-axle vehicles hold a significant portion of road accidents and death. To improve the active safety of vehicles, active systems have been developed and commercialized over the last decades to augment the driver's actions. However, unlike two-axle vehicles (e.g., passenger cars), multi-axle vehicles come in a rich diversity and variety to meet with many different transportation needs. Specifically, vehicle configurations are seen in different numbers of axles, numbers of articulations, powertrain modes, and active actuation systems. In addition, multi-axle vehicles are usually articulated, which makes the dynamics and control more complex and challenging as more instability modes appear, such as, trailer sway and jackknife. This research is hence motivated by an essential question: how can a universal and reconfigurable control system be developed for any multi-axle/articulated vehicle with any configuration? Leveraging the matrix approach and optimization-based techniques, this thesis developed a reconfigurable and universal modeling and control framework to this aim. Specifically, a general dynamics modeling that unifies any multi-axle and articulated vehicles in one formulation is developed in an intuitive manner. It defines the `Boolean Matrices' to determine any configuration of the articulation, the number of axles, and the active actuation systems. In this way, the corresponding dynamics model can be easily and quickly formulated when axles, articulations or actuators are added or removed. The general modeling serves to achieve the universality and reconfigurability in controller design. Therefore, a hierarchical, i.e., two-layer, control system is proposed. In the high layer, the optimization process of a model predictive control (MPC) calculates corrective Center of Gravity (CG) forces/moments, which are universal to any vehicle. The lower-level controller is achieved by a Control Allocation (CA) algorithm. It aims to realize the MPC commands by regulating the steering or torque (driving or braking) at each wheel optimally. In addition, the optimization takes into account real-time constraints, such as actuator limits, tire capacity, wheel slips, and actuators failure. Simulations are conducted on different vehicle configurations to evaluate control performance, reconfigurability, and robustness of the system. Additionally, to evaluate the real-time performance of the developed controller, experimental validation is carried out on an articulated vehicle with multiple configurations of differential braking systems. It is observed that the controller is very effective in dynamics control and has a promising reconfigurability when moving from one configuration to another

Integrated Vehicle Stability and Power Management Controls for Electric Vehicles

An integrated vehicle controller is presented for electric vehicles using independently driven wheel motors. This topology takes an optimal control approach to enhancing a vehicle's performance, stability, and energy consumption metrics simultaneously in a unified software structure. The logical output of this algorithm is a set of re-distributed wheel torques, to create torque vectoring for stability-focused yaw rate tracking, and longitudinal biasing to modify motor load for energy savings. A real-time numerical approach to solving the optimization problem is also presented, and shown to offer benefits over a closed form analytic approach. In this, solution constraints are used to link considerations such as nonlinear motor limits, tire friction envelopes, and lower-level traction control loops. To test the efficacy of this control structure, two vehicle test platforms were constructed as retrofits of production gas SUVs for electric drive. For this, the component layout is given, followed by an explanation of the software code structure as performed in a Simulink/Carsim/dSpace environment. Results from these platforms are given, with experimental and simulation data for traction control, yaw performance tracking and drive cycle power consumption. Proven performance over a variety of maneuvers and surface conditions further demonstrate the controller's stability and suitability for mass production.1 yea

