Validation of a hybrid electric vehicle dynamics model for energy management and vehicle stability control

A Simulink Hybrid Electric Vehicle dynamics model for the control of energy management and vehicle stability is developed. The model encompasses a transitional vehicle speed input parameterized by the New European Driving Cycle. Internal combustion engine torque, motor torque and varying corner radii are set to the same time constraints as the drive cycle. Lateral acceleration, yaw rate and tyre data are validated against measured car data, resulting in a simulation model that can be utilised (with modifications) as a tool to determine stability control and power deployment for front-wheel, rear-wheel or all-wheel drive hybrid vehicles. The model yields similar outputs to a driven vehicle’s normal measured responses

Model development and energy management control for hybrid electric race vehicles

A Hybrid Electric Vehicle longitudinal dynamics model for the control of energy management is developed. The model is implemented using Simulink and consists of a transitional vehicle speed input parameterized by, for example, the New European Driving Cycle. It is a backward looking model in that engine and motor on/off states are determined by the controller, dependent on wheel torque requirements and output targets. The objective of the simulation is to calculate tractive effort and resistance forces to determine longitudinal net vehicle force at the road. This article addresses model development and initial investigations of its dynamic behaviour in order to establish appropriate energy management strategies for the Hybrid Electric system. In particular, All Wheel Drive, Front Wheel Drive and Rear Wheel Drive drivetrain architectures are evaluated to determine minimum fuel usage and battery state of charge. The use of a logic controller allows a reduction of simulation time and ensures accurate results for charge depletion and harvesting. Simulated fuel consumption is within 1% of actual usage

Adaptive Integral Terminal Sliding Mode Control for the Nonlinear Active Vehicle Suspension System under External Disturbances and Uncertainties

Suspension system is one of the most effective vehicle components that play an essential role in the stability and comfort of the vehicle. The passive suspension can not fully meet a car's stability and comfort requirements. Instead, an active suspension system has been proposed to improve these challenges. Active suspension minimizes the vibrations entering the body using a closed-loop control system. To this end, in this research, an integral terminal sliding mode control (integral TSMC) for an active nonlinear car suspension system under external disturbances and uncertainties is designed. First, the integral TSMC is designed to deal with the uncertainties and the external disturbances in the system when the upper bound is known. Next, an adaptation law is recommended to estimate the upper bound of uncertainties and external disturbances. The results show that the proposed integral TSMC improves the convergence rate and tracking error of the closed-loop system. The stability of the nonlinear control system is investigated and proven using Lyapunov's stability theory. The numerical results indicate a good robust performance and stability for the proposed controller for the nonlinear suspension system with different road profiles in the presence of uncertainties and external disturbances. From the results, it can also be understood that important measures such as ride comfort, road holding, and mechanical structural limitations are met using the proposed 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

Integrated optimisation for dynamic modelling, path planning and energy management in hybrid race vehicles

Simulation software has for many years been developed to enhance the research and development phase of new vehicle introductions. With the introduction of the testing embargo in most forms of world championship motorsport, model validation is a necessity. To optimise the unknown vehicle and tyre parameters and to reduce the error between measured and simulated data in such a multi-input multi-output non-convex optimisation problem, a novel multi-objective particle swarm optimisation (PSO) technique is applied to ensure a fully validated vehicle model is developed and analysed for speed and performance. These optimisation algorithms are further developed to explore the trajectory planning problem to improve the lap time for the shortest path, minimum curvature and a combined approach, producing optimal racing line pathways and vehicle dynamic inputs and output responses by exploring trajectories and vehicle traction circle limits. Finally, a hybrid electric vehicle transient dynamics model for the control of energy management is presented. The hybrid powertrain contains an internal combustion engine, kinetic energy recovery system and heat energy recovery system with deployment and harvesting control parameters. The performance of single-objective and multi-objective particle swarm optimisation algorithms are compared and analysed. The proposed simulation model and optimisation techniques are applied to address an array of problems, including model validation, racing line trajectory design, fastest lap time problem, and energy management strategies. All results are validated and optimised with respect to the experimental data collected on the real track in Silverstone to ensure the results can be applied to physical real-world scenarios