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
The contribution of guest houses to economic growth and employment as key components of local economic development in the Eden District Area
- Authors: Ramukumba, Takalani
- Date: 2015
- Subjects: Economic development -- South Africa -- Western Cape , Sustainable tourism -- South Africa -- Western Cape
- Language: English
- Type: Thesis , Doctoral , DPhil
- Identifier: http://hdl.handle.net/10948/5214 , vital:20821
- Description: Tourism has come to be seen as a key driver for local economic development in South Africa, as it provides opportunities for pro-poor and community-based initiatives. On a global scale, the challenges of confronting poverty and unemployment continue to dominate the development agenda. The ability of Local Economic Development (LED) to empower local people has earned favour with national governments and development theorists. The imperative facing South Africa to achieve a more equitable and sustainable economy is essentially the challenge to adopt and implement a development approach that will reduce poverty and unemployment (which are the two key objectives of LED) to the greatest extent. It is within this context that the South African government has sought to incorporate LED into their economic development framework, predominantly through the decentralisation of development control and planning to the local government level. This study examined the contribution of guest houses to economic growth and employment as key components of LED in a sustainable manner. The study revealed that guest houses are playing a key role in the development of the local economy in the Eden district region. Guest houses are providing employment opportunities to the local residents both on a full-time and part-time basis. Further to this, guest houses are buying many locally-produced products and services from local suppliers and this contributes to economic growth of the local economy. However, this study also found that many of the guest houses in the area are not aware of government incentives available to support them and very few of them have made use of these services. This is something that needs to be addressed if these guest houses are to continue to strengthen the local economy and provide employment opportunities in a sustainable manner. The study revealed that many of the guest houses are operating in an environmentally friendly manner and this will ensure their future sustainability. The broader situation and the contribution of the accommodation sector as critical assets in local and national tourism economies has been thoroughly researched in tourism research around the world. Existing work on the accommodation sector in the South African tourism economy is mainly urban-focused and indicates that its local development impacts can be positive albeit not always maximised through local linkages, however, only a limited amount of academic investigations examines the contribution of tourism sub-sectors to economic growth and employment. The authenticity of this study is based on Its contribution which must be viewed in relation to the relatively limited body of literature in the contribution of tourism sub-sectors to economic growth and employment and in this case guest houses as one type of accommodation sub-sector.
- Full Text:
- Date Issued: 2015
- Authors: Ramukumba, Takalani
- Date: 2015
- Subjects: Economic development -- South Africa -- Western Cape , Sustainable tourism -- South Africa -- Western Cape
- Language: English
- Type: Thesis , Doctoral , DPhil
- Identifier: http://hdl.handle.net/10948/5214 , vital:20821
- Description: Tourism has come to be seen as a key driver for local economic development in South Africa, as it provides opportunities for pro-poor and community-based initiatives. On a global scale, the challenges of confronting poverty and unemployment continue to dominate the development agenda. The ability of Local Economic Development (LED) to empower local people has earned favour with national governments and development theorists. The imperative facing South Africa to achieve a more equitable and sustainable economy is essentially the challenge to adopt and implement a development approach that will reduce poverty and unemployment (which are the two key objectives of LED) to the greatest extent. It is within this context that the South African government has sought to incorporate LED into their economic development framework, predominantly through the decentralisation of development control and planning to the local government level. This study examined the contribution of guest houses to economic growth and employment as key components of LED in a sustainable manner. The study revealed that guest houses are playing a key role in the development of the local economy in the Eden district region. Guest houses are providing employment opportunities to the local residents both on a full-time and part-time basis. Further to this, guest houses are buying many locally-produced products and services from local suppliers and this contributes to economic growth of the local economy. However, this study also found that many of the guest houses in the area are not aware of government incentives available to support them and very few of them have made use of these services. This is something that needs to be addressed if these guest houses are to continue to strengthen the local economy and provide employment opportunities in a sustainable manner. The study revealed that many of the guest houses are operating in an environmentally friendly manner and this will ensure their future sustainability. The broader situation and the contribution of the accommodation sector as critical assets in local and national tourism economies has been thoroughly researched in tourism research around the world. Existing work on the accommodation sector in the South African tourism economy is mainly urban-focused and indicates that its local development impacts can be positive albeit not always maximised through local linkages, however, only a limited amount of academic investigations examines the contribution of tourism sub-sectors to economic growth and employment. The authenticity of this study is based on Its contribution which must be viewed in relation to the relatively limited body of literature in the contribution of tourism sub-sectors to economic growth and employment and in this case guest houses as one type of accommodation sub-sector.
