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This project aims to achieve various goals related to the selection and optimisation of powertrain components in a hybrid powertrain system. To achieve these objectives, automatic generation, calibration, and comparison of algorithms have been implemented. This research offers the opportunity to optimise multiple targets, such as state of charge (SOC), components selection, CO2 emissions, drive modes, and driveability for McLaren's hybrid powertrain system. The robustness of the Equivalent Consumption Minimisation Strategy (ECMS) is generated automatically for different optimisation objectives, and robustness validation is performed. Additionally, automatic calibration of the torque split strategy is applied to optimise another PhD's work on powertrain components selection. All simulations are based on McLaren's powertrain model. Furthermore, the limitations of the McLaren powertrain model are discussed, which leads to the development of a new powertrain model. The research on powertrain modelling is explored, and the process of generating the new powertrain model is presented in this project. Calibration and validation of the model, along with the implementation of the control strategy, are also researched. The new powertrain model is named after me, YR-Sim. For the application of the Dynamic Programming (DP) algorithm, the new powertrain model has been modified and upgraded with more subsystems and features. All the inputs and outputs of these subsystems have been standardised. The calibration and validation of the model with different algorithms guarantees that the simulation results are comparable to McLaren's model with different algorithms and optimisation cases. Besides optimising CO2 emissions and SOC conditions, driveability has been introduced to research deeper into different algorithms. Driveability is still an open question, but in this project, some novel concepts have been introduced. CO2 emissions, SOC conditions, and driveability have been optimised and researched in different drive cycles, including NEDC, WLTC, FTP75, and even the Nürburgring race track. For different views of driveability, the benefits of the application of ECMS and DP have been deeply researched. Because this is a project that focuses on real industry applications, the research on the algorithms shows the pros and cons of these two algorithms in different dimensions. From the view of the industry, the research results bridge the gap between mathematical algorithm research and strategies application. A series of functions for automatic generation, calibration, and optimisation of control strategy, as well as the experiences in complex powertrain modelling, control, and validation, can also be used for future hybrid/electric vehicle platform development. The academic goals for algorithm research include the performance comparison between different algorithms, as well as the calibration and validation of the powertrain model. The industry goals include algorithm application, robustness testing, and multicomponent selection in different drive cycles. Both of these two series of requirements are satisfied by the end of this project. For the novelty part of this project, a new quantitative definition of driveability is introduced and discussed. Based on this new definition and research on the torque margin, the relationship between throttle pedal position and torque margin is studied. Driveability is numerically divided into prediction and linearity, and both dimensions are quantitatively researched and compared between different algorithms. Another novel research is the parameterisation method of the powertrain model, which significantly accelerates the simulation speed of the model and transfers the model to a steady-state model to ensure it generates reliable results on different control calibrations. Regarding the application and contribution of this project, it has helped the industry sponsor, McLaren, to solve the problem of high-performance hybrid powertrain system driveability. During the engagement of the high-torque electric motor, the torque margin can be controlled, and the relationship between driveability and drive modes is discovered. This will be applied as an important reference for future high-performance powertrain system development.

Emissions from the transportation sector due to the consumption of fossil fuels by conventional vehicles have been a major cause of climate change. Hybrid electric vehicles (HEVs) are a cleaner solution to reduce the emissions caused by transportation, and well-designed HEVs can also outperform conventional vehicles. This study examines various powertrain configurations and components to design a hybrid powertrain that can satisfy the performance criteria given by the EcoCAR Mobility Challenge competition. These criteria include acceleration, braking, driving range, fuel economy, and emissions. A total of five different designs were investigated using MATLAB/Simulink simulations to obtain the necessary performance metrics. Only one powertrain design was found to satisfy all the performance targets. This design is a P4 hybrid powertrain consisting of a 2.5 L engine from General Motors, a 150 kW electric motor with an electronic drive unit (EDU) from American Axle Manufacturing, and a 133 kW battery pack from Hybrid Design Services.

