Assessment of real-world powertrain optimality in parallel hybrid electric vehicles
File(s)
Author(s)
Law, Andrew
Type
Thesis
Abstract
This thesis aims to discover powertrain optimality in parallel hybrid electric vehicles (HEVs). Achieving optimal HEV operation is dependent on minimizing the equivalent fuel consumption (EFC) which correlates fuel and electrical consumption into a singular metric, and reducing deviations in battery state of charge (SOC) to preserve long-term battery health. The control strategies were tested on a high-fidelity parallel through-the-road (TTR) HEV model that was developed to capture key transient dynamics of the engine and battery pathways operating in commercial HEVs.
Conventional heuristic and global optimized strategies were first employed to represent the baseline performances of parallel HEVs. Moreover, emulating characteristics of optimal HEV operation from global optimized methods led to the creation of the Torque-Levelling Threshold Changing Strategy (TTS) and its simplified version (STTS). This new class of modern heuristic strategies generated EFC improvements and introduced SOC charge-sustaining operation. Furthermore, both methods were developed into global heuristic strategies (GHS), allowing them to perform at their most optimal within each drive cycle. The Global STTS and Global TTS are able to perform similar to global optimized methods at up to less than 1\% EFC difference, but still retains the ease of heuristic implementation. Application of the GHS in the Real Driving Emissions (RDE) test also demonstrated similar performances, hence displaying feasibility of achieving optimal operation in real-world HEVs.
Lastly, integration of an electric booster coupled with a new TTS with current protection (iTTS) is capable of reducing battery current draw. This addresses the future role of boosting systems in HEVs, where fuel expenditure is intentionally increased to further reduce battery degradation. The techno-economic analysis conducted on the proposed optimality trade-off showcases that battery lifetimes are extendable up to 4 times without breaching vehicle emissions limits, and prolonging battery lifetimes translates towards substantial benefits for both manufacturers and consumers.
Conventional heuristic and global optimized strategies were first employed to represent the baseline performances of parallel HEVs. Moreover, emulating characteristics of optimal HEV operation from global optimized methods led to the creation of the Torque-Levelling Threshold Changing Strategy (TTS) and its simplified version (STTS). This new class of modern heuristic strategies generated EFC improvements and introduced SOC charge-sustaining operation. Furthermore, both methods were developed into global heuristic strategies (GHS), allowing them to perform at their most optimal within each drive cycle. The Global STTS and Global TTS are able to perform similar to global optimized methods at up to less than 1\% EFC difference, but still retains the ease of heuristic implementation. Application of the GHS in the Real Driving Emissions (RDE) test also demonstrated similar performances, hence displaying feasibility of achieving optimal operation in real-world HEVs.
Lastly, integration of an electric booster coupled with a new TTS with current protection (iTTS) is capable of reducing battery current draw. This addresses the future role of boosting systems in HEVs, where fuel expenditure is intentionally increased to further reduce battery degradation. The techno-economic analysis conducted on the proposed optimality trade-off showcases that battery lifetimes are extendable up to 4 times without breaching vehicle emissions limits, and prolonging battery lifetimes translates towards substantial benefits for both manufacturers and consumers.
Version
Open Access
Date Issued
2023-11
Date Awarded
2024-03
Copyright Statement
Creative Commons Attribution NonCommercial Licence
License URL
Advisor
Martinez-Botas, Ricardo
Rajoo, Srithar
Sponsor
Mitsubishi Heavy Industries (Firm)
Mitsubishi Turbocharger and Engine Europe B.V.
Publisher Department
Mechanical Engineering
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)