Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.694002
Title: Control strategies for series hybrid electric vehicles
Author: Shabbir, Wassif
ISNI:       0000 0004 5989 6374
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2015
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Abstract:
This thesis deals with the energy management problem of series hybrid electric vehicles (HEVs), where the objective is to maximize fuel economy for general driving. The work employs a high-fidelity model that has been refined to deliver appropriate level of dynamics (for the purposes of this research) at an acceptable computational burden. The model is then used to design, test and study established conventional control strategies, which then act as benchmarks and inspiration for proposed novel control strategies. A family of efficiency maximizing map strategies (EMMS) are developed based on a thorough and holistic analysis of the powertrain efficiencies. The real-time variants are found to deliver impressive fuel economy, and the global variant is found to outperform the conventional global benchmark. Two heuristic strategies are developed (exclusive operation strategy (XOS) and optimal primary source strategy (OPSS)) that are found to deliver significantly better fuel economy results, compared to conventional alternatives, and further desirable traits. This is found to be particularly related to the better use of modern start stop systems (SSSs) that has not been considered sufficiently in the past. A global heuristic strategy (GHS) is presented that successfully outperforms the conventional global benchmark without any particularly complex analysis. This exposes some of the limitations of optimization-based techniques that have been developed for simple vehicle models. Lastly, the sensitivity of the performance of the control strategies has been studied for variations in tuning accuracy, SSS efficiency, vehicle initial conditions, and general driving conditions. This allows a deeper insight into each control strategy, exposing strengths and limitations that have not been apparent from past work.
Supervisor: Evangelou, Simos Sponsor: Engineering and Physical Sciences Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.694002  DOI: Not available
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