- Full Text:
- Date Issued: 2015
Corporate social responsibility: a competitive strategy for small and medium-sized enterprises in Uganda
- Authors: Turyakira, Peter
- Date: 2012
- Subjects: Competition , Social responsibility of business -- Uganda , Corporate culture -- Uganda , Corporations -- Moral and ethical aspects
- Language: English
- Type: Thesis , Doctoral , DPhil
- Identifier: vital:9295 , http://hdl.handle.net/10948/d1012648 , Competition , Social responsibility of business -- Uganda , Corporate culture -- Uganda , Corporations -- Moral and ethical aspects
- Description: In view of the important role small and medium-sized enterprises (SMEs) universally play as the backbone of national economies and the survival and competitiveness challenges that they face, the purpose of this study was to develop specific models of corporate social responsibility (CSR) for SMEs in Uganda as an avenue to enhance their competitiveness and foster economic development. The primary objective was to gain insight into the deployment of CSR in SMEs, including investigating CSR factors and their potential impact on competitiveness. This study integrates previous findings and theories on CSR activities and SMEs‟ competitiveness into a comprehensive hypothesised model. A comprehensive literature study revealed potential factors that could influence the Increased competitiveness of SMEs in Uganda. Four independent variables (Workforce-oriented, Society-oriented, Market-oriented and Environmental-oriented CSR activities) and three mediating variables (Employee satisfaction, Business reputation and Customer loyalty) were identified as variables influencing the Increased competitiveness (dependent variable) of SMEs. Independent variables were categorised as CSR factors while mediating and dependent variables were categorised as outcomes factors. Furthermore, hypotheses were formulated for possible relationships between the independent, mediating and dependent variables. All the variables in the study were clearly defined and operationalised. Reliable and valid items sourced from various measuring instruments used in other similar studies, were used in the operationalisation of these variables. Furthermore, several items were generated from secondary sources. A structured self-administered questionnaire was made available to respondents identified using the stratified and purposive sampling techniques, and the data collected from 383 usable questionnaires was subjected to several statistical analyses. The validity and reliability of the measuring instrument was ascertained using an exploratory factor analysis and Cronbach-alpha coefficients respectively. An exploratory factor analysis using SPSS 18 for Windows was conducted to identify the unique factors available in the data before applying structural equation modelling (SEM). The data were categorised into models of independent variables (CSR factors) and the mediating variables (Outcomes factors). The items measuring Market-oriented CSR activities and Workforce-oriented CSR activities loaded as expected. The items measuring Environmental-oriented CSR activities loaded onto two separate factors which were renamed Environmental-oriented CSR activities and Regulated CSR activities. One of the items originally expected to measure the construct Society-oriented CSR activities loaded onto Environmental-oriented CSR activities, leaving three items which loaded together onto the Society-oriented CSR activities factor. Four factors constituted the outcomes submodel, namely Customer loyalty, Stakeholder trust, Business reputation, and Employee satisfaction. In this study, SEM was the main statistical procedure used to test the significance of the relationships hypothesised between the various independent and dependent variables. Owing to the sample size limitations, the hypothesised model could not be subjected to SEM as a whole. Consequently, six sub-models were identified and subjected to further analysis. The following independent variables were identified as influencing the dependent variables in this study: Workforce-oriented CSR activities, Society-oriented CSR activities, Market-oriented CSR activities, Environmental-oriented CSR activities, Regulated CSR activities. To establish the influence of the various demographic variables on the mediating and dependent variables, an Analysis of Variance (ANOVA) and Multiple Linear Regression (MLR) analysis were conducted. The respondent‟s position/title in the business, form of enterprise, branch/sector of business, level of education, and the size of business were found to have an influence on the mediating and dependent variables of this study. This study has therefore added to the underdeveloped body of business research in Uganda by investigating a particularly limited segment of the literature, namely SMEs. The study has also identified and developed various models that explain the most significant CSR factors that influence the competitiveness of SMEs. Consequently, this study has put forward several recommendations and suggestions that can enhance the competitiveness of SMEs locally and globally. Further research is encouraged on action-oriented areas such as: the success of different policies and techniques to increase the uptake of CSR amongst SMEs; the economic, social and environmental impact of CSR at sector level; and a typology of SMEs with regard to their engagement in CSR.
