Perceptions of South African original equipment manufacturers about pursuing new global electrical vehicle strategy
- Authors: Mmushi, Thabang
- Date: 2022-04
- Subjects: Electric vehicles , Motor vehicles
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10948/57936 , vital:58429
- Description: Global strategy in the automotive industry focusses on limiting climate changes and preservation of natural resources by gradually shifting towards non-gas emission transportation such as electrical vehicles (EVs). The strategic thinking and planning have advanced significantly globally. Automotive original equipment manufacturers (OEM’s) are responding to this growing demand of EVs by focusing investments on the research and development (R&D) and setting up manufacturing facilities. This EV strategy showed great degree of success already over the past few years. (Nathalie Ortar & Marianne Ryghaug, 2019) In 2013, sales of EVs were introduced in the South African market. The adoption of EVs is very low and existing studies suggest consumer perceptions towards EVs and possible social economic barriers in the South African automotive market prevent the adoption or intention to purchase EVs. The purpose of this research study was to unpack perception withholding the uptake of the EVs manufacturing in South Africa. It aimed to assess the barriers of the local automotive industry in pursuing the global strategy. The research structure was realised through conducting a literature review to explore the existing research topic. Empirical research evidence was obtained through conducting interviews which targeted existing OEMs whom the parent plants are currently manufacturing EVs globally. The study focused on key factors such as consumer knowledge, local infrastructure, and market uncertainty about EVs. The findings of the study highlighted consumers are knowledgeable about the benefits of owning EVs. However, the willingness set up a manufacturing infrastructure for EVs was highly not favourable for a majority of the factors such as power supply shortage, EVs supply chain shortage, small local market, and lack of proactive policies to drive EVs adoption or attract global investment. With these perceived positive opinions from respondents, it is down to government and private companies to work in cohesion to provide a conducive environment for EVs manufacturing in future. , Thesis (MA) -- Faculty of Business and Economic science, 2022
- Full Text:
- Date Issued: 2022-04
- Authors: Mmushi, Thabang
- Date: 2022-04
- Subjects: Electric vehicles , Motor vehicles
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10948/57936 , vital:58429
- Description: Global strategy in the automotive industry focusses on limiting climate changes and preservation of natural resources by gradually shifting towards non-gas emission transportation such as electrical vehicles (EVs). The strategic thinking and planning have advanced significantly globally. Automotive original equipment manufacturers (OEM’s) are responding to this growing demand of EVs by focusing investments on the research and development (R&D) and setting up manufacturing facilities. This EV strategy showed great degree of success already over the past few years. (Nathalie Ortar & Marianne Ryghaug, 2019) In 2013, sales of EVs were introduced in the South African market. The adoption of EVs is very low and existing studies suggest consumer perceptions towards EVs and possible social economic barriers in the South African automotive market prevent the adoption or intention to purchase EVs. The purpose of this research study was to unpack perception withholding the uptake of the EVs manufacturing in South Africa. It aimed to assess the barriers of the local automotive industry in pursuing the global strategy. The research structure was realised through conducting a literature review to explore the existing research topic. Empirical research evidence was obtained through conducting interviews which targeted existing OEMs whom the parent plants are currently manufacturing EVs globally. The study focused on key factors such as consumer knowledge, local infrastructure, and market uncertainty about EVs. The findings of the study highlighted consumers are knowledgeable about the benefits of owning EVs. However, the willingness set up a manufacturing infrastructure for EVs was highly not favourable for a majority of the factors such as power supply shortage, EVs supply chain shortage, small local market, and lack of proactive policies to drive EVs adoption or attract global investment. With these perceived positive opinions from respondents, it is down to government and private companies to work in cohesion to provide a conducive environment for EVs manufacturing in future. , Thesis (MA) -- Faculty of Business and Economic science, 2022
- Full Text:
- Date Issued: 2022-04
A scheduling model for the charging of electric vehicles in photovoltaic powered smart microgrids
- Authors: Nyumbeka, Dumisani Joshua
- Date: 2020
- Subjects: Electric vehicles , Photovoltaic power generation Photovoltaic power generation -- Developing countries
- Language: English
- Type: Thesis , Doctoral , DPhil
- Identifier: http://hdl.handle.net/10948/49259 , vital:41615
