The programme of research between the university and Siemens Industry Software NV offers a unique opportunity to gain both academic and industrial experience in vehicle dynamics modelling and test.
Vehicle passenger safety is a very important issue. With around 1.5 billion vehicles in the world, many people drive a car every day. Active safety systems such as the Electronic Stability Control (ESC) are designed to assist the driver when certain situations occur and improve the safety of all vehicle’s occupants. However such active safety systems must operate in complex environments and in all possible driving conditions to properly make safety-critical decisions. This implies that numerous test scenarios need to be considered during the development phase. Model-Based System Engineering (MBSE) has recently been introduced for the development of such systems, where plenty of different scenarios can be tested in a virtual environment. A rigorous and reliable model of the vehicle is therein developed, along with a model of the active safety system and of the environment. Those elements constitute the previously mentioned virtual environment in which the performance of the active safety system can be extensively simulated.
In order to achieve the above, there is a need for representative and reliable simulation models of the vehicle under development. State-parameter estimation algorithmscan be used to properly identify relevant vehicle parameters by making use of specific vehicle models in combination with dedicated sensors data gathered during appropriate testing. Such vehicle parameters might be of different nature, including e.g. tyre parameters, as well as kinematics and inertial parameters. Generally there are several possibilities in relation to model architectures, estimation schemes and dedicated sensors layout. In any case, only a limited amount of parameters is available with the current technology. The knowledge of a larger amount of parameters with respect to the current state-of-the-art is challenging, but it would allow the design of advanced active safety systems (e.g. evolutions of the current ESC). As a result, vehicle passenger safety could be significantly enhanced.
The aim of this PhD project is to research and develop advanced vehicle modeling tools, control and estimation strategies to reliably identify appropriate vehicle characteristics and behaviour. The identified characteristics and vehicle behaviour can then be implemented in a MBSE framework for the reliable assessment of the performance of novel active safety systems for a high number of scenarios and conditions. By allowing to validate the effectiveness of novel active safety systems, the project will contribute to their implementation, thereby contributing to the reduction of fatal road accidents.
The main objectives of this PhD project are:
- Research on vehicle model and behaviour identification methods for several key vehicle parameters and phenomena (e.g. tyre parameters, lateral dynamics, kinematic and inertial parameters, steering dynamics effects)
- Investigate the necessary features in terms of model, estimator and sensors, aimed at a successful and meaningful estimation of the relevant quantities and phenomena, to be used in the development and assessment of novel active safety systems
- Validate the developed methodologies on a demonstrator based on full in-vehicle testing supported by Siemens
The candidate will be based at the university and will also be hosted for 12-18 months at the industrial premises in Belgium, to apply the theoretical findings to a real vehicle case.
PLEASE NOTE: The anticipated start date for will be October 2020.
For information about how to apply, entry requirements, tuition fees and other costs please visit https://www.shu.ac.uk/research/degrees/apply
For further information, please contact Dr Basilio Lenzo [email protected]
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• All applicants should hold a strong undergraduate degree (2.1 or above) and/or a relevant Masters qualification (or expectation of the same).
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