In order to meet the worldwide limits on greenhouse gases emissions, a shift from a fossil fuels to a renewable energy-based electric system is required. As this process goes on, both the power generation and consumption profiles are changing in daily pattern and magnitude, so the power grid needs to become more and more flexible in order to handle this variability. At the distribution level, photo-voltaic (PV) systems are, by far, the most widespread distributed energy resource, mostly due to the recent drop in the cost at the residential level. As more and more consumers become also producers (the so called "prosumers") and the volatile solar energy production increases, a higher number of storage systems is required to both avoid grid destabilisation and minimise the CO$_2$ emissions. At the same time, since the transportation sector is responsible for a sizeable part of the total CO$_2$ emissions, electric vehicles (EVs) are bound to replace traditional internal combustion engine vehicles. However, two main issues may arise when a large number of vehicles are connected to the existing power grid at the same time. The first issue is that the electricity required to charge them needs to be renewable, while the second is that, a rapid electrification of the existing vehicles fleet could destabilise the grid. In this context, this thesis aims at partially addressing these two issues by analysing different ways to reduce the impact of both PV systems and EVs on low (LV) and medium (MV) voltage grids. After the introduction and a chapter dealing with the most closely related research work, a novel optimisation algorithm, aimed at obtaining the optimal storage capacity for each prosumer belonging to a "renewable energy community" is presented. The algorithm minimises the dependence of the community on the main grid, which is one of the main purposes of this new model, while minimising the total installed storage capacity. The algorithm is tailored to the specific case study, because it keeps track of the willingness of the users to install a battery and keeps the voltage levels between regulatory limits in the optimisation process. In the second part instead, the effects of "uncontrolled" and "smart" EV-charging the electric vehicles with the aim of reducing the power fluctuations at the MV/LV transformer level are analysed. In particular, the interaction between PV production and EV charging is investigated, while considering the grid voltage fluctuations, the distribution line losses and the transformer loading levels at the same time. The broader impact of smart charging is also analysed by performing a simplified economic and battery wear analysis. Results help in understanding if storage devices can reduce the dependence of a renewable energy community on the main grid, and to what extent it is possible and economically viable to do so. Moreover, results quantify a realistic range of EV and PV system penetration in a LV grid that still allows for a combined minimisation of their impact on the power grid.

Electric vehicle charging stations (EVCS) play a vital role in providing charging support to EV users. In order to facilitate users in terms of charging speed and price, two different charging modes (L2 and L3) are currently available at public charging stations. L3 mode provides quick charging with higher power, whereas L2 mode offers moderate charging speed with low power. The integration of an EVCS into the power grid requires coordinated charging strategies in order to reduce the electricity bill for a profitable operation. However, the effective utilization of the multi-mode charging strategy to serve the maximum number of EVs for a small charging station with limited charging capacity and spots is an open issue. To this end, we propose a priority-based online charging scheme, namely PBOS, which is based on real-time information and does not depend on future knowledge. The objective is to serve as many vehicles as possible in a day while fulfilling their charging requirements under a multi-mode EVCS setting and reducing the charging costs by utilizing the time-of-use pricing based demand response strategy. Extensive simulation is done while considering two different demand response strategies under various settings. The results show that the proposed algorithm can increase profit for the EVCS by up to 48\% with a 22\% lower rejection rate. In addition, it can serve EVs with a low battery charge, known as state of charge (SOC), up to 11\% higher than most of the other schemes and can save up to 81.75 minutes to attain the same SOC when compared with other schemes. / Graduate

The promotion of Electric Vehicles (EV) has become a key measure of the governments to reduce greenhouse gas emissions. However, range anxiety is a big barrier for drivers to choose EVs over traditional vehicles. Installing more charging stations in appropriate locations can relieve EV drivers’ range anxiety. To help decide the location and number of public charging stations, we propose two optimization models for two different charging modes - fast and slow charging, which aim at minimizing the total cost while satisfying certain spatial coverage goals. Instead of using discrete points we employ network and polygons to represent charging demands. Importantly, we resolve the partial coverage problem (PCP) by segmenting the geometric objects into smaller ones using Geographic Information System (GIS) functions. We compare the geometric segmentation method (GS) and the complementary partial coverage method (CP) developed by Murray (2005) to solve the PCP. After applying the models to Greater Toronto and Hamilton Area (GTHA) and to Downtown Toronto, we show that that the proposed models are practical and effective in determining the locations and number of required charging stations. Moreover, comparison of the two methods shows that GS can fully eliminate PCP and provide much more accurate result than CP. / Thesis / Master of Science (MSc)

