School of Engineering (SoE)

Permanent URI for this community

https://dspace.lib.cranfield.ac.uk/handle/1826/16

Browse

Browsing School of Engineering (SoE) by Supervisor "Assadian, Francis"

Now showing 1 - 3 of 3
  • Thumbnail Image
    ItemOpen Access
    Journey predictive energy management strategy for a plug-in hybrid electric vehicle
    (Cranfield University, 2013-05) Dharmaraj Ram Manohar, Ravi Shankar; Marco, J.; Assadian, Francis
    The adoption of Plug-in Hybrid Electric Vehicles (PHEVs) is widely seen as an interim solution for the decarbonisation of the transport sector. Within a PHEV, determining the required energy storage capacity of the battery remains one of the primary concerns for vehicle manufacturers and system integrators. This fact is particularly pertinent since the battery constitutes the largest contributor to vehicle mass. Furthermore, the financial cost associated with the procurement, design and integration of battery systems is often cited as one of the main barriers to vehicle commercialisation. The ability to integrate the optimization of the energy management control system with the sizing of key PHEV powertrain components presents a significant area of research. Further, recent studies suggest the use of \intelligent transport" infrastructure to include a predictive element to the energy management strategy to achieve reductions in emissions. The thesis addresses the problem of determining the links between component-sizing, real-world usage and energy management strategies for a PHEV. The objective is to develop an integrated framework in which the advantages of predictive energy management can be realised by component downsizing for a PHEV. The study is spilt into three sections. The first part presents the framework by which the predictive element can be included into the PHEV's energy management strategy. Second part describes the development of the PHEV component models and the various energy management strategies which control the split in energy used between the engine and the battery. In this section a new control strategy is presented which integrates the predictive element proposed in the first part. Finally, in the third section an optimisation framework is presented by which the size of the components within the PHEV are reduced due to the lower energy demands of the new proposed energy management strategy. The first part of the study presents a framework by which the energy consumption of a vehicle may be predicted over a route. The proposed energy prediction framework employs a neural network and was used o_-line for estimating the real-world energy consumption of the vehicle so that it can be later integrated within the vehicles energy management control system. Experimental results show an accuracy within 20%-30% when comparing predicted and measured energy consumptions for over 800 different real-world EV journeys … [cont.].
  • Thumbnail Image
    ItemOpen Access
    Optimal time and handling methods for motorsport differentials.
    (Cranfield University, 2014-12) Tremlett, Anthony; Assadian, Francis; Vaughan, Nicholas D.
    In the motorsport environment, where traction at one wheel is often compromised due to high cornering accelerations, Limited Slip Differentials (LSD) offer significant improvements in traction and vehicle stability. LSDs achieve these performance benefits through the transfer of torque from the faster to slower rotating driving wheel. In the majority of racing formulae, modern devices have evolved to become highly adjustable, allowing this torque bias to alter both ultimate vehicle performance and handling balance through specific corner entry, apex and corner exit phases. This work investigates methods to optimise LSD setup parameters, both for minimum lap time and desirable handling characteristics. The first stage of addressing this objective involved the creation of a range of contemporary motorsport LSD models. These included a plate or Salisbury type, a Viscous Coupling (VC) and a Viscous Combined Plate (VCP). A differential test rig was developed to validate these models. The parameter optimisation is addressed in two main parts. Firstly, a Quasi Steady State (QSS) time optimal method is used to maximise the vehicle's GG acceleration envelope using a direct, nonlinear program (NLP). A limitation of this approach however, is that system transients are neglected. This is addressed through the development of an alternative indirect, nonlinear optimal control (NOC) method. Both methods were able to find LSD setup parameters which minimised lap time, providing significant improvements over traditional open and locked devices. The NOC method however, was able to give greater insight into how a locked device ultimately limits the vehicle yaw response during quick direction changes. The time optimal analysis was extended to investigate aspects of vehicle stability and agility. These factors are known to have a major influence on driveability and thus, how much of the theoretical performance limit the driver can extract. A more unified GG diagram framework was implemented, to characterise both the vehicle's acceleration limits, and how its stability and agility changes leading up to this limit. The work has generated a number of novel contributions in this research field. Firstly, the creation and validation of a range of state-of-the-art motorsport LSD models. Secondly, the methodologies used to optimise LSD setup parameters, the results from which, have themselves provided the basis of a novel, vehicle speed dependent LSD device. Finally, a more practical and intuitive way to evaluate vehicle stability and agility at different cornering phases. This has laid the foundations of a procedure which not only maximises the vehicle's acceleration limits, but also allows its response to be tailored to suit individual driver preferences.
  • Thumbnail Image
    ItemOpen Access
    A toolbox for multi-objective optimisation of low carbon powertrain topologies
    (Cranfield University, 2016-05) Mohan, Ganesh; Assadian, Francis; Longo, Stefano
    Stricter regulations and evolving environmental concerns have been exerting ever-increasing pressure on the automotive industry to produce low carbon vehicles that reduce emissions. As a result, increasing numbers of alternative powertrain architectures have been released into the marketplace to address this need. However, with a myriad of possible alternative powertrain configurations, which is the most appropriate type for a given vehicle class and duty cycle? To that end, comparative analyses of powertrain configurations have been widely carried out in literature; though such analyses only considered limited types of powertrain architectures at a time. Collating the results from these literature often produced findings that were discontinuous, which made it difficult for drawing conclusions when comparing multiple types of powertrains. The aim of this research is to propose a novel methodology that can be used by practitioners to improve the methods for comparative analyses of different types of powertrain architectures. Contrary to what has been done so far, the proposed methodology combines an optimisation algorithm with a Modular Powertrain Structure that facilitates the simultaneous approach to optimising multiple types of powertrain architectures. The contribution to science is two-folds; presenting a methodology to simultaneously select a powertrain architecture and optimise its component sizes for a given cost function, and demonstrating the use of multi-objective optimisation for identifying trade-offs between cost functions by powertrain architecture selection. Based on the results, the sizing of the powertrain components were influenced by the power and energy requirements of the drivecycle, whereas the powertrain architecture selection was mainly driven by the autonomy range requirements, vehicle mass constraints, CO2 emissions, and powertrain costs. For multi-objective optimisation, the creation of a 3-dimentional Pareto front showed multiple solution points for the different powertrain architectures, which was inherent from the ability of the methodology to concurrently evaluate those architectures. A diverging trend was observed on this front with the increase in the autonomy range, driven primarily by variation in powertrain cost per kilometre. Additionally, there appeared to be a trade-off in terms of electric powertrain sizing between CO2 emissions and lowest mass. This was more evident at lower autonomy ranges, where the battery efficiency was a deciding factor for CO2 emissions. The results have demonstrated the contribution of the proposed methodology in the area of multi-objective powertrain architecture optimisation, thus addressing the aims of this research.