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-body dynamics: historical evolution and application

The historical developments in the discipline of engineering dynamics are briefly reviewed, with attention paid to the formulation and solution of the dynamic behaviour of multi-body systems. It is shown that the dynamic characteristics of practical multi-body systems are dependent upon the interactions of many physical phenomena that can induce, restrain or constrain motion of parts. The long process of understanding and formulating the physics of multi-body motions, in some cases with pioneering contributions centuries old, together with continual refinements in numerical techniques and enhanced computing power has resulted in the solution of quite complex and practical engineering problems. Linking the historical developments to the fundamental physical phenomena and their interactions, the paper presents solutions to two complex multi-body dynamic problems. The practical implications of the approach in design of these systems are highlighted

Identifying tyre models directly from vehicle test data using an extended Kalman filter

Individual tyre models are traditionally derived from component tests, with their parameters matched to force and slip measurements. They are imported into vehicle models which should, but do not always properly provide suspension geometry interaction. Recent advances in Global Positioning System (GPS)/inertia vehicle instrumentation now make full state measurement viable in test vehicles, so tyre slip behaviour is directly measurable. This paper uses an extended Kalman filter for system identification, to derive individual load-dependent tyre models directly from these test vehicle state measurements. The resulting model therefore implicitly compensates for suspension geometry and compliance. The paper looks at two variants of the tyre model, and also considers real-time adaptation of the model to road surface friction variations. Test vehicle results are used exclusively, and the results show successful tyre model identification, improved vehicle model state prediction – particularly in lateral velocity reproduction – and an effective real-time solution for road friction estimation

Multiple-Model Robust Adaptive Vehicle Motion Control

An improvement in active safety control systems has become necessary to assist drivers in unfavorable driving conditions. In these conditions, the dynamic of the vehicle shows rather different respond to driver command. Since available sensor technologies and estimation methods are insufficient, uncertain nonlinear tire characteristics and road condition may not be correctly figured out. Thus, the controller cannot provide the appropriate feedback input to vehicle, which may result in deterioration of controller performance and even in loss of vehicle control. These problems have led many researchers to new active vehicle stability controllers which make vehicle robust against critical driving conditions like harsh maneuvers in which tires show uncertain nonlinear behaviour and/or the tire-road friction coefficient is uncertain and low. In this research, the studied vehicle has active front steering system for driver steer correction and in-wheel electric motors in all wheels to generate torque vector at vehicle center of gravity. To address robustness against uncertain nonlinear characteristics of tire and road condition, new blending based multiple-model adaptive schemes utilizing gradient and recursive least squares (RLS) methods are proposed for a faster system identification. To this end, the uncertain nonlinear dynamics of vehicle motion is addressed as a multiple-input multiple-output (MIMO) linear system with polytopic parameter uncertainties. These polytopic uncertainties denote uncertain variation in tire longitudinal and lateral force capacity due to nonlinear tire characteristics and road condition. In the proposed multiple-model approach, a set of fixed linear parametric identifi cation models are designed in advance, based on the known bounds of polytopic parameter set. The proposed adaptive schemes continuously generates a weighting vector for blending the identifi cation model to achieve the true model (operation condition) of the vehicle. Furthermore, the proposed adaptive schemes are generalized for MIMO systems with polytopic parameter uncertainties. The asymptotic stability of the proposed adaptive identifi cation schemes for linear MIMO systems is studied in detail. Later, the proposed blending based adaptive identi fication schemes are used to develop Linear Quadratic (LQ) based multiple-model adaptive control (MMAC) scheme for MIMO systems with polytopic parameter uncertainties. To this end, for each identi fication model, an optimal LQ controller is computed on-line for the corresponding model in advance, which saves computation power during operation. The generated control inputs from the set of LQ controllers is being blended on-line using weighting vector continuously updated by the proposed adaptive identifi cation schemes. The stability analysis of the proposed LQ based optimal MMAC scheme is provided. The developed LQ based optimal MMAC scheme has been applied to motion control of the vehicle. The simulation application to uncertain lateral single-track vehicle dynamics is presented in Simulink environment. The performances of the proposed LQ based MMAC utilizing RLS and gradient based methods have been compared to each other and an LQ controller which is designed using the same performance matrices and fixed nominal values of the uncertain parameters. The results validated the stability and effectiveness of the proposed LQ based MMAC algorithm and demonstrate that the proposed adaptive LQ control schemes outperform over the LQ control scheme for tracking tasks. In the next step, we addressed the constraints on actuation systems for a model predictive control (MPC) based MMAC design. To determine the constraints on torque vectoring at vehicle center of gravity (CG), we have used the min/max values of torque and torque rate at each corner, and the vehicle kinematic structure information. The MPC problem has been redefi ned as a constrained quadratic programming (QP) problem which is solved in real-time via interior-point algorithm by an embedded QP solver using MATLAB each time step. The solution of the designed MPC based MMAC provides total steering angle and desired torque vector at vehicle CG which is optimally distributed to each corner based on holistic corner control (HCC) principle. For validation of the designed MPC based MMAC scheme, several critical driving scenarios has been simulated using a high- fidelity vehicle simulation environment CarSim/Simulink. The performance of the proposed MPC based MMAC has been compared to an MPC controller which is designed for a wet road condition using the same tuning parameters in objective function design. The results validated the stability and effectiveness of the proposed MPC based MMAC algorithm and demonstrate that the proposed adaptive control scheme outperform over an MPC controller with fixed parameter values for tracking tasks