To reduce the negative impacts of climate change, electric vehicles (EVs) have emerged as a viable solution in the mobility sector, leading to an increase in battery production and use. For electric vehicle batteries (EVBs) to be sustainable, the total impact of the supply and value chain has to be as low as possible, making the implementation of circular economy (CE) principles a key element in the transition. There is currently limited research regarding circularity implementation in the value chains of EVBs, and especially in developing countries. This thesis explores the key stakeholders in the EVB ecosystem in Nairobi, Kenya, and identifies barriers and enablers for CE implementation. The objective of the thesis is to analyze, based on literature and empirical findings, what activities are deemed necessary to facilitate a circular value chain (CVC) for EVBs in Nairobi. To enable a realistic and thus relatively comprehensive analysis, a case study was conducted involving a literature study, interview study, document review, and workshop. Findings indicate that the Nairobi EVB ecosystem is largely unexplored but that currently, the most important stakeholders can be seen as EV manufacturers and consumers, as well as waste management actors, and second life actors. The key enablers of the ecosystem are government, government agencies, energy providers and suppliers, as well as importers and distributors. The findings indicate the possibility of new essential stakeholders in the future including battery original equipment manufacturers (OEMs), new EV manufacturers and original equipment manufacturers (OEMs), public transport companies, and second life actors. Potential ecosystem enablers include financial actors, research institutions, and insurance companies. Identified barriers and enablers of CE implementation can be divided into five areas, namely technology and infrastructure, supply chain and management, economic, policy and regulation, and social. The findings indicate that barriers identified within the different areas are interconnected, requiring coinciding strategies to be solved and making it difficult to rate them in terms of importance. The findings further suggest that for a CVC for EVBs to be initiated and supported by stakeholders, actors within the ecosystem have to adopt CE strategies to create a closed loop supply and value chain. Battery design needs to enable CE strategies, such as reuse, repair, refurbish, remanufacture, repurpose, and recycle. Furthermore, innovative business models that extend the life cycle of products need to be developed to facilitate the transition to CE. This requires collaboration between the stakeholders in the ecosystem, as well as working with the barriers and enablers identified. To create favorable and enabling system conditions, there is a need for policies and regulations to facilitate the implementation of CE strategies at end-of-life (EoL).

Indiana University-Purdue University Indianapolis (IUPUI) / In this project, a chassis concept has been developed for a small electric vehicle ’minibus’. The vehicle is intended to be used as a transport between agricultural locations in Ethiopia to cities where the products can be sold. The objective is to develop a chassis that can house several different modular structures for the purposes of transporting refrigerated goods, a mobile power grid, or people. Literature studies have been conducted on current electric vehicle markets, battery markets, chassis materials, and optimal cross-sections. The battery housings have also been analyzed from an environmental perspective to account for conditions in Ethiopia. Based on this, it was found that a four-wheeled ’minibus’ design including space for approximately fourteen custom batteries is optimal. It is essential to keep in mind that this project has been carried out both on a conceptual level within the framework of a degree project as well as a production project for use in Ethiopian rural areas. This master thesis project aims to provide a solid benchmark for further development and research within the subject.

A Hybrid Electric Vehicle (HEV) is a complex electro-mechanical-chemical system that involves two or more energy sources. The inherent advantages of HEVs are their increased fuel economy, reduced harmful emissions and better vehicle performance. The extent of improvement in fuel economy and vehicle performance greatly depends on selecting optimal component sizes. The complex interaction between the various components makes it difficult to size specific components manually or analytically. So, simulation-based multi-variable design optimization is a possible solution for such kind of system level design problems. The multi-modal, noisy and discontinuous nature of the Hybrid Vehicle design requires the use of derivativeree global algorithms because the derivative-based local algorithms work poorly with such design problems. In this thesis, a Hybrid Vehicle is optimized using various Global Algorithms ? DIviding RECTangles (DIRECT), Simulated Annealing (SA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). The objective of this study is to increase the overall fuel economy on a composite of city and highway driving cycle and to improve the vehicle performance. The performance of each algorithm is compared on a six variable hybrid electric vehicle design problem. Powertrain System Analysis Tool (PSAT), a state-of-the-art powertrain simulator, developed in MATLAB/Simulink environment by Argonne National Laboratory is used as the vehicle simulator. Further, a Hybrid algorithm that is a combination of global and local algorithm is developed to improve the convergence of the global algorithms. The hybrid algorithm is tested on two simple mathematical functions to check its efficiency.

