Browsing by Author "Neigum, Kaila Marie"

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    Optimize performance of second life batteries in an electric vehicle charging network
    (Faculty of Graduate Studies and Research, University of Regina, 2024-07) Neigum, Kaila Marie; Wang, Zhanle (Gerald); Zhang, Lei
    The ever-increasing concerns of climate change has led to an exponential growth of electric vehicles (EVs) as a way to decarbonize the transportation sector. Although this solution appears promising, this method to reduce transportation emissions does comes with its challenges. Two main criticisms of EVs are the increased electricity demand due to EV charging participation and the accumulation of retired EV batteries. This study aims to provide a solution to both of these concerns. The EV battery can only sustain the vehicle's needs for a limited time. Once the battery reaches a state of health (SOH) that can no longer support the driving requirements of the car, acceleration and duration, it must be retired from the EV. However, these batteries still have much remaining capacity and can be used in less demanding applications where they are referred to as second life batteries (SLBs). This study investigates utilizing SLBs to reduce the peak demand of an EV charging network. A hypothetical charging station in Saskatchewan will be under study as this province operates in a unique, xed-price market which is uncommonly found in previous literature. Since battery degradation is the premise of SLBs, further battery degradation in the form of both calendar and cycle aging will be included in the model. A multi-objective problem is formulated to minimize operational costs of the charging station by balancing battery degradation with the cost of demand, and to achieve optimal performance of the SLB by extending its remaining useful life (RUL). The algorithm updates the battery's SOH daily, and continues to run until the end of life condition of 70% SOH has been reached. The algorithm rst investigated considering only demand in the objective function, followed by the aforementioned multi-objective approach. It was found that considering battery degradation in the objective function allowed for optimal control of the charge and discharge scheduling of the ESS, and by doing so, the RUL of the SLB was signi cantly extended. Next, a new ESS was investigated using the same algorithm. The new ESS was able to generate marginally lower costs and higher savings compared to the SLB, however the SLB still provided signi cantly lower costs, compared to not incorporating ESS at all, at half the investment cost. Various economic metrics, such as total cost of operation, total savings, return on investment, net present value, payback period, and internal rate of return, are used in this study to draw conclusions on SLB use compared to new ESS. The data used in this study was to demonstrate the algorithm and to assess the use of SLBs to new ESS. Further, this study aims to o er a framework for charging station owners to use in assessing the feasibility of SLBs in their respective charging stations.