This thesis outlines the development of a control system for a P4-P0 Parallel-Through-The-Road Hybrid Electric Vehicle. This project was part of the EcoCAR Mobility Challenge, an Advanced Vehicle Technology Competition, sponsored by the U.S. Department of Energy, MathWorks and General Motors. The McMaster Engineering EcoCAR team is participating in its second iteration, re-engineering a 2019 Chevrolet Blazer to suit a car-sharing service located within the Greater Toronto Hamilton Area. The proposed architecture uses a 1.5L Engine together with a Belted Alternator Starter motor connected to the traditional low voltage system. The rear axle is electrified containing an Electric Machine, a power oriented Battery Pack and team-designed gear reduction as well as a clutch. The whole rear powertrain is operating at high voltage and has no connection to the traditional low voltage system. Fuel economy improvements up to 12% can be expected while maintaining stock performance targets.
A vehicle simulation model was built to accompany the vehicle design process. This includes a mathematical representation of all powertrain components, the development of energy management algorithms, the design of the Hybrid Supervisory Controller structure, and validating and discussing gathered results. Furthermore, all necessary controllers were chosen and communication within them was established by designing the serial data architecture.
The developed energy management algorithm is customized to utilize the strengths of all components and this specific architecture. A simple rule-based algorithm is used to operate the engine as close as possible to its most fuel efficient operation point at any time. The P4 and P0 motor are used to apply supportive torque to the engine or load the engine with a negative torque. In that way the energy can be regenerated inside the powertrain and charge sustaining operation
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can be achieved. Fuel economy and performance targets are used to discuss the assumed performance of the vehicle once re-engineered. The set targets range from city and highway fuel economy to IVM – 60 mph acceleration time.
Overall the developed control system suits a car-sharing service with its ability to adapt to the occurring driving situations ensuring a close to optimal operation for any known or unknown driving situation. It focuses on modularity, simplicity and functionality to allow a working implementation in future years of the EcoCAR Mobility Challenge. / Thesis / Master of Applied Science (MASc) / During the re-engineering of a Hybrid Electric Vehicle different expectations must be considered, for example set government fuel economy regulations, defined performance targets, novelty in innovation, stakeholder expectations as well as the used vehicle platform and the available components. The re-engineering process will be done according to the vehicle development process of the EcoCAR Mobility Challenge. Summarized expectations are the use of this vehicle inside a car-sharing service for the Greater Toronto Hamilton Area targeting “Millennials” while focusing on fuel economy improvements and a low cost of ownership.
The research shown in this thesis is set by the requirements derived from the expectations mentioned above. One point of interest is achieving a working control system able to operate close to an optimal state to maximize fuel efficiency and ensuring stock vehicle performance targets. Therefore, the control system has to use the electrification components in an intelligent way. Defining what intelligent control of the engine and the electrification components was one of the main challenges.
This thesis outlines how developing a control system for a Hybrid Electric Vehicle can be realized while ensuring that all included interests are met. The object of this research contains choosing the necessary controllers, building a sufficient vehicle simulation model, developing the energy management algorithm, validating the model performance and evaluating the gathered results.
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/24786 |
| Date | January 2019 |
| Creators | Haußmann, Mike |
| Contributors | Emadi, Ali, Mechanical Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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