Simultaneous Plant/Controller Optimization of Traction Control for Electric Vehicle

Development of electric vehicles is motivated by global concerns over the need for environmental protection. In addition to its zero-emission characteristics, an electric propulsion system enables high performance torque control that may be used to maximize vehicle performance obtained from energy-efficient, low rolling resistance tires typically associated with degraded road-holding ability. A simultaneous plant/controller optimization is performed on an electric vehicle traction control system with respect to conflicting energy use and performance objectives. Due to system nonlinearities, an iterative simulation-based optimization approach is proposed using a system model and a genetic algorithm (GA) to guide search space exploration. The system model consists of: a drive cycle with a constant driver torque request and a step change in coefficient of friction, a single-wheel longitudinal vehicle model, a tire model described using the Magic Formula and a constant rolling resistance, and an adhesion gradient fuzzy logic traction controller. Optimization is defined in terms of the all at once variable selection of: either a performance oriented or low rolling resistance tire, the shape of a fuzzy logic controller membership function, and a set of fuzzy logic controller rule base conclusions. A mixed encoding, multi-chromosomal GA is implemented to represent the variables, respectively, as a binary string, a real-valued number, and a novel rule base encoding based on the definition of a partially ordered set (poset) by delta inclusion. Simultaneous optimization results indicate that, under straight-line acceleration and unless energy concerns are completely neglected, low rolling resistance tires should be incorporated in a traction control system design since the energy saving benefits outweigh the associated degradation in road-holding ability. The results also indicate that the proposed novel encoding enables the efficient representation of a fix-sized fuzzy logic rule base within a GA

Smart Traction Control Systems for Electric Vehicles Using Acoustic Road-type Estimation

The application of traction control systems (TCS) for electric vehicles (EV) has great potential due to easy implementation of torque control with direct-drive motors. However, the control system usually requires road-tire friction and slip-ratio values, which must be estimated. While it is not possible to obtain the first one directly, the estimation of latter value requires accurate measurements of chassis and wheel velocity. In addition, existing TCS structures are often designed without considering the robustness and energy efficiency of torque control. In this work, both problems are addressed with a smart TCS design having an integrated acoustic road-type estimation (ARTE) unit. This unit enables the road-type recognition and this information is used to retrieve the correct look-up table between friction coefficient and slip-ratio. The estimation of the friction coefficient helps the system to update the necessary input torque. The ARTE unit utilizes machine learning, mapping the acoustic feature inputs to road-type as output. In this study, three existing TCS for EVs are examined with and without the integrated ARTE unit. The results show significant performance improvement with ARTE, reducing the slip ratio by 75% while saving energy via reduction of applied torque and increasing the robustness of the TCS.Comment: Accepted to be published by IEEE Trans. on Intelligent Vehicles, 22 Jan 201

Urban and extra-urban hybrid vehicles: a technological review

Pollution derived from transportation systems is a worldwide, timelier issue than ever. The abatement actions of harmful substances in the air are on the agenda and they are necessary today to safeguard our welfare and that of the planet. Environmental pollution in large cities is approximately 20% due to the transportation system. In addition, private traffic contributes greatly to city pollution. Further, “vehicle operating life” is most often exceeded and vehicle emissions do not comply with European antipollution standards. It becomes mandatory to find a solution that respects the environment and, realize an appropriate transportation service to the customers. New technologies related to hybrid –electric engines are making great strides in reducing emissions, and the funds allocated by public authorities should be addressed. In addition, the use (implementation) of new technologies is also convenient from an economic point of view. In fact, by implementing the use of hybrid vehicles, fuel consumption can be reduced. The different hybrid configurations presented refer to such a series architecture, developed by the researchers and Research and Development groups. Regarding energy flows, different strategy logic or vehicle management units have been illustrated. Various configurations and vehicles were studied by simulating different driving cycles, both European approval and homologation and customer ones (typically municipal and university). The simulations have provided guidance on the optimal proposed configuration and information on the component to be used

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

Simulation of Electric Vehicles Combining Structural and Functional Approaches

In this paper the construction of a model that represents the behavior of an Electric Vehicle is described. Both the mechanical and the electric traction systems are represented using Multi-Bond Graph structural approach suited to model large scale physical systems. Then the model of the controllers, represented with a functional approach, is included giving rise to an integrated model which exploits the advantages of both approaches. Simulation and experimental results are aimed to illustrate the electromechanical interaction and to validate the proposal.Fil: Silva, Luis Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Rio Cuarto. Facultad de Ingeniería. Grupo de Electronica Aplicada; ArgentinaFil: Magallán, Guillermo Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Rio Cuarto. Facultad de Ingeniería. Grupo de Electronica Aplicada; ArgentinaFil: de la Barrera, Pablo Martin. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Rio Cuarto. Facultad de Ingeniería. Grupo de Electronica Aplicada; ArgentinaFil: de Angelo, Cristian Hernan. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Rio Cuarto. Facultad de Ingeniería. Grupo de Electronica Aplicada; ArgentinaFil: Garcia, Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Rio Cuarto. Facultad de Ingeniería. Grupo de Electronica Aplicada; Argentin

