Analysis of the Powertrain Component Size of Electrified Vehicles Commercially Available on the Market

The paper aims to present an analysis of the component sizes of commercially available vehicles with electrified powertrains. The paper provides insight into how the powertrain components (an internal combustion engine, an electric motor and a battery) of mass production electrified vehicles are sized. The data of wide range of mass production electrified vehicles are collected and analyzed. Firstly, the main requirements to performance of a vehicle are described. The power values to meet the main performance requirements are calculated and compared to the real vehicle data. Based on the calculated values of the power requirements the minimum sizes of the powertrain components are derived. The paper highlights how the sizing methodologies, described in the research literature, are implemented in sizing the powertrain of the commercially available electrified vehicles.

Traffic and Energy Consumption Modelling of Electric Vehicles: Parameter Updating from Floating and Probe Vehicle Data

This paper focuses on the estimation of energy consumption of Electric Vehicles (EVs) by means of models derived from traffic flow theory and vehicle locomotion laws. In particular, it proposes a bi-level procedure with the aim to calibrate (or update) the whole parameters of traffic flow models and energy consumption laws by means of Floating Car Data (FCD) and probe vehicle data. The reported models may be part of a procedure for designing and planning transport and energy systems. This aim is to verify if, and in what amount, the existing parameters of the resistances/energy consumptions model calibrated in the literature for Internal Combustion Engines Vehicles (ICEVs) change for EVs, considering the above circular dependency between supply, demand, and supply–demand interaction. The final results concern updated parameters to be used for eco-driving and eco-routing applications for design and a planning transport system adopting a multidisciplinary approach. The focus of this manuscript is on the transport area. Experimental data concern vehicular data extracted from traffic (floating car data and probe vehicle data) and energy consumption data measured for equipped EVs performing trips inside a sub-regional area, located in the Città Metropolitana of Reggio Calabria (Italy). The results of the calibration process are encouraging, as they allow for updating parameters related to energy consumption and energy recovered in terms of EVs obtained from data observed in real conditions. The latter term is relevant in EVs, particularly on urban routes where drivers experience unstable traffic conditions.

Towards Data-Driven Vehicle Estimation for Signalised Intersections in a Partially Connected Environment

Connected vehicles (CVs) have the potential to collect and share information that, if appropriately processed, can be employed for advanced traffic control strategies, rendering infrastructure-based sensing obsolete. However, before we reach a fully connected environment, where all vehicles are CVs, we have to deal with the challenge of incomplete data. In this paper, we develop data-driven methods for the estimation of vehicles approaching a signalised intersection, based on the availability of partial information stemming from an unknown penetration rate of CVs. In particular, we build machine learning models with the aim of capturing the nonlinear relations between the inputs (CV data) and the output (number of non-connected vehicles), which are characterised by highly complex interactions and may be affected by a large number of factors. We show that, in order to train these models, we may use data that can be easily collected with modern technologies. Moreover, we demonstrate that, if the available real data is not deemed sufficient, training can be performed using synthetic data, produced via microscopic simulations calibrated with real data, without a significant loss of performance. Numerical experiments, where the estimation methods are tested using real vehicle data simulating the presence of various penetration rates of CVs, show very good performance of the estimators, making them promising candidates for applications in the near future.

Which Aggregation Fits Best? The Use of Linear Regression to Show the Influence of Temporal and Spatial Aggregation of Sparse Probe Vehicle Data on the Explanation of Travel Time Reliability

Aggregation of sparse probe vehicle data (PVD) is a crucial issue in travel time reliability (TTR) analysis. This study, therefore, examines the effect of temporal and spatial aggregation of sparse PVD on the results of a linear regression analysis where two different measures of TTR are analyzed as the dependent variable. Our results show that by aggregating the data to longer time intervals and coarser spatial units the linear model can explain a higher proportion of the variance in TTR. Furthermore, we find that the effects of road design characteristics in particular depend on the variable used to represent TTR. We conclude that the temporal and spatial aggregation of sparse PVD affects the results of linear regression explaining TTR.

