Energy management systems (EMSs), implemented in the electronic control unit (ECU) of an actual vehicle with electri ed powertrain, is a much simpler version of the theoretically developed EMS. Such simpli cation is done to accommodate the EMS within the given memory constraint and computational capacity of the ECU. The simpli cation should ensure reasonable performance compared to theoretical EMS under real-life driving scenarios. The process of simpli cation must be effective to create a versatile and utilitarian EMS. The reinforcement learning-based controllers feature pro table characteristics in optimizing the performance of controllable physical systems as they do not mandatorily require a mathematical model of system dynamics (i.e. they are model-free). Quite naturally, it can aspired to testify such prowess of reinforcement learning-based controllers in achieving near-global optimal performance for energy management system (supervisory) of electri ed powertrains. Before deployment of any supervisory controller as a mainstream controller, they should be essentially scrutinized through various levels of virtual simulation platforms with an ascending order of physical system emulating-capability. The controller evolves from a mathematical concept to an utilitarian embedded system through a series of these levels where it undergoes gradual transformation to finally become apposite for a real physical system. Implementation of the control strategy in a Simulink-based forward simulation model could be the first stage of the aforementioned evolution process. This brief will delineate all the steps required for implementing an reinforcement learning-based supervisory controller in a forward simulation model of a hybrid electric vehicle. A novel framework of loss-minimization based instantaneous optimal strategy is introduced for the energy management system of a multi-mode hybrid electric powertrain in this brief. The loss-minimization strategy is flexible enough to be implemented in any architecture of electrified powertrains. It is mathematically proven that the overall system loss minimization is equivalent to the minimization of fuel consumption. An online simulation framework is developed in this article to evaluate the performance of a multi-mode electrified powertrain equipped with more than one power source. An electrically variable transmission with two planetary gear-set has been chosen as the centerpiece of the powertrain considering the versatility and future prospects of such transmissions. It is noteworthy to mention that a novel architecture topology selected for this dissertation is engendered through a series of rigorous screening process whose workflow is presented here with brevity.
One of the legitimate concern of multi-mode transmission is it's proclivity to contribute discontinuity of power-flow in the downstream of the powertrain. Mode-shift events can be predominantly held responsible for engendering such discontinuity. Advent of dynamic coordinated control as a technique for ameliorating such discontinuity has been substantiated by many scholars in literature. Hence, a system-level coordinated control is employed within the energy management system which governs the mode schedule of the multi-mode powertrain in real-time simulation. / Thesis / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/26446 |
| Date | January 2021 |
| Creators | Biswas, Atriya |
| Contributors | Emadi, Ali, Mechanical Engineering |
| Source Sets | McMaster University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
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