Full Vehicle State Estimation Using a Holistic Corner-based Approach

Vehicles' active safety systems use different sensors, vehicle states, and actuators, along with an advanced control algorithm, to assist drivers and to maintain the dynamics of a vehicle within a desired safe range in case of instability in vehicle motion. Therefore, recent developments in such vehicle stability control and autonomous driving systems have led to substantial interest in reliable road angle and vehicle states (tire forces and vehicle velocities) estimation. Advances in applications of sensor technologies, sensor fusion, and cooperative estimation in intelligent transportation systems facilitate reliable and robust estimation of vehicle states and road angles. In this direction, developing a flexible and reliable estimation structure at a reasonable cost to operate the available sensor data for the proper functioning of active safety systems in current vehicles is a preeminent objective of the car manufacturers in dealing with the technological changes in the automotive industry. This thesis presents a novel generic integrated tire force and velocity estimation system at each corner to monitor tire capacities and slip condition individually and to address road uncertainty issues in the current model-based vehicle state estimators. Tire force estimators are developed using computationally efficient nonlinear and Kalman-based observers and common measurements in production vehicles. The stability and performance of the time-varying estimators are explored and it is shown that the developed integrated structure is robust to model uncertainties including tire properties, inflation pressure, and effective rolling radius, does not need tire parameters and road friction information, and can transfer from one car to another. The main challenges for velocity estimation are the lack of knowledge of road friction in the model-based methods and accumulated error in kinematic-based approaches. To tackle these issues, the lumped LuGre tire model is integrated with the vehicle kinematics in this research. It is shown that the proposed generic corner-based estimator reduces the number of required tire parameters significantly and does not require knowledge of the road friction. The stability and performance of the time-varying velocity estimators are studied and the sensitivity of the observers' stability to the model parameter changes is discussed. The proposed velocity estimators are validated in simulations and road experiments with two vehicles in several maneuvers with various driveline configurations on roads with different friction conditions. The simulation and experimental results substantiate the accuracy and robustness of the state estimators for even harsh maneuvers on surfaces with varying friction. A corner-based lateral state estimation is also developed for conventional cars application independent of the wheel torques. This approach utilizes variable weighted axles' estimates and high slip detection modules to deal with uncertainties associated with longitudinal forces in large steering. Therefore, the output of the lateral estimator is not altered by the longitudinal force effect and its performance is not compromised. A method for road classification is also investigated utilizing the vehicle lateral response in diverse maneuvers. Moreover, the designed estimation structure is shown to work with various driveline configurations such as front, rear, or all-wheel drive and can be easily reconfigured to operate with different vehicles and control systems' actuator configurations such as differential braking, torque vectoring, or their combinations on the front or rear axles. This research has resulted in two US pending patents on vehicle speed estimation and sensor fault diagnosis and successful transfer of these patents to industry

Holistic Vehicle Control Using Learning MPC

In recent years, learning MPC schemes have been introduced to address these challenges of traditional MPC. They typically leverage different machine learning techniques to learn the system dynamics directly from data, allowing it to handle model uncertainty more effectively. Besides, they can adapt to changes by continuously updating the learned model using real-time data, ensuring that the controller remains effective even as the system evolves. However, there are some challenges for the existing learning MPC techniques. Firstly, learning-based control approaches often lack interpretability. Understanding and interpreting the learned models and their learning and prediction processes are crucial for safety critical systems such as vehicle stability systems. Secondly, existing learning MPC techniques rely solely on learned models, which might result in poor performance or instability if the model encounters scenarios that differ significantly from the training data. Thirdly, existing learning MPC techniques typically require large amounts of high-quality data for training accurate models, which can be expensive or impractical in the vehicle stability control domain. To address these challenges, this thesis proposes a novel hybrid learning MPC approach for HVC. The main objective is to leverage the capabilities of machine learning algorithms to learn accurate and adaptive models of vehicle dynamics from data, enabling enhanced control strategies for improved stability and maneuverability. The hybrid learning MPC scheme maintains a traditional physics-based vehicle model and a data-based learning model. In the learned model, a variety of machine-learning techniques can be used to predict vehicle dynamics based on learning from collected vehicle data. The performance of the developed hybrid learning MPC controller using torque vectoring (TV) as the actuator is evaluated through the Matlab/Simulink and CarSim co-simulation with a high-fidelity Chevy Equinox vehicle model under a series of harsh maneuvers. Extensive real-world experiments using a Chevy Equinox electric testing vehicle are conducted. Both simulation results and experimental results show that the developed hybrid learning MPC approach consistently outperforms existing MPC methods with better yaw rate tracking performance and smaller vehicle sideslip under various driving conditions

Intelligent and Efficient Transport Systems

The aim of this book is to present a number of digital and technology solutions to real-world problems across transportation sectors and infrastructures. Nine chapters have been well prepared and organized with the core topics as follows: -A guideline to evaluate the energy efficiency of a vehicle -A guideline to design and evaluate an electric propulsion system -Potential opportunities for intelligent transportation systems and smart cities -The importance of system control and energy-power management in transportation systems and infrastructures -Bespoke modeling tools and real-time simulation platforms for transportation system development This book will be useful to a wide range of audiences: university staff and students, engineers, and business people working in relevant fields

