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

Torque vectoring based drive assistance system for turning an electric narrow tilting vehicle

The increasing number of cars leads to traffic congestion and limits parking issue in urban area. The narrow tilting vehicles therefore can potentially become the next generation of city cars due to its narrow width. However, due to the difficulty in leaning a narrow tilting vehicle, a drive assistance strategy is required to maintain its roll stability during a turn. This article presents an effective approach using torque vectoring method to assist the rider in balancing the narrow tilting vehicles, thus reducing the counter-steering requirements. The proposed approach is designed as the combination of two torque controllers: steer angle–based torque vectoring controller and tilting compensator–based torque vectoring controller. The steer angle–based torque vectoring controller reduces the counter-steering process via adjusting the vectoring torque based on the steering angle from the rider. Meanwhile, the tilting compensator–based torque vectoring controller develops the steer angle–based torque vectoring with an additional tilting compensator to help balancing the leaning behaviour of narrow tilting vehicles. Numerical simulations with a number of case studies have been carried out to verify the performance of designed controllers. The results imply that the counter-steering process can be eliminated and the roll stability performance can be improved with the usage of the presented approach

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

Dynamic Optimization Self-adaptive AI Controller for a Four-wheel Independent Drive Electric Rover

In this paper, a dynamic optimization self-adaptive controller for a four-wheel independent drive electric rover has been investigated to enhance the dynamic stability. The proposed self-adaptive AI controller is based on dynamic Fuzzy Logic (FL) control mechanism. The dynamic self-adaptive properties have been integrated into the proposed FL controller through a dynamically tuned Particle Swarm Optimization (PSO) mechanism. Nevertheless, the dynamic FL controller and the dynamic PSO mechanism has been synchronized together for every sampling instance k to obtain the optimum performance of the electric rover. In this electric rover, all the four wheels have a fixed orientation and each wheel powered by a 250-Watt Brushless Direct Current (BLDC) motor through separate gear ratio mechanisms to obtain the desired torque and angular velocity. Therefore, the steering mechanism was achieved in this rover through the proposed AI controller, which was based on the differential speed mechanism. However, this paper presents the control methodology and obtained test results related to straight road tests under different slippery road conditions. The rover test results show that on different slippery road conditions the proposed PSO based FL controller has maintained the wheel slip ratio of all the four wheels which was less than 0.35 approximately. Here, the translational speed has been limited to 40 km/hr approximately within its recorded top speed of 90 km/hr while maintaining the desired fix orientation

Research and Implement of PMSM Regenerative Braking Control for Electric Vehicle

As the society pays more and more attention to the environment pollution and energy crisis, the electric vehicle (EV) development also entered in a new era. With the development of motor speed control technology and the improvement of motor performance, although the dynamic performance and economical cost of EVs are both better than the internal-combustion engine vehicle (ICEV), the driving range limit and charging station distribution are two major problems which limit the popularization of EVs. In order to extend driving range for EVs, regenerative braking (RB) emerges which is able to recover energy during the braking process to improve the energy efficiency. This thesis aims to investigate the RB based pure electric braking system and its implementation. There are many forms of RB system such as fully electrified braking system and blended braking system (BBS) which is equipped both electric RB system and hydraulic braking (HB) system. In this thesis the main research objective is the RB based fully electrified braking system, however, RB system cannot satisfy all braking situation only by itself. Because the regenerating electromagnetic torque may be too small to meet the braking intention of the driver when the vehicle speed is very low and the regenerating electromagnetic torque may be not enough to stop the vehicle as soon as possible in the case of emergency braking. So, in order to ensure braking safety and braking performance, braking torque should be provided with different forms regarding different braking situation and different braking intention. In this thesis, braking torque is classified into three types. First one is normal reverse current braking when the vehicle speed is too low to have enough RB torque. Second one is RB torque which could recover kinetic energy by regenerating electricity and collecting electric energy into battery packs. The last braking situation is emergency where the braking torque is provided by motor plugging braking based on the optimal slip ratio braking control strategy. Considering two indicators of the RB system which are regenerative efficiency and braking safety, a trade-off point should be found and the corresponding control strategy should be designed. In this thesis, the maximum regenerative efficiency is obtained by a braking torque distribution strategy between front wheel and rear wheel based on a maximum available RB torque estimation method and ECE-R13 regulation. And the emergency braking performance is ensured by a novel fractional-order integral sliding mode control (FOISMC) and numerical simulations show that the control performance is better than the conventional sliding mode controller

Integrating Dynamics and Wear Modelling to Predict Railway Wheel Profile Evolution

