Electronic and Electrical Engineering
Title of Scholarship Project:
EV Chargepoint Infrastructure Planning
My name is Angelos Koutsoukos, and I am about to embark on my second year of studying Electrical and Electronic Engineering. The focus of my research project, which I worked on this summer, was Electric Vehicle (EV) charging point infrastructure planning. This critical aspect of the transition to sustainable transportation has profound implications for our environment and our future.
My project focused on optimizing the design of EV charging infrastructure in public spaces, a task that required careful evaluation of many charging configurations and diligent application of local and regional datasets. This analytical work allowed me to generate optimal location guidelines. The climax of my project was handing over these valuable findings to local government authorities, specifically the West Yorkshire Combined Authority. They can now use this data to establish efficient and equitable public EV charging infrastructure, supporting not only local drivers but also broader environmental goals.
When choosing my Laidlaw project, I felt a mix of anticipation and uncertainty, eager to face new challenges and opportunities. This project immediately stood out from the vast list of Laidlaw pre-defined projects. It essentially married the two things I am most passionate about: cars and electrical engineering. From the moment I saw it, I knew this project was the perfect match for my interests and skills, and I was eager to dive into this unique blend of my passions.
The foundation of my research was anchored on a rich tapestry of datasets pertaining to Lower Layer Super Output Areas (LSOAs) in West Yorkshire. These datasets included various metrics, such as population density, vehicle ownership, housing type, and commuting methods. Such granular data allowed me to understand not only the spatial distribution of potential Electric Vehicle (EV) users but also provided insights into their behaviour and potential needs.
A significant portion of my task revolved around data preprocessing and analysis, which I primarily executed through Python using Google Colaboratory. The coding process involved merging different datasets, creating a comprehensive and integrated dataset for analysis. To standardize the metrics, I normalized these datasets using the MinMaxScaler. Such normalization ensured that all the parameters, irrespective of their original scales, could contribute equally to the subsequent multi-criteria analysis (MCA).
Weighting played a crucial role in my research, determining the significance of each criterion. A sensitivity analysis was conducted by adjusting these weights, which allowed me to gauge how changes in weights influenced the prioritization of LSOAs for charging point installations. This approach was crucial in refining the MCA methodology to ensure that the results were not only optimal but also robust against changes in assumptions.
Beyond the pure data analysis, a considerable part of my research was dedicated to the translation of these data-driven insights into actionable guidelines. By utilizing a bespoke algorithm, I estimated the number of charging points required for each LSOA.
During the project, there were moments of adaptation and course correction. For instance, the initial datasets were distinguished based on urban and rural settings. This categorization required me to tailor the MCA differently for each setting, considering the unique challenges and opportunities they presented. Furthermore, my engagement with the West Yorkshire Combined Authority provided real-time feedback, leading to tweaks in the parameters used and the way the scoring system was structured.
In essence, the project was a dance between data, analysis, and real-world application. Every decision, whether it was in the weighting of criteria or the phases of charging point installations, was made to balance analytical rigor with the practical needs of West Yorkshire's EV transition.
The research I undertook has profound implications for not just West Yorkshire, but potentially for wider regions contemplating a paradigm shift towards sustainable transportation. Efficient allocation of charging points is pivotal to encouraging wider EV adoption. Without a readily available charging infrastructure, logistical issues and "range anxiety" might delay the adoption of electric vehicles.
The analysis techniques developed during this research can act as a roadmap for local authorities. It emphasizes data-driven decision-making, ensuring that the placement of charging points aligns with areas of highest potential usage. This not only maximizes the utility of each charging point but also ensures that public funds are used optimally, yielding the highest return on investment in terms of both infrastructure usage and environmental impact.
Speaking of environmental impact, the broader context of this research cannot be understated. As the world grapples with the pressing challenges of climate change, transitioning to EVs stands out as a practical action point to reduce greenhouse gas emissions. Vehicles are a significant contributor to global CO2 emissions, and a shift to electric can substantially reduce our carbon footprint. But for this shift to happen at scale, potential EV owners need confidence in the infrastructure supporting their vehicles. By optimizing charging point locations and ensuring their adequate distribution, my research directly feeds into this confidence-building exercise.
The dissemination phase proved as enlightening as the research itself, offering numerous invaluable insights that shaped my understanding. This stage was not merely about presenting the culmination of my efforts; it also became a powerful means to gather diverse feedback and refine my approach.
