The Research questions can be like-
RQ1. The prediction of how the number of Electric vehicles grows (eg. percentage-wise in 2025 or 2030)
RQ2. What kind of changes do Electric Cars bring to the vehicle industry?
RQ3. What value Electric Cars will create in terms of sustainability?
Feel free to modify the topic and the RQs, but the idea should be the same.
However, the main part of the thesis will be according to RQ1 like building some prediction /Machine Learning models of how the number of electric vehicles grows. We can predict, for example, 7-8 countries including Sweden, Finland, Norway, Germany, UK, USA, China, Japan, India, etc. (may be some other EU countries or you decide). We have to check information from different countries separately.
We should find monthly data for the mentioned countries. When we look for the data country-wise, there should be some announcement at least regarding monthly registered vehicles. Thus we will retrieve data for several years. For example, for Finland you can probably find by the following-
https://stat.fi/til/merek/tie_en.html
Or you may go with some other authentic sources depending on your reach
We primarily can consider forecasting with Smoothing Methods (Such as- Moving Averages, Weighted Moving Averages and Exponential Smoothing) and then we can do some machine learning models (several ways depending on the problems we want to solve based on theory part. For this, we will use Python programming in jupyter notebook and libraries will be used such as- numpy, pandas, matpolotlib, seaborn, regression methods, Naïve Bayes (NB) Classifier, Neural Networks, Support Vector, Regression trees, Random Forest, K-fold cross validation, Scikit-learn, Keras, as I have knowledge about these. Basically, this will depend what kind of problems we encounter to solve in the literature review part.
The deliverables will be the thesis, all the raw data and the jupyter notebook file. Every step should be well-discussed with me so that I can understand well and describe them to my supervisors, opponents, and fellow students.