- Description: Electric vehicles (EVs) have emerged as a viable option to advance sustainable mobility, but adoption is still relatively low. This has been largely due to the limited range one can travel on a single charge, leading to range anxiety, longer charge cycles and long wait times at charging stations. One solution to range anxiety is to erect charging stations on major roads and urban centres. There is also a lack of real-time information regarding the state of charging stations and charging ports in existing charging infrastructure. To increase the benefit of using EVs, using renewable energy sources, such as photovoltaics (PV) to power EVs, can further increase the benefit of reduced carbon footprint. The main research objective was to design a Charge Scheduling Model for charging EVs using a PV-powered smart microgrid (SMG). The model addresses the lack of an integrated platform where EV drivers can schedule when and where to charge their EVs. The model also reduces the negative effects of the adoption of EVs, including range anxiety. The Charge Scheduling Model was developed using the Design Science Research (DSR) methodology and was the main artefact of the study. A literature study was conducted of research related to SMGs, renewable energy, EVs and scheduling, to identify shortcomings that currently exist in EV charge scheduling (EVCS), and to identify the requirements of a potential solution. The literature study also identified the hard and soft constraints that are unique to EVCS, and the available energy in the SMG was identified as one of the hard constraints. Therefore, an Energy Forecasting Model for forecasting energy generated in PV-powered SMGs was required before the Charge Scheduling Model could be designed. During the first iteration of the design and development activities of DSR, four models were designed and implemented to evaluate their effectiveness in forecasting the energy generated in PV-powered SMGs. The models were Support Vector Regression (SVR), K-Nearest Neighbour (KNN), Decision Trees, and Multilayer Perceptron. In the second iteration, the Charge Scheduling Model was designed, consisting of a Four Layered Architecture and the Three-Phase Data Flow Process. The Charge Scheduling Model was then used to design the EVCS prototype. The implementation of the EVCS prototype followed the incremental prototyping approach, which was used to verify the effectiveness of the model. An artificial-summative evaluation was used to evaluate the design of the Charge Scheduling Model, whereas iterative formative evaluations were conducted during the development of the EVCS prototype. The theoretical contribution of this study is the Charge Scheduling Model, and the EVCS prototype is the practical contribution. The results from both evaluations, i.e. the Energy Forecasting Model and the Charge Scheduling Model, also make a contribution to the body of knowledge of EVs.
- Full Text:
- Date Issued: 2020
- Authors: Nyumbeka, Dumisani Joshua
- Date: 2020
- Subjects: Electric vehicles , Photovoltaic power generation Photovoltaic power generation -- Developing countries
- Language: English
- Type: Thesis , Doctoral , DPhil
- Identifier: http://hdl.handle.net/10948/49259 , vital:41615
- Description: Electric vehicles (EVs) have emerged as a viable option to advance sustainable mobility, but adoption is still relatively low. This has been largely due to the limited range one can travel on a single charge, leading to range anxiety, longer charge cycles and long wait times at charging stations. One solution to range anxiety is to erect charging stations on major roads and urban centres. There is also a lack of real-time information regarding the state of charging stations and charging ports in existing charging infrastructure. To increase the benefit of using EVs, using renewable energy sources, such as photovoltaics (PV) to power EVs, can further increase the benefit of reduced carbon footprint. The main research objective was to design a Charge Scheduling Model for charging EVs using a PV-powered smart microgrid (SMG). The model addresses the lack of an integrated platform where EV drivers can schedule when and where to charge their EVs. The model also reduces the negative effects of the adoption of EVs, including range anxiety. The Charge Scheduling Model was developed using the Design Science Research (DSR) methodology and was the main artefact of the study. A literature study was conducted of research related to SMGs, renewable energy, EVs and scheduling, to identify shortcomings that currently exist in EV charge scheduling (EVCS), and to identify the requirements of a potential solution. The literature study also identified the hard and soft constraints that are unique to EVCS, and the available energy in the SMG was identified as one of the hard constraints. Therefore, an Energy Forecasting Model for forecasting energy generated in PV-powered SMGs was required before the Charge Scheduling Model could be designed. During the first iteration of the design and development activities of DSR, four models were designed and implemented to evaluate their effectiveness in forecasting the energy generated in PV-powered SMGs. The