Data analysis methods are important to analyze the ever-growing enormous quantity of the high dimensional data. Cluster analysis separates or partitions the data into disjoint groups such that data in the same group are similar while data between groups are dissimilar. The focus of this thesis study is to identify natural groups or clusters of drivers using the data which is based on driving style. In finding such a group of drivers, evaluation of the combinations of dimensionality reduction and clustering algorithms is done. The dimensionality reduction algorithms used in this thesis are Principal Component Analysis (PCA) and t-distributed stochastic neighbour embedding (t-SNE). The clustering algorithms such as K-means Clustering and Hierarchical Clustering are selected after performing Literature Review. In this thesis, the evaluation of PCA with K-means, PCA with Hierarchical Clustering, t-SNE with K-means and t-SNE with Hierarchical Clustering is done. The evaluation was done on the Volvo Cars’ drivers dataset based on their driving styles. The dataset is normalized first and Markov Chain of driving styles is calculated. This Markov Chain dataset is of very high dimensions and hence dimensionality reduction algorithms are applied to reduce the dimensions. The reduced dimensions dataset is used as an input to selected clustering algorithms. The combinations of algorithms are evaluated using performance metrics like Silhouette Coefficient, Calinski-Harabasz Index and DaviesBouldin Index. Based on experiment and analysis, the combination of t-SNE and K-means algorithms is found to be the best in comparison to other combinations of algorithms in terms of all performance metrics and is chosen to cluster the drivers based on their driving styles.

Background: As the requirements for the modern-day comforts are raising from day to day, the great evolution in the field of lawn-mowers is recorded. This evolution made companies produce a fleet of lawn-mowers(commercial, house-hold) for different kinds of usages. Despite the great evolution and market in this field, to the best of our knowledge, no effort was made to understand customer usage by analysis of real-time usage of lawn-mowers. This research made an attempt to analyse the real-time usage of lawn-mowers using techniques like machine learning. Objectives: The main objective of the thesis work is to understand customer usage of lawn-mowers by analysing the real-time usage data using machine learning algorithms. To achieve this, we first review several studies to identify what are the different ways(scenarios) and how to understand customer usage from those scenarios. After discussing these scenarios with the stakeholders at the company, we evaluated a suitable scenario in the case of lawn-mowers. Finally, we achieved the primary objective by clustering the usage of lawn-mowers by analysing the real-world time-series data from the Controller Area Network(CAN) bus based on the driving patterns. Methods: A Systematic literature review(SLR) is performed to identify the different ways to understand customer usage by analysing the usage data using machine learning algorithms and SLR is also performed to gain detailed knowledge about different machine learning algorithms to apply to the real-world data. Finally, an experiment is performed to apply the machine learning algorithms on the CAN bus time-series data to evaluate the usage of lawn-mowers into various clusters and the experiment also involves the comparison and selection of different machine learning algorithms applied to the data. Results: As a result of SLR, we achieved different scenarios to understand customer behaviours by analysing the usage data. After formulating the best suitable scenario for lawn-mowers, SLR also suggested the best suitable machine learning algorithms to be applied to the data for the scenario. Upon applying the machine learning algorithms after making necessary pre-processing steps, we achieved the clusters of usage of lawn-mowers for every driving pattern selected. We also achieved the clusters for different features of driving patterns that indicate the various characteristics like a change of intensity in the usage, rate of change in the usage, etc. Conclusions: This study identified customer behaviours based on their usage data by clustering the usage data. Moreover, clustering the CAN bus time-series data from lawn-mowers gave fresh insights to study human behaviours and interaction with the lawn-mowers. The formulated clusters have a great scope to classify and develop the individual strategy for each cluster formulated. Further, clusters can also be useful for identifying the outlying behaviour of users and/or individual components.

This article explores the potential of using real-world driving patterns to derive PHEV and BEV utility factors and evaluates how different travel and recharging behaviours affect the calculation of the standard SAE J2841 utility factor. The study relies on six datasets of driving data collected monitoring 508,607 conventional fuel vehicles in six European areas and a dataset of synthetic data from 700,000 vehicles in a seventh European area. Sources representing the actual driving behaviour of PHEV together with the WLTP European utility factor are adopted as term of comparison. The results show that different datasets of driving data can yield to different estimates of the utility factor. The SAE J2841 standard method results to be representative of a large variety of behaviours of PHEVs and BEVs' drivers, characterised by a fully-charged battery at the beginning of the trip sequence, thus being representative for fuel economy and emission estimates in the early phase deployment of EVs, charged at home and overnight. However the results show that the SAE J2841 utility factor might need to be revised to account for more complex future scenarios, such as necessity-driven recharge behaviour with less than one recharge per day or a fully deployed recharge infrastructure with more than one recharge per day.

The well-to-wheel (WTW) environmental performance of plug-in hybrid electric vehicles (PHEVs) is sensitive to driving patterns, which vary within and across regions. This thesis develops and applies a novel approach for estimating specific regional driving patterns. The approach employs a macroscopic traffic assignment model linked with a vehicle motion model to construct driving cycles, which is done for a wide range of driving patterns. For each driving cycle, the tank-to-wheel energy use of two PHEVs and comparable non-plug-in alternatives is estimated. These estimates are then employed within a WTW analysis to investigate implications of driving patterns on the energy use and greenhouse gas emission of PHEVs, and the WTW performance of PHEVs relative to non-plug-in alternatives for various electricity generation scenarios. The results of the WTW analysis demonstrate that driving patterns and the electricity generation supply interact to substantially impact the WTW performance of PHEVs.

The well-to-wheel (WTW) environmental performance of plug-in hybrid electric vehicles (PHEVs) is sensitive to driving patterns, which vary within and across regions. This thesis develops and applies a novel approach for estimating specific regional driving patterns. The approach employs a macroscopic traffic assignment model linked with a vehicle motion model to construct driving cycles, which is done for a wide range of driving patterns. For each driving cycle, the tank-to-wheel energy use of two PHEVs and comparable non-plug-in alternatives is estimated. These estimates are then employed within a WTW analysis to investigate implications of driving patterns on the energy use and greenhouse gas emission of PHEVs, and the WTW performance of PHEVs relative to non-plug-in alternatives for various electricity generation scenarios. The results of the WTW analysis demonstrate that driving patterns and the electricity generation supply interact to substantially impact the WTW performance of PHEVs.