Managing transition to autonomous and electric vehicles
Creators: name: Todorovic, Milan
Publisher: name: RMIT University
Year: 2024
Subjects: classificationCode: 401407
subject: Manufacturing management

description: This thesis gives contribution to the transition management, trying to support and speed up more sustainability implementation in the transport sector. Problems that have to be solved are multidisciplinary, many stakeholders are involved, including local and world governance. Constant innovations in the automotive industry created safer, more ecological and affordable vehicles. This industry faces significant changes initiated by the introduction of electrical
powertrains and autonomous vehicle (AV) technologies. These technologies reached a level of development which makes feasible their widespread deployment. That may provide significant social benefits by improving safety, saving lives, reducing fuel consumption, congestion and pollution, and improving mobility and land use. Transition from traditional cars to AVs and electric vehicles (EV) is driven by the environment sustainability awareness, convergence of connectivity, electrification and shifting customer needs. The chief objective of this research was to explore various aspects of this paradigm shift and propose an optimum pathway for the transition, which would provide a synergetic guidance framework for technological changes, government policies and consumer preferences. Both EV and AV transitions are subjects of automotive industry and governments strategic decisions. Various strategic decision-making methodologies, acceptance models and pathways of new technologies are presented.

One of the emerging methodologies in decision-making is the application of Artificial Intelligence (AI). One of the segments of AI, Fuzzy Logic, is used in control system design but also in business and strategic decisions making. How the Multi-Attribute Decision Making (MADM) methodology could be used to make strategic choices about new mobility technology is presented here and already published earlier. For example, many users are transitioning and
replacing internal combustion engine (ICE) vehicles with electric vehicles (EV) based on their advantages. However, considering the complexity, i.e., many characteristics and attributes involved, a comprehensive decision-making model is necessary to select the best solution. Global changes facing the automotive industry are a transition from vehicles with internal combustion engines to various hybrid and fully electric vehicles and the introduction of more
intelligence and communication capabilities which leads towards fully autonomous vehicles. Both transitions are complex, and decisions of the optimal pathways depend on big data and incomplete and inconsistent knowledge and expectations. Considering the fuzziness of the global business, social, ethical, and other domains, this research presents the results of the investigation and application of fuzzy logic in the decision-making process of car
manufacturers’ transition to autonomous vehicles. It considers the crisp and fuzzy information, outcomes and actions and compares additional information values.

Clustering and pattern recognition processes have applications in automotive engineering but also in transitioning to electric and autonomous vehicles. The chief characteristic of these processes is dealing with large amounts of raw data, searching for structures, classifying, and responding accordingly based on these structures. Artificial intelligence applications deal with a large amount of data. The essence is to identify and recognize the structure in data and classify them according to similar attributes, features and patterns. Among several clustering methodologies, specific consideration was dedicated to c-means clustering. This approach is founded on measuring data sets resemblances by computing the distances concerning sets of feature vectors. It can be applied to both crisp and fuzzy data sets. One of the chapters presents a method for clustering fuzzy data sets based on the iterative optimization algorithm model and Euclidian distances. This method determines optimum solutions for clustering segments when differences in subsequent iterations satisfy the pre-specified tolerance level. The clustering process finishes with one of the methods for the defuzzification of fuzzy data. It also presents a pattern recognition process that often continues the clustering process. The maximum approaching degree method is used as a similarity metric to evaluate the closeness between elements with known patterns and unrecognized data. This way of presented calculation can be expanded and applied for big data computing to find the best clustering outcome and the wanted structures in raw data.

