Cite as: Shariati, Omid; Smith, Stefan (2024): Heavy Goods Electric Vehicle (HGEV) On-Route Charging Demand Modelling Software. 
University of Reading. Software. https://doi.org/10.17864/1947.001305

Copyright 2024 University of Reading. This code is made available under the terms of the GNU General Public License 3.0: 
https://www.gnu.org/licenses/gpl-3.0.html. The accompanying documentation is issued under a Creative Commons Attribution 4.0 International License: https://creativecommons.org/licenses/by/4.0/.

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%---------------------------------------Introduction to Heavy Goods Electric Vehicle (HGEV) On-Route Charging-----------------------------------------
%---------------------------------------------------------------Demand Modelling Software-------------------------------------------------------------
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% Introducing the "Heavy Goods Electric Vehicle (HGEV) On-Route Charging Demand Modelling":
% This software package is designed to model the charging demand profile of targeted Heavy Goods Electric Vehicles (HGEV) at on-route charging stations
% operating in a public charging style. To effectively utilise the software, the following factors must be available or defined for the model:
%
%---Case Factors:
%--- 1) Total number of vehicles being charged at the station (per day): Should be defined in the related box available in the CSV file named "PopularTimes_ServiceStations".
%--- 2) Popular Time: This section shows the typical busyness of the station throughout the day based on Google Popular Times data. Popularity is calculated
% as an average over the past few months, with each hour's value displayed as a percentage relative to the station's busiest weekday hour (which is considered 100%).
% You'll find these details in hourly resolution for weekdays within the "PopularTimes_ServiceStations.csv" file.
%--- 3) Charger Power: Power rating of the available charging points at the station.
%--- 4) Charge Point Efficiency: Efficiency of the available charging points at the station.
%---Country/Operating Zone Based Factors:
%--- 1) Average Battery Capacity: Capacity of the batteries installed in the vehicles.
%--- 2) Average Specific Energy Consumption: Energy consumption per unit distance for the fleet vehicles. Averaging may be required depending on the
% available data.
%--- 3) Daily Travelled Distance: The average distance travelled by vehicles in the country/operational zone. (Currently set for the UK)
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%---The software package, developed for Heavy Goods Electric Vehicle (HGEV) On-Route public Charging station Demand Modelling, encompasses a comprehensive set of
% functions tailored to accurately simulate and analyse the charging demands of HGEVs at charging stations. Here's a breakdown of the key components:
%---1) Initialisation and Control:
% The main controlling component initialises vehicle attributes, charging station parameters, and charging parameters. It orchestrates the execution of functions
% in a prioritised order to model the load profile for a specified number of vehicles in the fleet.
%---2) Fleet Travelled Distance Generation:
% The fleet travelled distance function stochastically generates a population of travelled distances based on the fleet size. It produces a vector representing the
% distances travelled by vehicles upon arrival at the charging station.
%---3) State of Charge Calculation:
% The state of charge (SoC) function computes the SoC vector based on the travelled distance and the specific energy consumption of a typical vehicle. The generated
% SoC data is stored in an Excel file for further analysis and utilisation in the vehicle charging function.
%---4) Load Profile Visualisation:
% The load profile plotting function generates both the instantaneous load of the station demand and the averaged demand, based on specified input parameters and
% desired average demand type. This visualisation aids in understanding the distribution of charging demands over time.
%---5) Vehicle Charging Modelling:
% The vehicle charging function models the load profile of individual vehicles, taking into account their respective SoCs and the charging power available at the
% station. This ensures an accurate representation of the charging process for each vehicle in the fleet.
%---6) Vehicle Attendance Prediction:
% The vehicle attendance function predicts the distribution of vehicle arrivals based on popular times of the station and the number of vehicles being serviced.
% This feature leverages popular times data from platforms like Google Maps, providing insights into peak demand periods and optimising station resource allocation.
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% Through these integrated functionalities, our software package offers a comprehensive solution for modelling and analysing HGEV charging demands at on-route public
% charging stations, facilitating informed decision-making and efficient station management.
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%---Note: Before proceeding, please unzip the folder "HGEV_On Route_Charging_Modelling.zip" and carefully follow the instructions
% provided in the "UserGuide" file located within.
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