I laid out my findings systematically, beginning with the methodology and ending with the research's potential impact. Through the feedback from various stakeholders, I realized some nuances I hadn't initially considered. For instance, while my original weighting of certain criteria was based on specific assumptions, discussions during the presentations prompted me to consider how these weights might be adjusted across different scenarios or regions.
The continuous feedback loop was among the most enriching experiences of this phase. Feedback varied from suggestions to simplify the content for non-technical audiences to reevaluating certain foundational assumptions in my analysis. Such input was pivotal in making my research more comprehensive and robust.
My collaboration with my mentor, professor Kelsall, was an anchor throughout this process. Navigating the intricacies of such a multi-faceted research project required consistent guidance and feedback. Prof. Kelsall's expertise was invaluable in helping me stay on course, especially when I faced challenges or uncertainties. We had regular check-ins to discuss progress, share insights, and chart out the next steps. His feedback was always constructive, ensuring that I not only identified potential gaps in the research but also found ways to address them. It was this collaborative approach, coupled with the invaluable feedback during the dissemination phase, that truly honed my research into its final form.
In essence, the dissemination was not just about sharing findings but also a process of refining and enriching the research, ensuring it stood up to scrutiny and remained relevant to its intended audience.
Setting out on this research was like starting an unexpected adventure. It was a profound learning experience that fundamentally transformed my approach to data analysis, problem-solving, and the broader applications of engineering principles.
The sheer depth and plethora of the datasets I dealt with refined my data analysis skills. Faced with numerous metrics and diverse data types, I quickly realized the importance of diligent preprocessing and strict validation. Every assumption made and every criterion weighted was based on a blend of data-driven insights and engineering judgment. Iterative processes, such as sensitivity analyses, further solidified my understanding of the fluid nature of real-world data and its implications. The experience underscored the significance of adaptability in data analysis; it isn't just about crunching numbers, but also about being aware of and adjusting to the nuances they carry.
The most profound realization, however, was the bridge between pure data and its real-world implications. Data, in isolation, is merely a collection of facts and figures. But when analysed and applied correctly, it has the power to drive decisions that impact communities, economies, and the environment. This research project underscored the symbiotic relationship between data and decision-making. By analysing datasets, I wasn't just engaging in an academic exercise, but rather participating in a larger narrative – one that could potentially reshape the transportation landscape of West Yorkshire and set a precedent for other regions.
The very essence of research, especially one as intricate as optimizing EV infrastructure, demands leadership – both in thought and in action. Throughout the research process, I was consistently thrust into positions that required strategic foresight, meticulous planning, and assertive execution, elements that are cornerstone to effective leadership.
Planning the research trajectory was akin to charting out a strategic roadmap. Defining clear objectives, breaking them down into actionable tasks, and subsequently prioritizing these tasks required both vision and detail-orientation. This project required managing large datasets, interfacing with various stakeholders, and being responsive to feedback – all of which tested and honed my organizational abilities. The act of organizing not only meant the technicalities of data management but also meant effectively allocating my time, prioritizing tasks, and consistently staying ahead of potential challenges.
Furthermore, the collaborative dimension of my project was instrumental in developing my leadership skills. Engaging with my mentor, professor Kelsall, stakeholders from the West Yorkshire Combined Authority, and other experts was a continuous exercise in effective communication, negotiation, and consensus-building. The very nature of feedback, especially when it challenged my assumptions or methodologies, forced me to both defend and sometimes pivot my strategies, a delicate balance that every leader must strike. Professor Kelsall’s guidance was instrumental, not just for the technical aspects but also in understanding the nuances of collaborative work, absorbing constructive criticism, and continuously improving.
The project presented numerous challenges – from data inconsistencies to refining the weighting in the multi-criteria analysis. Each challenge was a decision-making crossroad. Should I recalibrate the weightings? Should I segregate urban and rural data even more? How do I ensure that the algorithm's output resonates with the real-world scenario? These moments not only tested my analytical skills but also my leadership spirit. Decisions had repercussions, and every choice I made was backed by a blend of data-driven rationale and instinct, a combination that I believe lies at the heart of leadership.
Adaptability was another key leadership attribute I developed during this journey. The iterative nature of the research, especially when interacting with stakeholders, meant that I had to be smooth in my approach, ready to make course corrections when required. This skill, the ability to adapt and evolve in the face of changing scenarios, is what I believe differentiates a leader from a manager.
In retrospect, the leadership skills I gained from this research are invaluable. Beyond the technical proficiencies and the findings, it's the evolution of my leadership persona that stands out. The project was not just an academic pursuit; it served as a testing ground for my leadership skills, where they were ultimately strengthened. It emphasized the interdependence of in-depth technical work and the larger leadership culture, reiterating my conviction that great leaders are frequently those who dig deeply, question norms, collaborate successfully, and always, always keep learning.