Integrated Stability and Tracking Control System for Autonomous Vehicle-Trailer Systems

The addition of a trailer to a vehicle significantly changes the dynamics of a single-vehicle and generates new instability modes including jackknifing and snaking. Understanding of the dynamics of these modes leads to the development of more effective control strategies for improved stability and handling. In addition, vehicle-trailer systems suffer from the off-tracking problem meaning that the path of the trailer rear end differs from that of the vehicle front end. Off-tracking makes these vehicles less maneuverable and increases swept path of the vehicle, especially in urban areas and tight spaces. Vehicle active safety systems have been extensively developed over the past decades to improve vehicle handling and assist the driver to keep the vehicle under control in unfavorable driving situations. Such active safety systems, however, are not well developed for vehicle-trailer systems specialty to control the above-mentioned instability modes. In addition to the above active safety systems, off-tracking of vehicle-trailers is another aspect that is essential in path planning and path tracking of autonomous vehicle-trailers. In this thesis, first, phase portraits are used to study the non-linear dynamics of the system through state trajectories and equilibrium point locations of a vehicle-trailer system, with respect to the inputs and also the loading conditions of the trailer. By the study of the phase portraits, the foundation of the vehicle-trailer yaw instability is recognized and a two-dimensional stable region as the stability envelope is defined. This stability envelope is utilized to prevent unnecessary control interventions while the vehicle is operating in the safe stability region whereas the controller is allowed to effectively interfere when the vehicle crosses the stability envelope. To handle multiple control actions in the vehicle and trailer units, a control structure with two hierarchical layers is proposed. In the upper layer, a model predictive controller is formulated as a quadratic optimization problem with a virtual control action for each degree of freedom of the vehicle dynamic model. In this formulation, the developed stability envelope is constructed as state constraints along with control action constraints. In the lower layer, a control allocation approach optimally transforms the virtual control actions provided by the upper layer into steering and/or braking commands for each wheel. In this approach, actuator failure in addition to actuator and tire capacity constraints is taken into account in real-time. A hybrid A*-based motion planning is developed to generate a trajectory while considering the trailer effect in terms of stability in high speed and off-tracking in high curvature paths. The proposed motion planning utilizes a hybrid A* algorithm combined with potential fields to find a feasible and collision-free trajectory for the vehicle-trailer system. To assess the performance of the proposed control structure in instability prevention both in snaking and jackknifing, different simulations are conducted using different control actions. Additionally, the fault tolerance and robustness of the proposed controller are investigated. To validate the real-time performance of the proposed control strategy, experimental tests are performed on a vehicle-trailer system with differential braking capability. The results show that the proposed control strategy is able to effectively prevent both instability modes as well as unnecessary engagement of the control action to reduce control intervention. The performance of the developed motion planning module is also evaluated for normal driving, obstacle avoidance, off-tracking compensation, and crash mitigation using high-fidility Carsim model. It is observed that proposed motion planning is able to effectively satisfy all the expected requirements

Investigation of integrated control of articulated heavy vehicle using scaled multi-body dynamic model

Heavy vehicle handling control systems have proven to be an efficient way of reducing road accidents and improving road traffic safety. Testing these control systems on heavy vehicles can be expensive and unsafe. Meanwhile, the scaled model has proven a secure and inexpensive way of designing and deploying vehicle dynamics control. However, the scaled model's mathematical modelling has been mainly limited to the bicycle model, reducing the scope of exploring the handling dynamics. This study presents an innovative way of modelling a scaled tractor semi-trailer using multi-body dynamics software and testing control systems through co-simulation to help develop new control systems safely and inexpensively for improving road traffic safety. In this research, modelling the scaled model of an articulated vehicle was simulated on MSC ADAMS/View, which extends the mathematical model to 168 degrees of freedom. A 1/14 physical model was used to validate the simulation model and co-simulation has been established between MSC ADAMS/View and MATLAB to investigate the control of a scaled model built on MSC ADAMS/View with a developed control system built on MATLAB/Simulink. The scaled model is a 1/14 Scania R620 articulated lorry manufactured by TAMIYA. Different parameters of the scaled model have been measured and used as inputs to the simulation model. MSC ADAMS/View was used to model the vehicle and to capture its response. The results were validated through physical tests, so a microcontroller was added to the physical model with different accelerometers to control and record the vehicle's motion instead of the existing radio control. Co-simulation has been implemented using two different control schemes, which have been built and compared against each other. The first control scheme is the electronic stability control system only. The second one is an integrated control system which combines the active front steering with the electronic stability control scheme. The main target of the developed control systems is to stabilise the vehicle through manoeuvres using the Fuzzy logic methodology. The study's main findings are that the experimental results show reasonable similarity to the simulation results, although there are minor differences. The physical validation of the simulation model indicates that it is possible to model a scaled model using multi-body dynamics software with specific considerations. Also, the results give a good understanding of the performance of heavy vehicles. Finally, using the co-simulation implemented using two different control schemes proves that the control can be developed using the scaled model. The proposed control method has been shown to be useful in developing the stability of the vehicle. It enhances the yaw rate for both tractor-trailer by around 25% and the lateral acceleration by around 20% at manoeuvres. Also, the control can be tuned easily using MATLAB. Meanwhile, the electronic stability control scheme gives better performance than the combined active front steering and electronic stability control scheme

