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Hierarchical Optimal Energy Management of Electric Vehicles

Periodic Reporting for period 1 - HOEMEV (Hierarchical Optimal Energy Management of Electric Vehicles)

Reporting period: 2019-08-01 to 2021-07-31

The transportation sector is a key contributor to greenhouse gas (GHG), air pollution and noise. Road transport accounts for around one fifth of the GHG emissions of the European Union (EU). It has been widely recognized that vehicle electrification provides a potential way for the EU to move towards a more decarbonized transport system and sustainable circular economy. Although sales of electric vehicles have been growing steadily in recent years, they only represent 1.4% of all new cars sold in the EU in 2017. Developing electric vehicle technologies is essentially important to increase the market share, and control technology plays an indispensable role in improving the overall efficiency of electric vehicles; however, their control problem is very challenging because electric vehicles exhibit complex dynamics with uncertainties and nonlinearities, and strong physical couplings among different subsystems. Moreover, considering the significant advancement of other technologies in contributing to the development of smart transportation systems, it is highly promising to develop advanced control strategies for electric vehicles which can be combined with these latest enabling technologies to dramatically improve the overall efficiency of the energy management of electric vehicles. This was the main motivation for the fellow to pursue this project on electric vehicle control by developing an advanced electric vehicle control framework enhanced by the advantages of the latest enabling technologies in other disciplines.
The overarching objective of this project was to develop a novel computationally efficient hierarchical adaptive optimal control framework incorporating transportation information and drivers’ habits suitable for energy management of multi-source electric vehicles. This project aimed to sufficiently merge the critical information of human, road and vehicle with a hierarchical control framework to facilitate the energy management of electric vehicles. The proposed cutting-edge research will contribute to the fundamental research of electric vehicle energy management by developing the next generation electric vehicle control strategy. The proposed research not only benefits EU vehicle and battery industries, but also contributes to the research excellence of the EU in wider disciplines.
Upon finishing the project, the fellow has successfully advanced a hierarchical and predictive control theory for energy management of electric vehicles and applied them to design a distribution strategy adaptively according to driving range, transportation information and battery performance variation. The fellow conducted a variety of research on hierarchical optimal energy management of electric vehicles. The project has fully achieved its objectives and milestones according to the well-designed research plan.
The project output filled in the gap in the theoretical study of the hierarchical optimal energy management strategy to promote its application for energy management of electric vehicles. The project was multidisciplinary and at the forefront of electrics, control science, information, and communication technologies, and was also important from the aspects of both fundamental theories and applications. The project has combined the state-of-the-art control, optimization, numerical simulation, and real-time hardware experimentation. In addition, a mass of battery control techniques has emerged as an extension of the study. The project dramatically improves the role of controlling techniques in performance promotion of electric vehicles. The main innovation outputs lie in the establishment of an integrated hierarchical optimal energy management of electric vehicles. The fellow fully incorporated the state-of-the-art control algorithms and the high nonlinear time-varying characteristics of vehicle powertrain to supply high-quality energy control of electric vehicles, thereby improving their operation economy and extending the battery lifespan. Also, the outstanding outputs in battery control enables the controlling performance promotion of lithium-ion batteries equipped in electric vehicles. The research outputs will contribute to both control theory and applications in electric vehicles with extensions to other engineering problems going forward. The developed control framework is generic and can be extended for a broad class of engineering problems, which need to be optimally controlled with multiple objectives and subject to complicated nonlinearities and constraints.
After the high-quality research at QMUL, the fellow published 44 papers in top journals. These papers are open access. Based on the project support, the fellow not only finished all the designated research tasks, but also extended the research to intelligent connected vehicles and advanced lithium-ion battery control. In addition, the fellow attended a few international conferences (even with the huge challenges as a result of COVID-19 pandemic), including the 34th World Electric Vehicle Symposium & Exhibition, the 4th CAA International Conference on Vehicular Control and Intelligence, and MSCA European Green Deal cluster, to disseminate research output. The fellow won the Excellent Paper Award in EVS34. The fellow was elected to the Fellow of Institution of Engineering and Technology, due to his outstanding contributions to optimal control of electric vehicles. The fellow was also elected to an editor of two journals: IEEE Access and Vehicles.
The project has achieved enabling control breakthroughs in energy management of electric vehicles. In addition, a variety of research has been extended to optimal control of lithium-ion batteries. A mass of research output has been published in top journals in this field after rigid peer review, and some research outputs have gained wide attention and interest from industry partners, such as Foresight Williams Technology Funds, Changan UK and B&R Automation.
The project’s research output can dramatically improve the controlling performance of widely-accepted lithium-ion batteries. As the key energy storage components of electric vehicles, lithium-ion batteries need to be properly managed to ensure safe, efficient, and reliable operation. To promote on-board management for batteries, the fellow has provided a game-changing software based on the developed hierarchical high-performance management algorithms using the advantage of cloud computation and wireless communication. The project can reduce the occurrence probability of hazard in batteries by 90%, and has improved the operation performance of batteries dramatically. The fellow’s research output has shown potential market opportunities as well as having wider impact in terms of aligning with the green deal and a decarbonized transport system and sustainable circular economy through improving electric vehicle and battery technology. The fellow’s research output could be a valuable reference that would be of interest to the government and could be shared with instructive suggestions to policymakers to potentially influence the vehicular technology development and electric vehicle future trends. Some general perspectives were summarized by the fellow and shared through the MSCA Green Deal Cluster Event.
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