My understanding of the broad length and possible influence of electrical and electronic engineering, particularly in sustainable transportation, has unquestionably changed because of this study project. This experience, rich in learning and introspection, has been pivotal in illuminating the path I wish to tread in my future academic and professional endeavours.
Observing firsthand the pivotal role that precise planning plays in advancing the sustainable transport agenda, I am now keen on exploring intersections between electrical engineering, data science, and urban planning. This confluence, I believe, is where the future lies, especially if we are to make our urban centres more sustainable, efficient, and liveable.
Although I remain enamoured by the world of cars and their electrical intricacies, this project has widened my horizon. I see myself not just as an engineer focused on vehicles but as a problem solver keen on leveraging technology and data to address broader societal challenges. Continuing work in this domain is certainly on the cards, whether it's further research, internships, or collaborative projects. The interface with the West Yorkshire Combined Authority was particularly enlightening, introducing me to the world of public policy and infrastructure planning. This nexus between engineering, data, and policymaking is a domain I am keen to delve deeper into.
In terms of immediate next steps, I am contemplating internships or projects that sit at this tri-junction of engineering, data analytics, and urban planning. This will provide a more hands-on understanding and a clearer vision of how I can contribute and evolve in this space.
Embarking on the Laidlaw Programme was like diving into a world of deep exploration and self-discovery. Beyond just academic research, this journey widened my perspectives, sharpened my skills, and clarified my future vision. From the early stages of selecting my topic to the final presentation, every step taught me valuable lessons. The programme bridged my love for cars and electrical engineering, weaving a story that was both technical and impactful. Navigating through intricate data, understanding diverse feedback, and grasping the real-world effects of my work all came together to create a transformative experience.
On a personal front, the programme was a mirror, reflecting my strengths, areas of improvement, aspirations, and the profound impact I can wield as an engineer. The challenges faced, the milestones achieved, and the relationships forged have collectively crafted an array of growth, resilience, and vision.
I extend my heartfelt gratitude to my mentor, professor Kelsall, whose unwavering support, expertise, and encouragement have been the bedrock upon which my research journey was built. To the Laidlaw Programme, I owe a debt of gratitude for presenting such a transformative platform. My appreciation also goes out to everyone – peers and stakeholders – who provided insights, feedback, and support throughout my research period. This journey has been nothing short of monumental, an anchor in my academic and professional trajectory. It's not just about what I discovered during my research; it's about what I discovered about myself.
It was a joy to supervise this project! 6 weeks is a very short period of time to conduct meaningful research work, and so there was a lot for Angelos to fit in. Moreover, there were so many aspects of the work that were totally new to Angelos. Conducting a research study is nothing like studying the first year of a degree programme! I was very impressed at the speed at which Angelos got to grips with the software tools needed for this project. As the days progressed, it was great to see how his familiarity with, and understanding of, the project topic grew and deepened. As he mentions above, this was an interdisciplinary, multi-faceted project which depended on data processing for its outputs, but also required consideration of a range of interlinked social, environmental and economic factors. During the 6 weeks, I saw Angelos transform from someone who had no prior experience of this mode of study, to someone who was thoroughly engaged in the work and all its interdependent aspects. Angelos learned how to develop models, how to identify assumptions, how to hold an academic conversation about a research investigation, how to question assumptions and question data, and how to iterate and refine methodologies. He also developed information gathering and referencing skills, all, of course, in a self-study context. He demonstrated initiative in finding different software tools in order to carry out specific tasks.
The project provided a great opportunity to liaise regularly with local government leaders across West Yorkshire who were responsible for providing public charging infrastructure for electric vehicles. Angelos participated in this “Task Force”: he presented his work to the group on more than one occasion; his presentations were very clear and very-well received. He listened and responded to the group’s feedback. As the project progressed, it was great to see Angelos develop his own views on the work and make his own proposals for directions of study. I feel that, through this project, Angelos has made great strides in developing his leadership skills in terms of his ability to conduct independent research, to take initiative in sourcing information, data and research tools, to format research results and prepare and deliver presentations, to analyse and question assumptions, models and research outputs, and to listen to and act upon feedback. All these skills are highly transferable and will greatly strengthen his ability to conduct and lead investigative activity in future.
Signature of Scholar ______________________ Date: _14__/_09_/__23
Signature of Project Leader ____ Date: __14_/_09_/__23