Advanced Sensing and Control for Connected and Automated Vehicles

Connected and automated vehicles (CAVs) are a transformative technology that is expected to change and improve the safety and efficiency of mobility. As the main functional components of CAVs, advanced sensing technologies and control algorithms, which gather environmental information, process data, and control vehicle motion, are of great importance. The development of novel sensing technologies for CAVs has become a hotspot in recent years. Thanks to improved sensing technologies, CAVs are able to interpret sensory information to further detect obstacles, localize their positions, navigate themselves, and interact with other surrounding vehicles in the dynamic environment. Furthermore, leveraging computer vision and other sensing methods, in-cabin humans’ body activities, facial emotions, and even mental states can also be recognized. Therefore, the aim of this Special Issue has been to gather contributions that illustrate the interest in the sensing and control of CAVs

Reliable Vehicle State and Parameter Estimation

Diverse vehicle active safety systems including vehicle electronic stability control (ESC) system, anti-lock braking system (ABS), and traction control system (TCS) are significantly relying on information about the vehicle's states and parameters, as well as the vehicle's surroundings. However, many important states or parameters, such as sideslip angle, tire-road friction coefficient, road gradient and vehicle mass are hard to directly measure, and hence advanced estimation algorithms are needed. Furthermore, enhancements of sensor technologies and the emergence of new concepts such as {\it Internet of Things} and their automotive version, {\it Internet of Vehicles}, facilitate reliable and resilient estimation of vehicle states and road conditions. Consequently, developing a resilient estimation structure to operate with the available sensor data in commercial vehicles and be flexible enough to incorporate new information in future cars is the main objective of this thesis. This thesis presents a reliable corner-based vehicle velocity estimation and a road condition classification algorithm. For vehicle velocity estimation, a combination of vehicle kinematics and the LuGre tire model is introduced in the design of a corner-based velocity observer. Moreover, the observability condition for both cases of time-invariant and parameter varying is studied. The effect of suspension compliance on enhancing the accuracy of the vehicle corner velocity estimation is also investigated and the results are verified via several experimental tests. The performance and the robustness of the proposed corner-based vehicle velocity estimation to model and road condition uncertainties is analyzed. The stability of the observer is discussed, and analytical expressions for the boundedness of the estimation error in the presence of system uncertainties for the case of fixed observer gains are derived. Furthermore, the stability of the observer under arbitrary and stochastic observer gain switching is studied and the performances of the observer for these two switching scenarios are compared. At the end, the sensitivity of the proposed observer to tire parameter variations is analyzed. These analyses are referred to as offline reliability methods. In addition to the off-line reliability analysis, an online reliability measure of the proposed velocity estimation is introduced, using vehicle kinematic relations. Moreover, methods to distinguish measurement faults from estimation faults are presented. Several experimental results are provided to verify the approach. An algorithm for identifying (classifying) road friction is proposed in this thesis. The analytical foundation of this algorithm, which is based on vehicle response to lateral excitation, is introduced and its performance is discussed and compared to previous approaches. The sensitivity of this algorithm to vehicle/tire parameter variations is also studied. At the end, various experimental results consisting of several maneuvers on different road conditions are presented to verify the performance of the algorithm