Amongst the myriad of potential hybrid powertrain architectures, selecting the right one for a given application is a daunting task. Whenever available, computer models greatly assist in the task. However, some elements, such as pollutant emissions, are difficult to model, leaving no other option than to test, for which at some point a real powertrain will be needed. Validating plausible options before assembling the entire powertrain has the potential of speeding up the development of vehicles. Doing so without having to ship the components around the world, even more. This work undertakes the task of designing a system to link test rigs over long distances in order to virtually couple vehicle components whilst avoiding physical contact. In the past, methods have been attempted with and without using mathematical models of the components to couple. In both cases the methods show reasonable accuracy only when the systems to couple present slow dynamics in relation to the communications delay. In addition, these methods seem to overlook the implications of operating a distributed system without a common time frame with synchronized clocks, as no method explicitly accounting for setpoint synchronisation has been found. Therefore, the problem of remotely coupling highly dynamic components remains still unsolved. In order to overcome the inherent latency arising from long-range communication, the proposed design combines the two following features in a novel arrangement: The exploitation of synchronised clocks to introduce setpoint commands simultaneously, and the use of models (observers) of the components being coupled, generated through their own operational data. Despite the appeal of observer-free coupling techniques, these are deemed limited in their ability to predict future behaviour under all circumstances, since these are generally based on some sort of static predicting rule/filter based on immediate past behaviour. The situation is analogous to that of driving a car while watching the rear-view mirror. It works well when the road ahead is straight, but not so well when curves lie in front. Hence, the observer method route is preferred. Nevertheless, the use of models clashes against the essence of the application - if good models were available, why not just simulate the coupling on a computer? This dilemma is sought to be minimised by using data-driven models requiring no prior plant knowledge. These models are created using the LOLIMOT algorithm. The designed coupling architecture is tested against two simulated physical systems. The first one, a simple deterministic system consisting of three rotating inertias coupled by means of spring-dampers, in which over a 70% error reduction is obtained when compared to direct transmission of the signals. The second one, consists of an internal combustion engine coupled to an electric motor/generator, typical of a hybrid vehicle powertrain configuration. Despite improvement over the duration the coupling can be kept working compared to direct transmission of the signals - 60 seconds against 20 seconds -, the fidelity of the virtual coupling remains far from faithful to the physical behaviour. The reason lies in the quality of the observers obtained through the LOLIMOT algorithm, especially that for the engine. Different methods of data collection are devised to improve these models, finding that data stemming from the original physical coupling results in better models. However, having to do so goes against the nature of the application. As a result, it is concluded that the LOLIMOT algorithm is inadequate to model an engine as a single unit for the objective of the application. Nevertheless, the devised coupling architecture may still prove useful in the event of obtaining more accurate models. Although from the point of view of the application, having to employ physics-based models is not as desirable as pure data-driven models, the blending of the two, in the so-called hybrid models, may be the most promising route to success.

Bicycles with electric pedal assist systems are becoming increasingly popular, with increasing versatility, a range of intended areas of use, and different design approaches for system realization. This paper deals with the proposal for a novel modular drivetrain concept based on a friction driving wheel, for which a brief design outline is given. Desired performances based on law constraints and average bicycle driving experience analysed, and basic powertrain properties required for their realization assessed.

The height-adjustable seat would be a boon for shorter drivers too. The overall material quality could be a lot better, though, considering the price of the higher trim levels. [...]I feel that the Smart Plus offers almost everything one may need or want from a modern family car except a reversing camera, so a DCA-equipped version on a lower trim level might have given the customers some actual choice.

The height-adjustable seat would be a boon for shorter drivers too. The overall material quality could be a lot better, though, considering the price of the higher trim levels. [...]I feel that the Smart Plus offers almost everything one may need or want from a modern family car except a reversing camera, so a DCA-equipped version on a lower trim level might have given the customers some actual choice.

An automotive battery pack for use in electric vehicles consists of a large number of individual battery cells that are structurally held and electrically connected. Making the required electrical and structural joints represents several challenges, including, joining of multiple and thin highly conductive/reflective materials of varying thicknesses, potential damage (thermal, mechanical, or vibrational) during joining, a high joint durability requirement, and so on. This paper reviews the applicability of major and emerging joining techniques to support the wide range of joining requirements that exist during battery pack manufacturing. It identifies the advantages, disadvantages, limitations, and concerns of the joining technologies. The maturity and application potential of current joining technologies are mapped with respect to manufacturing readiness levels (MRLs). Further, a Pugh matrix is used to evaluate suitable joining candidates for cylindrical, pouch, and prismatic cells by addressing the aforementioned challenges. Combining Pugh matrix scores, MRLs, and application domains, this paper identifies the potential direction of automotive battery pack joining.