The Tailored Traveller: Exploring digital vehicle data and large-scale FDM 3D printing to produce a luxury sleeping space

<p>This research is focused towards the use of large-scale FDM 3D printing within the automotive industry, specifically to design a bespoke habitable sleeping environment attached to a Range Rover Sport. 3D printing has risen as a viable form of manufacturing in comparison with conventional methods. Allowing the designer to capitalise on digital data, enabling specific tailored designs to any vehicle model. This thesis asks the question “Can design use the properties of digital vehicle data in conjunction with large-scale FDM 3D printing to sustainably produce bespoke habitable sleeping environments for an automotive context?” Further to this, FDM 3D printing at a large-scale has so far not been explored extensively within the automotive industry. FDM 3D printing is an emerging technology that possesses the ability to revolutionise the automotive industry, through expansion of functionality, customisation and aesthetic that is currently limited by traditional manufacturing methods. Presently, vehicle models are digitally mapped, creating an opportunity for customisation and automatic adaption through computer aided drawing (CAD). This thesis takes advantage of the digitisation of the automotive industry through 3D modelling and renders as a design and development tool. This project explored a variety of methods to demonstrate a vision of a 3D printed habitable sleeping environment. The primary methodologies employed in this research project are Research for Design (RfD) and Research through Design (RtD). These methodologies work in conjunction to combine design theory and practice as a genuine method of inquiry. The combination of theory and design practice has ensued in the concepts being analysed, reflected and discussed according to a reflective analysis design approach. The design solution resulted in an innovative and luxury bespoke habitable sleeping space to be FDM 3D printed. Through the use of digitisation, the sleeping capsule was cohesively tailored to the unique design language of the Range Rover Sport. This thesis resulted in various final outputs including a 1:1 digital model, high quality renders, accompanied by small scale prototypes, photographs and sketch models.</p>

The Tailored Traveller: Exploring digital vehicle data and large-scale FDM 3D printing to produce a luxury sleeping space

<p>This research is focused towards the use of large-scale FDM 3D printing within the automotive industry, specifically to design a bespoke habitable sleeping environment attached to a Range Rover Sport. 3D printing has risen as a viable form of manufacturing in comparison with conventional methods. Allowing the designer to capitalise on digital data, enabling specific tailored designs to any vehicle model. This thesis asks the question “Can design use the properties of digital vehicle data in conjunction with large-scale FDM 3D printing to sustainably produce bespoke habitable sleeping environments for an automotive context?” Further to this, FDM 3D printing at a large-scale has so far not been explored extensively within the automotive industry. FDM 3D printing is an emerging technology that possesses the ability to revolutionise the automotive industry, through expansion of functionality, customisation and aesthetic that is currently limited by traditional manufacturing methods. Presently, vehicle models are digitally mapped, creating an opportunity for customisation and automatic adaption through computer aided drawing (CAD). This thesis takes advantage of the digitisation of the automotive industry through 3D modelling and renders as a design and development tool. This project explored a variety of methods to demonstrate a vision of a 3D printed habitable sleeping environment. The primary methodologies employed in this research project are Research for Design (RfD) and Research through Design (RtD). These methodologies work in conjunction to combine design theory and practice as a genuine method of inquiry. The combination of theory and design practice has ensued in the concepts being analysed, reflected and discussed according to a reflective analysis design approach. The design solution resulted in an innovative and luxury bespoke habitable sleeping space to be FDM 3D printed. Through the use of digitisation, the sleeping capsule was cohesively tailored to the unique design language of the Range Rover Sport. This thesis resulted in various final outputs including a 1:1 digital model, high quality renders, accompanied by small scale prototypes, photographs and sketch models.</p>

Changes of seagrass area in Beralas Pasir and Beralas Bakau Island observed from Sentinel-2 Satellite and verified by Unmanned Surface Vehicle (USV)

Today, the area of seagrass ecosystems in Indonesia is estimated to have shrunk significantly. Bintan Island has quite a large seagrass ecosystems area. Along with the development of satellite technology, monitoring of conditions and changes to a coastal ecosystem can be carried out effectively through remote sensing technology. One satellite image that is relatively new and has good spatial quality is Sentinel-2 with a spatial resolution value of 10×10 m2 / pixel. Field data retrieval is facilitated by the use of Unmanned Surface Vehicle (USV). This research went through several stages such as image pre-processing, water column correction, masking, unsupervised classification, and detection of changes of seagrass area. The data obtained from the USV becomes the data for the accuracy-test in the supervised classification. Seagrass area was obtained in Beralas Pasir and Beralas Bakau Island is 84.27 ha (2016), 81.3 ha (2019) and 77.4 ha (2021). Detection of seagrass to non-seagrass area changes resulting 31.35 ha (2016-2019) and 30.91 ha (2019-2021). On the other hand non-seagrass to seagrass area is 24.84 ha (2016-2019) and 27.98 ha (2019-2021). The accuracy test of 2019 image classification and Unmanned Surface Vehicle data resulting overall accuracy at 62.20%.