This thesis outlines the development of a control system for a series-parallel plugin hybrid electric vehicle. The vehicle, developed at McMaster University for the EcoCAR 3 Advanced Vehicle Technology Competition, was produced in an effort to provide a Chevrolet Camaro with a high-performance, fuel efficient, hybrid powertrain.
A rational design methodology was adopted and guided the development of the control system and the implementation of its respective algorithms. A simulation tool was created using MATLAB and Simulink which, in turn, allowed for the effectiveness of the supervisory control logic to be evaluated by approximating the vehicle’s energy consumption, fuel consumption, and emissions. The impact of hybridizing the vehicle’s powertrain was similarly assessed by comparing it against its unelectrified counterpart, the 2016 Chevrolet Camaro LT.
A solution to the vehicle’s energy management problem was proposed in the form of an Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) which was then evaluated against more common heuristic approaches as well as non-adaptive instantaneous minimization methods. An artificial neural network was selected as the strategy’s adaptation mechanism and it was used to identify specific vehicular driving patterns in real-time. The neural network addresses many issues that arise due to the sensitivity of algorithms that attempt to solve the energy management problem without prior knowledge of the driving cycle.
The methods used during the process of the control system’s verification and calibration are also discussed in this thesis and, in addition, encompass the use of software representations of the vehicle’s Electronic Control Units (ECUs), the development of test cases, and the supervisory control software’s evaluation in the Model-in-the-Loop (MIL), Software-in-the-Loop (SIL), and Hardware-in-the-Loop (HIL) environments. / Thesis / Master of Applied Science (MASc) / Compared to conventional combustion vehicles, an automobile with an electrified propulsion system has the potential to reduce fuel consumption and emissions due to the presence of an energy storage system and one or more electric machines. These benefits, however, come at the cost of increased control system complexity.
The question of how and when to use alternative energy sources – whether it be electrical or fuel energy – in a hybrid vehicle is at the epicenter of research and development initiatives in the automotive industry. Traditional heuristic methods have proven to be unstable due to their sensitivity to driving conditions and that optimal control policies require prior knowledge of the vehicle’s route and destination, and therefore, are not suitable in most applications. Strategies which attempt to instantaneously minimize a vehicle’s fuel or energy consumption, however, can overcome these aforementioned obstacles. As such, this area of research and development has received much interest.
The objective of this research was twofold: the first being to develop a control system for a series-parallel plug-in hybrid electric vehicle in a rational and systematic manner, and, secondarily, to evaluate the benefits of instantaneous minimization methods for energy management.
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/21240 |
| Date | January 2017 |
| Creators | Lebel, Alexander |
| Contributors | Lawford, Mark, Wassyng, Alan, Software Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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