Transport sectors contribute around 20%to 25% of the global pollution in countries worldwide. Green transportation relies on renewable energy sources rather than fossil fuels. Sustainability brings low impacts on the environment and balances current and future needs. Issues are related to the remaining available reserves of fossil fuels essential for the further use of ICEs. On another side, the availability and reserves of lithium and other metals required for EV batteries and motors may impact the swiftness of green transportation introduction. Policy implications also direct the usage of green energy for EV charging and related infrastructure development. The economics of green transportation and business models should ensure their sustainability. There are many regulatory, ethical, and technological dilemmas regarding the AV transition. Communities, governments and industry are preparing for the transition to new technology by the end of this decade. The research presented here aimed to determine if the general public and other key stakeholders are ready for the change and their degree of AV acceptance. Data analysis, from the surveys conducted, was based on statistics tools but also on fuzzy logic.

Methods applied incorporate criteria identified by survey responses that influence the degree of the new autonomous technology acceptance. These criteria are related to the positive outcomes, like reduction of air pollution, environmental sustainability, efficiency and effectiveness of new transport systems and negative issues like high cost and associated risks of automation. The surveys were conducted in Australia and Saudi Arabia, where a new futuristic, smart megacity is being launched. It is mainly based on autonomous technologies and will be powered by renewable energy with zero emissions. The survey is part of a multiyear longitudinal project to establish if the community, businesses and governments are ready to adopt new autonomous driving technology. The model developed here can be used in various applications reliant on multi-attribute constraints, envisioned to obtain a resolution that satisfies all restraints.

The concluding chapter presents research contributions. According to the literature review conducted to evaluate research contributions (Chapter 2), the article presented in Chapter 4 was one of the first to holistically consider both transitions from traditional automotive technologies to autonomous and electric vehicles. Searching the Scopus database using the keywords autonomous, electric, vehicle and transition has confirmed that. Furthermore, it was one of the first contributions to systematically create an inventory of all relevant aspects of the transition, current at the time of writing the Thesis.

Another contribution is the early consideration of some aspects of the transition, which will later become the centre of the researchers’ attention. It is primarily about sustainability and green transportation, where certain conclusions were drawn regarding the feasibility of the transition and the availability of fossil fuels that are currently used and which can strongly influence the policy development and speed of the transition.

Feasibility study, initially conducted in this research, has answered the research question of whether we could perform a transition to electric vehicles and if we have sufficient resources for that. It was found that lithium supplies will be enough for all volumes of electric vehicles produced, even though the share of lithium consumption for batteries would increase significantly, from 30% in 2015 to 95% in 2030. There are also sufficient quantities of other
metals and materials to produce batteries for this transition. Strong research in new technologies and materials for battery storage systems is intensively conducted, but at this stage, lithium-ion batteries have the best energy density in watt-hours per kilogram (Wh/kg).

In addition, the study has found that there is no threat that fossil fuel reserves will be exhausted as they are used now. In fact, despite many expectations, their quantities increase every year. The findings from this study are essential for the creation of relevant policies and for setting the deadline for this transition, which is now compulsory in many countries. For example, the European Union has set 2035 as the transition year, but Norway, which now has 56% of electric vehicles, will do so in 2025.

Despite the short deadlines for the transition to electric vehicles, the problem now being revealed and explained in the thesis is that in the past 20 years, there has been a delay in the construction and standardization of infrastructure, creating new obstructions for the sustainability transition.

Uncertainties are also present in many other domains of the transition to electric and autonomous vehicles, starting with choosing the best technology and ending with decisions on the selection and priority of investments. As an illustration, since 2010, investors have transferred nearly $330 billion into more than 2,000 mobility companies focused on some aspect of transition, with over $80 billion invested since the beginning of 2019 alone. About
two-thirds of the total investment, or $206 billion, went to autonomous-vehicle technologies and smart mobility, while about $123 billion went to connectivity and electric vehicles.

References for all data, and more details, are given in the published papers and incorporated into the thesis chapters.
The thesis also presents a contribution to the development of computational intelligence methods of clustering, pattern recognition and fuzzy system design that can be used as a complement to machine learning or in cases where there is not enough prior knowledge for the optimal use of artificial intelligence.

A detailed list of thesis contributions is given in Chapter 13, Conclusions and future work.