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Personal vehicles have made great contributions to our life and satisfy our daily mobility needs. However, they have also caused societal issues, such as air pollution and global warming. Further to the recent attention to low-carbon energy technologies and environmentally friendly mobility, hybrid electric vehicles play an important role in the current automotive industry. As a leading center and an educational institution in Canada, McMaster University wants to build a Hybrid Electric Vehicle Powertrain Laboratory for introducing undergraduate students to hybrid powertrain architectures, instrumentation and control. A phased development of the hybrid powertrain teaching laboratory is being pursued. The first phase is to design a electric motor laboratory, as a platform for demonstrating motor characteristics. A LabVIEW based interface is designed to enable electric motor characterization tests. This laboratory set-up is still under construction. Real experiments would be implemented, once finishing the utility connections. For the hybrid powertrain laboratory, an innovative design architecture is proposed to enable different hybrid architectures, such as series, parallel, and power-split modes to be investigated. Instead of a planetary gearbox, bevel gearboxes with a continuous variable transmission (CVT) are used for making the laboratory more compact and flexible for demonstrating hybrid functionalities. The additional generator provides the ability of input power-split for allowing the engine to operate at a narrow high efficiency region. After designing the hybrid laboratory, a novel rule-based energy management strategy is applied to a simplified simulation model. / Thesis / Master of Applied Science (MASc)

The electric vehicle (EV) market is experiencing growth at an exponential rate, forcing automotive manufactures to invest in powertrain electrification. Manufactures are seeking low cost alternatives for electric propulsion motor technologies with switched reluctance motors (SRMs) having tremendous potential. The performance characteristics of SRMs designed for EV propulsion applications have yet to be experimentally verified. In this thesis, the operation of a 24/16 propulsion SRM specifically designed for a hybrid electric vehicle will be verified with a theoretical model and experimentally. The results are analyzed to gain further understanding of the factors affecting propulsion SRM operation. Two distinct theoretical models of a SRM are presented where one includes the effects of mutual coupling between two excited phases. The theoretical models and the experimental results indicate that for high power density SRMs, designed for propulsion applications, the effects of mutual coupling cannot be ignored. The motor is experimentally tested using a dynamometer machine. A test plan is presented which tests the motor at a wide speed and torque range suitable for EV applications. The testing procedure attempts to segregate the motor losses similar to international standards for induction machines and permanent magnet machines; however, these methods prove invalid due to the non-sinusoidal current in SRMs. Torque ripple minimization is highlighted to reduce the risk of detrimental speed fluctuation during motor testing with careful attention to thermal limitations. The SRM is tested using PWM current control as the baseline control method because hysteresis control is proven to be challenging for the tested SRM. The work presents many challenges associated with the testing and characterization of SRMs for propulsion applications; however, new research findings illustrate the potential of future improvements in propulsion SRM design and operation. / Thesis / Master of Applied Science (MASc)

The goals of the research documented in this thesis were the design, construction, modeling, and validation of a fuel cell hybrid electric vehicle based a conversion of a five-passenger production sedan. Over 60 engineering students working together as the Hybrid Electric Vehicle Team of Virginia Tech (HEVT), integrated a proton exchange membrane fuel cell system into a series hybrid electric vehicle. This design produced an efficient and truly zero-emission vehicle. This 1997 Chevrolet Lumina sedan, renamed ANIMUL H2, carries this advanced powertrain, using an efficient AC induction drivetrain, regenerative braking, compressed hydrogen fuel storage, and an advanced lead-acid battery pack for peak power load leveling. The vehicle weighed 2000 kg (4400 lb) and achieved a combined city/highway fuel economy of 9L/100 km or 26 mpgge (miles per gallon gasoline equivalent, charge depleting, state of charge corrected). A model of the vehicle was developed using ADVISOR, an Advanced Vehicle Simulator that tracks energy flow and fuel usage within the vehicle drivetrain and energy conversion components. The vehicle was tested using the Environmental Protection Agency city and highway driving cycles to provide data for validation of the model. Vehicle data and model results show good correlation at all levels and show that ADVISOR has the capability to model fuel cell hybrid electric vehicles. To make techniques proven by this work more versatile for real world application, VT worked with engineers at the National Renewable Energy Laboratory to develop a 'generic' version of this fuel cell system model that was released to the public in ADVISOR 2.2. This generic model correlates well to test data and incorporates both fuel cell stack and subsystem models. This feature allowed HEVT to predict the benefits of load following subsystem control, showing a 40% fuel economy improvement. / Master of Science