models were Support Vector Regression (SVR), K-Nearest Neighbour (KNN), Decision Trees, and Multilayer Perceptron. In the second iteration, the Charge Scheduling Model was designed, consisting of a Four Layered Architecture and the Three-Phase Data Flow Process. The Charge Scheduling Model was then used to design the EVCS prototype. The implementation of the EVCS prototype followed the incremental prototyping approach, which was used to verify the effectiveness of the model. An artificial-summative evaluation was used to evaluate the design of the Charge Scheduling Model, whereas iterative formative evaluations were conducted during the development of the EVCS prototype. The theoretical contribution of this study is the Charge Scheduling Model, and the EVCS prototype is the practical contribution. The results from both evaluations, i.e. the Energy Forecasting Model and the Charge Scheduling Model, also make a contribution to the body of knowledge of EVs.
- Full Text:
- Date Issued: 2020
A multi-factor model for range estimation in electric vehicles
- Authors: Smuts, Martin Bradley
- Date: 2019
- Subjects: Electric vehicles , Hybrid electric vehicles Energy consumption Machine learning Information technology -- Management
- Language: English
- Type: Thesis , Doctoral , DPhil
- Identifier: http://hdl.handle.net/10948/43589 , vital:36926
- Description: Electric vehicles (EVs) are well-known for their challenges related to trip planning and energy consumption estimation. Range anxiety is currently a barrier to the adoption of EVs. One of the issues influencing range anxiety is the inaccuracy of the remaining driving range (RDR) estimate in on-board displays. RDR displays are important as they can help drivers with trip planning. The RDR is a parameter that changes under environmental and behavioural conditions. Several factors (for example, weather, and traffic) can influence the energy consumption of an EV that are not considered during the RDR estimation in traditional on-board computers or third-party applications, such as navigation or mapping applications. The need for accurate RDR estimation is growing, since this can reduce the range anxiety of drivers. One way of overcoming range anxiety is to provide trip planning applications that provide accurate estimations of the RDR, based on various factors, and which adapt to the users’ driving behaviour. Existing models used for estimating the RDR are often simplified, and do not consider all the factors that can influence it. Collecting data for each factor also presents several challenges. Powerful computing resources are required to collect, transform, and analyse the disparate datasets that are required for each factor. The aim of this research was to design a Multi-factor Model for range estimation in EVs. Five main factors that influence the energy consumption of EVs were identified from literature, namely, Route and Terrain, Driving Behaviour, Weather and Environment, Vehicle Modelling, and Battery Modelling. These factors were used throughout this research to guide the data collection and analysis processes. A Multi-factor Model was proposed based on four main components that collect, process, analyse, and visualise data from available data sources to produce estimates relating to trip planning. A proof-of-concept RDR system was developed and evaluated in field experiments, to demonstrate that the Multi-factor Model addresses the main aim of this research. The experiments were performed to collect data for each of the five factors, and to analyse their impact on energy consumption. Several machine learning techniques were used, and evaluated, for accuracy in estimating the energy consumption, from which the RDR can be derived, for a specified trip. A case study was conducted with an electric mobility programme (uYilo) in Port Elizabeth, South Africa (SA). The case study was used to investigate whether the available resources at uYilo were sufficient to provide data for each of the five factors. Several challenges were noted during the data collection. These were shortages of software applications, a lack of quality data, technical interoperability and data access between the data collection instruments and systems. Data access was a problem in some cases, since proprietary systems restrict access to external developers. The theoretical contribution of this research is a list of factors that influence RDR and a classification of machine learning techniques that can be used to estimate the RDR. The practical contributions of this research include a database of EV trips, proof-of-concept RDR estimation system, and a deployed machine learning model that can be accessed by researchers and EV practitioners. Four research papers were published and presented at local and international conferences. In addition, one conference paper was published in an accredited journal: NextComp 2017 (Appendix C), Conference Paper, Pointe aux Piments (Mauritius); SATNAC 2017 (Appendix F), Conference Paper, Barcelona (Spain); GITMA 2018 (Appendix B), Conference Paper, Mexico City (Mexico); SATNAC 2018 (Appendix G), Conference Paper, George (South Africa), and IFIP World Computer Congress 2018 (Appendix E), Journal Article.