Dual Loop Rider Control of a Dynamic Motorcycle Riding Simulator

Compared to the automotive industry, the use of simulators in the motorcycle domain is negligible as for their lack of usability and accessibility. According to the state-of-the-art, it is e.g. not possible for motorcyclists to intuitively control a high-fidelity dynamic motorcycle riding simulator when getting in contact with it for the first time. There are four main reasons for the insufficient simulation quality of dynamic motorcycle riding simulators: ▪ The instability of single-track vehicles at low speed, ▪ The steering force-feedback with highly velocity-dependent behavior, ▪ Motion-simulation (high dynamics, roll angle, direct contact to the environment), ▪ The specific influence of the rider to vehicle dynamics (incl. rider motion). The last bullet point is peculiar for motorcycles and dynamic motorcycle riding simulators in comparison with other vehicle simulators, as motorcycles are significantly affected in their dynamics by the rider’s body motion. However, up until today, almost no special emphasis has been put on the consideration of rider motion on dynamic motorcycle riding simulators. In this thesis, a motorcycle riding simulator is designed, constructed and put into operation. The focus here is attaching a real rider to a virtual motorcycle. Based on a commercially available multi-body-simulation model, a simulator architecture is designed, that allows to control the virtual motorcycle not only by steering, but by rider leaning as well. This is realized by determining the so-called rider induced roll torque, that allows a holistic measurement of the apparent coupling forces between rider and simulator mockup. Performance measures and study concepts are developed that allow to rate the system. In expert and participant studies, the influence of the system on the riding behavior of the simulator is investigated. It is shown that the rider motion determination allows realistic control inputs and has a positive effect on the stabilization at various velocities. The feedback of the rider induced roll torque to the virtual dynamics model allows study participants to control the virtual motorcycle more intuitively. The vehicle states during cornering are affected as expected from real riding. First results indicate that it becomes easier for naïve study participants to access the simulator in first-contact scenarios. The achieved improvements regarding the rideability of the simulator however do not suffice to overcome the abovementioned challenges to a degree that allows for a completely intuitive interaction with the simulator throughout the whole dynamic range

Smartphone-based vehicle telematics: a ten-year anniversary

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordJust as it has irrevocably reshaped social life, the fast growth of smartphone ownership is now beginning to revolutionize the driving experience and change how we think about automotive insurance, vehicle safety systems, and traffic research. This paper summarizes the first ten years of research in smartphone-based vehicle telematics, with a focus on user-friendly implementations and the challenges that arise due to the mobility of the smartphone. Notable academic and industrial projects are reviewed, and system aspects related to sensors, energy consumption, and human-machine interfaces are examined. Moreover, we highlight the differences between traditional and smartphone-based automotive navigation, and survey the state of the art in smartphone-based transportation mode classification, vehicular ad hoc networks, cloud computing, driver classification, and road condition monitoring. Future advances are expected to be driven by improvements in sensor technology, evidence of the societal benefits of current implementations, and the establishment of industry standards for sensor fusion and driver assessment

Reconfigurable Integrated Vehicle Stability Control Using Optimal Control Techniques

The motivation for the development of vehicle stability control systems comes from the fact that vehicle dynamic behavior in unfavorable driving conditions such as low road-tire adhesion and high speed differs greatly from its nominal behavior. Due to this unexpected behavior, a driver may not be successful in controlling the vehicle in challenging driving situations based only on her/his everyday driving experience. Several noteworthy research works have been conducted on stability control systems over the last two decades to prevent car accidents due to human error. Most of the resultant stability controllers contain individual modules, where each perform a particular task such as yaw tracking, sideslip control, or wheel slip control. These design requirements may contradict each other in some driving scenarios. In such situations, inconsistent control actions can be generated with individual modules. The development of a stability controller that can satisfy diverse and often contradictory requirements is a great challenge. In general, transferring a control structure from one vehicle to another with a different drivetrain layout and actuation system configuration requires remarkable rectifications and repetition of tuning processes from the beginning to achieve a similar performance. This can be considered to be a serious drawback for car manufacturing companies since it results in extra effort, time, and expenses in redesigning and retuning the controller. In this thesis, an integrated controller with a modular structure has been designed to concurrently provide control of the vehicle chassis (yaw rate and sideslip control) and wheel stability (wheel slip ratio control). The proposed control structure incorporates longitudinal and lateral vehicle dynamics to decide on a unified control action. This control action is an outcome of solving an optimization problem that considers all the control objectives in a single cost function, so integrated wheel and vehicle stability is guaranteed. Moreover, according to the particular modular design of the proposed control structure, it can be easily reconfigured to work with different drivetrain layouts such as all-wheel-drive, front-wheel-drive, and rear-wheel-drive, as well as various actuators such as torque vectoring, differential braking, and active steering systems. The high-level control module provides a Center of Gravity (CG) based error analysis and determines the required longitudinal forces and yaw moment adjustments. The low-level control module utilizes this information to allocate control actions optimally at each vehicle corner (wheel) through a single or multi-actuator regime. In order to consider the effect of the actuator dynamics, a mathematical description of the auction system is included in distribution objective function. Therefore, a legitimate control performance is promised in situations requiring shifting from one configuration to another with minimal modifications. The performance of the proposed modular control structure is examined in simulations with a high-fidelity model of an electric GM Equinox vehicle. The high-fidelity model has been developed and provided by GM and the use of the model is to reduce the number of labor-intensive vehicle test and is to test extreme and dangerous driving conditions. Several driving scenarios with severe steering and throttle commands, then, are designed to evaluate the capability of the proposed control structure in integrated longitudinal and lateral vehicle stabilization on slippery road condition. Experimental tests also have been performed with two different electric vehicles for real-time implementation as well as validation purposes. The observations verified the performance qualifications of the proposed control structure to preserve integrated wheel and vehicle chassis stability in all track tests