- Full Text:
- Date Issued: 2012
- Authors: Turyakira, Peter
- Date: 2012
- Subjects: Competition , Social responsibility of business -- Uganda , Corporate culture -- Uganda , Corporations -- Moral and ethical aspects
- Language: English
- Type: Thesis , Doctoral , DPhil
- Identifier: vital:9295 , http://hdl.handle.net/10948/d1012648 , Competition , Social responsibility of business -- Uganda , Corporate culture -- Uganda , Corporations -- Moral and ethical aspects
- Description: In view of the important role small and medium-sized enterprises (SMEs) universally play as the backbone of national economies and the survival and competitiveness challenges that they face, the purpose of this study was to develop specific models of corporate social responsibility (CSR) for SMEs in Uganda as an avenue to enhance their competitiveness and foster economic development. The primary objective was to gain insight into the deployment of CSR in SMEs, including investigating CSR factors and their potential impact on competitiveness. This study integrates previous findings and theories on CSR activities and SMEs‟ competitiveness into a comprehensive hypothesised model. A comprehensive literature study revealed potential factors that could influence the Increased competitiveness of SMEs in Uganda. Four independent variables (Workforce-oriented, Society-oriented, Market-oriented and Environmental-oriented CSR activities) and three mediating variables (Employee satisfaction, Business reputation and Customer loyalty) were identified as variables influencing the Increased competitiveness (dependent variable) of SMEs. Independent variables were categorised as CSR factors while mediating and dependent variables were categorised as outcomes factors. Furthermore, hypotheses were formulated for possible relationships between the independent, mediating and dependent variables. All the variables in the study were clearly defined and operationalised. Reliable and valid items sourced from various measuring instruments used in other similar studies, were used in the operationalisation of these variables. Furthermore, several items were generated from secondary sources. A structured self-administered questionnaire was made available to respondents identified using the stratified and purposive sampling techniques, and the data collected from 383 usable questionnaires was subjected to several statistical analyses. The validity and reliability of the measuring instrument was ascertained using an exploratory factor analysis and Cronbach-alpha coefficients respectively. An exploratory factor analysis using SPSS 18 for Windows was conducted to identify the unique factors available in the data before applying structural equation modelling (SEM). The data were categorised into models of independent variables (CSR factors) and the mediating variables (Outcomes factors). The items measuring Market-oriented CSR activities and Workforce-oriented CSR activities loaded as expected. The items measuring Environmental-oriented CSR activities loaded onto two separate factors which were renamed Environmental-oriented CSR activities and Regulated CSR activities. One of the items originally expected to measure the construct Society-oriented CSR activities loaded onto Environmental-oriented CSR activities, leaving three items which loaded together onto the Society-oriented CSR activities factor. Four factors constituted the outcomes submodel, namely Customer loyalty, Stakeholder trust, Business reputation, and Employee satisfaction. In this study, SEM was the main statistical procedure used to test the significance of the relationships hypothesised between the various independent and dependent variables. Owing to the sample size limitations, the hypothesised model could not be subjected to SEM as a whole. Consequently, six sub-models were identified and subjected to further analysis. The following independent variables were identified as influencing the dependent variables in this study: Workforce-oriented CSR activities, Society-oriented CSR activities, Market-oriented CSR activities, Environmental-oriented CSR activities, Regulated CSR activities. To establish the influence of the various demographic variables on the mediating and dependent variables, an Analysis of Variance (ANOVA) and Multiple Linear Regression (MLR) analysis were conducted. The respondent‟s position/title in the business, form of enterprise, branch/sector of business, level of education, and the size of business were found to have an influence on the mediating and dependent variables of this study. This study has therefore added to the underdeveloped body of business research in Uganda by investigating a particularly limited segment of the literature, namely SMEs. The study has also identified and developed various models that explain the most significant CSR factors that influence the competitiveness of SMEs. Consequently, this study has put forward several recommendations and suggestions that can enhance the competitiveness of SMEs locally and globally. Further research is encouraged on action-oriented areas such as: the success of different policies and techniques to increase the uptake of CSR amongst SMEs; the economic, social and environmental impact of CSR at sector level; and a typology of SMEs with regard to their engagement in CSR.
- Full Text:
- Date Issued: 2012
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