Slip and Adhesion in a Railway Wheelset Simulink Model Proposed for Detection Driving Conditions Via Neural Networks

Constantly enlarging operation of locomotives with a very high tractive power in modern railway transport has caused problems with optimal supplying torque from motor to wheel-sets. Losses emerging with inadequate torque values lead to wheel slipping connected with excessive wear and limited acceleration. In models simulating dynamics of torque transmission from the drive units to wheels, the most important are the submodel of the drive and the submodel of balance between traction forces and drive resistances. Some issues of this field studied within a PhD program and SGS (CTU Students Grant Competition) has been focused on increasing quality of these submodels. This contribution is aimed at an innovated part in the existing Simulink model utilizing new data sources and modeling techniques. This improvement supports application of operating point detection methods based on machine learning techniques. New control facilities provided with pulse-width modulated frequency control of the asynchronous motor will be used for automatic submission of optimal operating points. The idea of utilization of via simulation obtained data is an on-line training of polynomial neural unit as an approximation of current driving conditions.Neustále narůstající provoz lokomotiv s velmi vysokým trakčním výkonem v moderní železniční dopravě způsobuje problémy s přenosem optimálního hnacího momentu z motoru na dvojkolí. Ztráty vyplývající z nevhodných hodnot točivého momentu vedou k prokluzu kol spojeným s nadměrným opotřebením a omezeným zrychlením. V modelech simulujících dynamiku přenosu točivého momentu z pohonné jednotky na dvojkolí jsou nejdůležitější submodely pohonu a rovnováhy mezi trakčními silami a jízdními odpory. Výzkum prováděný v rámci doktorských studijních programů a SGS (Studentská grantová soutěž ČVUT) se zaměřuje na zvyšování kvality těchto submodelů. Tento příspěvek je zaměřen na inovovanou část v existujícím Simulink modelu využívajícím nové zdroje dat a technik modelování. Nové možnosti regulace zajištěné pulzně-šířkovou frekvenční regulací asynchronního motoru budou použity pro automatické poskytnutí optimálních provozních bodů. Představa využití simulací získaných dat je on-line učení polynomické neuronové jednotky jako aproximace současných jízdních podmínek

Fuzzy Logic Maximum Structure and State Feedback Control Strategies of the Electrical Car

AbstractThis paper treats the design and control of different models and control strategies for an Electric Vehicle (EV). An hybrid controller is designed using a fuzzy logic integrated in Maximum Control Structure (FL-MCS), the FL nonlinear controller involves online estimation of the total reference force which corresponds to a torque reference to be applied to MCS. The second proposed regulator is a states feedback controller using the Linear Quadratic Regulation (LQR) to optimise and to determine the feedback control parameters. The LQR allows reducing the consumption of the energy according to the desired EV's dynamic performances, these lasts can be changed depending on the choice of Q and R matrices. In this work, we apply and validate the proposed control strategies by a comparison between our simulation results and the results of the classical MCS, which has been developed by L2EP (Lille, France) to control the EV speed under Matlab/Simulink

MODELING AND SIMULATION OF PM MOTOR TESTING ENVIRONMENT TOWARDS EV APPLICATION CONSIDERING ROAD CONDITIONS

The electric vehicle (EV) performance testing is an indispensable aspect of the design study and marketing of electric vehicle. The development of a suitable electric motor testing environment for EVs is very significant. On the one hand, it provides a relatively realistic testing environment for the study of the key technologies of electric vehicles, and it also plays an essential role in finding a reasonable and reliable optimization scheme. On the other hand, it provides a reference to the evaluation criteria for the products on the market. This thesis is based on such requirements to model and simulate the PM motor testing environment towards EV applications considering road conditions. Firstly, the requirements of the electric motor drive as a propulsion system for EV applications are investigated by comparing to that of the traditional engine as a propulsion system. Then, as the studying objective of this work, the mathematical model of PMSM is discussed according to three different coordinate systems, and the control strategy for EV application is developed. In order to test the PM motor in the context of an EV, a specific target vehicle model is needed as the virtual load of the tested motor with the dyno system to emulate the real operating environment of the vehicle. A slippery road is one of the severe driving conditions for EVs and should be considered during the traction motor testing process. Fuzzy logic based wheel slip control is adopted in this thesis to evaluate the PM motor performance under slippery road conditions. Through the proposed testing environment, the PM motor can be tested in virtual vehicle driving conditions, which is significant for improving the PM motor design and control