- Full Text:
- Date Issued: 2019
- Authors: Smuts, Martin Bradley
- Date: 2019
- Subjects: Electric vehicles , Hybrid electric vehicles Energy consumption Machine learning Information technology -- Management
- Language: English
- Type: Thesis , Doctoral , DPhil
- Identifier: http://hdl.handle.net/10948/43589 , vital:36926
- Description: Electric vehicles (EVs) are well-known for their challenges related to trip planning and energy consumption estimation. Range anxiety is currently a barrier to the adoption of EVs. One of the issues influencing range anxiety is the inaccuracy of the remaining driving range (RDR) estimate in on-board displays. RDR displays are important as they can help drivers with trip planning. The RDR is a parameter that changes under environmental and behavioural conditions. Several factors (for example, weather, and traffic) can influence the energy consumption of an EV that are not considered during the RDR estimation in traditional on-board computers or third-party applications, such as navigation or mapping applications. The need for accurate RDR estimation is growing, since this can reduce the range anxiety of drivers. One way of overcoming range anxiety is to provide trip planning applications that provide accurate estimations of the RDR, based on various factors, and which adapt to the users’ driving behaviour. Existing models used for estimating the RDR are often simplified, and do not consider all the factors that can influence it. Collecting data for each factor also presents several challenges. Powerful computing resources are required to collect, transform, and analyse the disparate datasets that are required for each factor. The aim of this research was to design a Multi-factor Model for range estimation in EVs. Five main factors that influence the energy consumption of EVs were identified from literature, namely, Route and Terrain, Driving Behaviour, Weather and Environment, Vehicle Modelling, and Battery Modelling. These factors were used throughout this research to guide the data collection and analysis processes. A Multi-factor Model was proposed based on four main components that collect, process, analyse, and visualise data from available data sources to produce estimates relating to trip planning. A proof-of-concept RDR system was developed and evaluated in field experiments, to demonstrate that the Multi-factor Model addresses the main aim of this research. The experiments were performed to collect data for each of the five factors, and to analyse their impact on energy consumption. Several machine learning techniques were used, and evaluated, for accuracy in estimating the energy consumption, from which the RDR can be derived, for a specified trip. A case study was conducted with an electric mobility programme (uYilo) in Port Elizabeth, South Africa (SA). The case study was used to investigate whether the available resources at uYilo were sufficient to provide data for each of the five factors. Several challenges were noted during the data collection. These were shortages of software applications, a lack of quality data, technical interoperability and data access between the data collection instruments and systems. Data access was a problem in some cases, since proprietary systems restrict access to external developers. The theoretical contribution of this research is a list of factors that influence RDR and a classification of machine learning techniques that can be used to estimate the RDR. The practical contributions of this research include a database of EV trips, proof-of-concept RDR estimation system, and a deployed machine learning model that can be accessed by researchers and EV practitioners. Four research papers were published and presented at local and international conferences. In addition, one conference paper was published in an accredited journal: NextComp 2017 (Appendix C), Conference Paper, Pointe aux Piments (Mauritius); SATNAC 2017 (Appendix F), Conference Paper, Barcelona (Spain); GITMA 2018 (Appendix B), Conference Paper, Mexico City (Mexico); SATNAC 2018 (Appendix G), Conference Paper, George (South Africa), and IFIP World Computer Congress 2018 (Appendix E), Journal Article.
- Full Text:
- Date Issued: 2019
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