The ability to compare multiple types of powertrain is important because a significant number of advanced vehicle configurations are available. Because of time and money constraints, it is impossible to build and test every one of these configurations. In addition, for each configuration, users need to be able to choose among different component models. To be able to make the right decisions, practitioners would benefit from a flexible simulation tool that allows for easier manipulation of different powertrain architectures and component size optimisations.
Additionally, literature survey has indicated that there are no tools at present that has the ability to simultaneously select the most appropriate powertrain architecture from a list of components, and then optimising the size of the components within the architecture, for a given cost function. Therefore, to pursue the creation of a better methodology for comparing different powertrain architectures, a simulation toolbox will be created as part of this research.
It has been identified that there are two methods for carrying out vehicle powertrain modelling: forward-facing and backward-facing models. Based on the literature, it is deduced that forward-facing models are is computationally more costly due to the need for smaller time-steps and higher order integration, and could be an issue in the context of iterative optimisation runs.
Modularity is important because this toolbox will need to offer the flexibility for completely defining the layout of the powertrain configuration. One approach to this can be seen by Hofman et al. [33], who described the usage of the QSS toolbox and
series, parallel and series-parallel, albeit running three independent simulations for achieving those results.
Practitioners, who will ultimately use the toolbox developed in this research, will have specific requirements. Thus, the proposed toolbox will need to have an open architecture and provide availability of source code to allow a significant amount of customisation. Users can replace the existing component models with more detailed models if necessary. Additionally, using MATLAB/Simulink as the backbone makes it possible to link to other software packages for component models [64]. Proprietary models can be compiled and linked to Simulink to protect intellectual property.
A deeper understanding of powertrains at the component level will need to be achieved. Therefore, discussions on fundamental understanding and underlying equations of the powertrain components will be carried out in the next chapter.
Finally, it is important to reiterate that this proposed tool is used to obtain a first-hand approximation for the type of powertrain that is most likely suitable or a given vehicle type and duty cycle. Thus, this research aims to produce a tool suitable for powertrain analysis, rather than specific powertrain design. It can be used to predict measures such as fuel consumption and emissions; however, it cannot be used to study details that require smaller time-steps, such as vibrations and NVH. Nevertheless, based on the results of the simulation using this tool, practitioners can then incorporate the findings into more appropriate simulation software to pursue such investigations.
Therefore, based on the finding of the literature survey, the aims of this research will be directed towards the following:
Develop a toolbox, which contains a novel methodology that combines both powertrain topology and component size optimisations
Utilise this toolbox to carry out novel comparative investigations, such as multi- objective optimisations for identifying trade-offs between cost functions by way of powertrain topology selection.
3 MODEL DEVELOPMENT
The previous chapter covered several simulations tools that are used for the purpose of powertrain simulation and optimisation. This chapter uses those findings to proceed with creating the powertrain component models, which will then be utilised in the forthcoming chapters for demonstrating the workings of the proposed methodology. The motivations behind developing the models in-house were two-folds; it enabled understanding of fundamental principles in powertrain modelling and it provided the flexibility to implement the models more intrinsically within the proposed powertrain optimisation algorithms.
The models will be created in the MATLAB/Simulink environment, consistent with the modelling environment that were utilised by the commercial powertrain simulation tools discussed in the previous chapter. To address the hypothesis set forth in the introduction, this research will focus on developing a methodology that facilitates the comparative analysis of multiple powertrain architectures. The powertrain component models that are described in this chapter will be used to illustrate the workings of this methodology. Additionally, the use of uniform power-based interface between the models will allow for flexibility in incorporating other types of powertrain components that are not described here, such as different battery chemistries or different types of ICEs.
To enable powertrain architectures to be categorised as shown in Section 2.2, the powertrain components will be modular in implementation, in order to allow for interchangeability across different topologies. To allow such levels of interchange- ability, it is determined that the communication between each energy storage and energy converter device also has to be standardised. This is achieved using power bonds, similar to the concept of bond graphs [76]. The Modular Powertrain Structure (MPS), which then facilitates the interchangeability between powertrain components, is discussed further in Chapter 4. In addition to being modular, the powertrain models will also need to be scalable and have sufficient fidelity to capture the efficiency and operating envelops of the respective powertrain component.
Therefore, each powertrain component is treated as a module and is designed to be both modular and scalable in implementation, an example of which was shown by Mason et
al. [77]. Methodology for sizing powertrain components as a part of an optimisation
routine has been covered in literature [66; 78], and a similar approach will be employed in this research.
Powertrain components can be divided into three main categories [62]; energy storage, energy converter, and power transformers. The fundamental understanding and underlying equations of the powertrain models will be discussed in the rest of this chapter.