The aim of the work described was to predict wheel profile evolution by integrating multi-body dynamics simulations of a wheelset with a wear model. The wear modelling approach is based on a wear index commonly used in rail wear predictions. This assumes wear is proportional to Tγ, where T is tractive force and γ is slip at the wheel/rail interface. Twin disc testing of rail and wheel materials was carried out to generate wear coefficients for use in the model. The modelling code is interfaced with ADAMS/Rail, which produces multi-body dynamics simulations of a railway wheelset and contact conditions at the wheel/rail interface. Simplified theory of rolling contact is used to discretise the contact patches produced by ADAMS/Rail and calculate traction and slip within each. The wear model combines the simplified theory of rolling contact, ADAMS/Rail output and the wear coefficients to predict the wear and hence the change of wheel profile for given track layouts

Integration of anti-lock braking system and regenerative braking for hybrid/electric vehicles

Vehicle electrification aims at improving energy efficiency and reducing pollutant emissions which creates an opportunity to use the electric machines (EM) as Regenerative Braking System (RBS) to support the friction brake system. Anti-lock Braking System (ABS) is part of the active safety systems that help drivers to stop safely during panic braking while ensuring the vehicle’s stability and steerability. Nevertheless, the RBS is deactivated at a safe (low) deceleration threshold in favour of ABS. This safety margin results in significantly less energy recuperation than what would be possible if both RBS and ABS were able to operate simultaneously. Vehicle energy efficiency can be improved by integrating RBS and friction brakes to enable more frequent energy recuperation activations, especially during high deceleration demands. The main aim of this doctoral research is to design and implement new wheel slip control with torque blending strategies for various vehicle topologies using four, two and one EM. The integration between the two braking actuators will improve the braking performance and energy efficiency of the vehicle. It also enables ABS by pure EM in certain situations where the regenerative brake torque is sufficient. A novelmethod for integrating the wheel slip control and torque blending is developed using Nonlinear Model Predictive Control (NMPC). The method is well known for the optimal performance and enforcement of critical control and state constraints. A linear MPC strategy is also developed for comparison purpose. A pragmatic brake torque blending algorithm using Daisy-Chain with sliding mode slip control is also developed based on a pre-defined energy recuperation priority. Simulation using high fidelity model using co-simulation in Matlab/Simulink and CarMaker is used to validate the developed strategies. Different test patterns are used to evaluate the controllers’ performance which includes longitudinal and lateral motions of the vehicle. Comparison analysis is done for the proposed strategies for each case. The capability for real-time implementation of the MPC controllers is assessed in simulation testing using dSPACE hardware

A robust super twisting fractional-order sliding mode-based control of vehicle longitudinal dynamic subjected to a constant actuator fault

This paper deals with the design and analysis of a super twisting fractional-order sliding mode controller (ST-FOSMC) to adjust the vehicle longitudinal dynamic when braking. While vehicle loading, road types, and modeling uncertainties are time-varying parameters, the control law must be robust against these disturbances. Also, the aging of the brake plate may introduce a difference between the control output and the actuator response that should be considered. The proposed control strategy has been used to enable the anti-lock braking system (ABS) to track the desired wheel slip value despite the presence of disturbances and constant actuator fault. The design of this controller is presented and the system stability is guaranteed by applying the Lyapunov theory. We carried out a simulation example that makes a comparison between our controller and the one based on the fractional-order sliding mode control to investigate which one of them outperforms the other. The results exhibit the superiority of the super twisting fractional order controller over the traditional fractional-order sliding mode controller during the braking phase

Yaw Rate and Sideslip Angle Control Through Single Input Single Output Direct Yaw Moment Control

Electric vehicles with independently controlled drivetrains allow torque vectoring, which enhances active safety and handling qualities. This article proposes an approach for the concurrent control of yaw rate and sideslip angle based on a single-input single-output (SISO) yaw rate controller. With the SISO formulation, the reference yaw rate is first defined according to the vehicle handling requirements and is then corrected based on the actual sideslip angle. The sideslip angle contribution guarantees a prompt corrective action in critical situations such as incipient vehicle oversteer during limit cornering in low tire-road friction conditions. A design methodology in the frequency domain is discussed, including stability analysis based on the theory of switched linear systems. The performance of the control structure is assessed via: 1) phase-plane plots obtained with a nonlinear vehicle model; 2) simulations with an experimentally validated model, including multiple feedback control structures; and 3) experimental tests on an electric vehicle demonstrator along step steer maneuvers with purposely induced and controlled vehicle drift. Results show that the SISO controller allows constraining the sideslip angle within the predetermined thresholds and yields tire-road friction adaptation with all the considered feedback controllers