The development of electric vehicle (EV) greatly promotes building a green and sustainable society. The new technology also brings new challenges. With the penetration of electric vehicles, the charging demands are increasing, and how to efficiently coordinate EVs' charging activities is a major challenge and sparks numerous research efforts. In this dissertation, we investigate the EV charging scheduling problem under the public charging and home charging scenarios from different perspectives. First, we investigate the EV charging scheduling problem under a charging station scenario by jointly considering the revenue of the charging station and the service requirements of charging customers. We first propose an admission control algorithm to guarantee the non-flexible charging requirements of all admitted EVs being satisfied before their departure time. Then, a utility based charging scheduling algorithm is proposed to maximize the profit for the charging station. With the proposed charging scheduling algorithm, a win-win situation is achieved where the charging station enjoys a higher profit and the customer enjoys more cost savings. Second, we investigate the EV charging scheduling problem under a parking garage scenario, aiming to promote the total utility of the charging operator subject to the time-of-use pricing. By applying the analyzed battery charging characteristic, an adaptive utility oriented scheduling algorithm is proposed to achieve a high profit and low task declining probability for the charging operator. We also discuss a reservation mechanism for the charging operator to mitigate the performance degradation caused by charging information mismatching. Third, we investigate the EV charging scheduling problem of a park-and-charge system with the objective to minimize the EV battery degradation cost during the charging process while satisfying the battery charging characteristic. A vacant charging resource allocation algorithm and a dynamic power adjustment algorithm are proposed to achieve the least battery degradation cost and alleviate the peak power load, which is beneficial for both the customers and charging operator. Fourth, we investigate the EV charging scheduling problem under a residential community scenario. By jointly considering the charging energy and battery performance degradation during the charging process, we propose a utility maximization problem to optimize the gain of the community charging network. A utility maximized charging scheme is correspondingly proposed to achieve the utility optimality for the charging network. In summary, the research outcomes of the dissertation can contribute to the effective management of the EV charging activities to meet increasing charging demands. / Graduate

This thesis provides an insight into battery modeling in electric vehicles which includes degradation mechanisms as in automotive operation in electric vehicles. As electric vehicles with lithium ion batteries increase in popularity there is an increased need to study and model the capacity losses in such batteries. If there is a good understanding of the phenomena involved and an ability to predict these losses there is also a foundation to take measures to minimize these losses. In this thesis a battery model for lithium ion batteries which includes heat dissipation is used as groundwork. This model is expanded with the addition of capacity losses due to usage as well as storage. By combining this with a simple vehicle model one can use these models to achieve an understanding as to how a battery or pack of several batteries would behave in a specific driving scenario. Much of the focus in the thesis is put into comparing the different factors of degradation to highlight what the major contributors are. The conclusion is drawn that heat is the main cause for degradation for batteries in electric vehicles. This applies for driving usage as well as during storage. As heat is generated when a battery is used, the level of current is also a factor, as well as in which state of charge region the battery is used.

This thesis presents a battery electric bus energy consumption model (ECONS-M) coupled with an electrochemical battery capacity fade model (CFM). The underlying goals of the project were to develop analytical tools to support the integration of battery electric buses. ECONS-M projects the operating costs of electric bus and the potential emission reductions compared to diesel vehicles for a chosen transit route. CFM aims to predict the battery pack lifetime expected under the specific driving conditions of the route. A case study was run for a transit route in Victoria, BC chosen as a candidate to deploy a 2013 BYD electric bus. The novelty of this work mainly lays in its application to battery electric buses, as well as in the coupling of the ECONS-M and the electrochemical model to predict how long the batteries can last if the electric bus is deployed on a specific transit route everyday. An in-depot charging strategy is the only strategy examined in this thesis due to the charging rate limitations of the electrochemical model. The ECONS-M is currently being utilized in industry for the preparations of Phase I and II of the Pan-Canadian Electric Bus Demonstration & Integration Trial led by the Canadian Urban Transit Research and Innovation Consortium (CUTRIC). This project aims to deploy up to 20 battery electric buses for phase I and 60 electric buses for phase II across Canada to support the standardization of overhead fast chargers and in-depot chargers, which in a first in the world. At this time, the developed CFM can not support any final claims due to the lack of electrochemical data in the literature for the high capacity lithium-ion cells used in electric buses. This opens the door to more research in the ageing testing of batteries for heavy-duty applications. / Graduate

This work deals with problems of degradation mechanisms of lead-acid batteries. For a better understanding of the diverse effects that cause the degradation are analyzed and the physical explanation of each of the degradation mechanisms. The paper describes the measuring method by which they examined the different manifestations of degradation mechanisms. At the conclusion of works are carried out measurements in which it is possible to trace the manifestations of the various degradation mechanisms in lead battery taking place mainly on the active surface electrodes.

A form of laboratory tests are performed to determine battery degradation due to charging and discharging of batteries (cycling). This is done as part of quality assurance in battery production since a certain amount of degradation corresponds to the end of the battery lifetime. Currently, this requires a significant amount of cycling. Thus, if it’s possible to decrease the number of cycles required, the time and costs for battery degradation testing can be reduced. The aim of this thesis is therefore to create a model for prediction of battery lifetime while using early cycle data. Further, to assist planning regarding scale of cycle testing this study aims to examine the impact of implementing such a prediction model in production. To examine which data driven model that should be used to predict the battery lifetime at the company, extensive feature engineering is performed where measurements from specific cycles are used, inspired by the previous work of Severson et al. (2019) and Fei et al. (2021). Two models are then examined: Linear Regression with Elastic net and Support Vector Regression. To investigate the extent to which an implementation of such a model can affect battery testing capacity, two scenarios are compared. The first scenario is that of the current cycle testing at the company and the second scenario involves implementing a prediction model. The comparison then examines the time required for battery testing and the number of machines to cycle the batteries (cyclers). Based on the results obtained, the data driven model that should be implemented is a Support Vector Regression model with features relating to different battery cycling phases or measurements, such as charge process, temperature and capacity. It can also be shown that if a battery lifetime prediction model is implemented, it can reduce the time and number of cyclers required for testing with approximately 93 %, compared to traditional testing.

<p> In the pursuit to enable the rapid charging of lithium-ion batteries, lithium plating at the anode  poses one of the most significant challenges. Additionally, the heat generation that accompanies  high rate battery operation in conjunction with non-uniform cooling and localized heating at tabs  is known to result in thermal inhomogeneity. Such thermal anomalies in the absence of proper  thermal management can instigate accelerated degradation in the cell. This work seeks to elucidate  the link between thermal gradients and lithium plating in lithium-ion batteries using a combined  experimental and simulation-based approach. First, we experimentally characterize the lithium  plating phenomenon on graphite anodes under a wide variety of charging rates and temperatures  to gain mechanistic insights into the processes at play. An in operando detection method for the  onset of dendritic lithium plating is developed. Lithium plating regimes are identified as either  nucleate or dendritic, which exhibit vast differences in reversibility. An operando method to  quantify lithium stripping based on the rest phase voltage plateau is presented. Next, a model is  employed to provide fundamental insights to the thermo-electrochemical interactions during  charging in scenarios involving an externally imposed in-plane and inter-electrode thermal  gradient. The relative importance of in-plane vs. inter-electrode thermal gradients to charging  performance and cell degradation is necessary to inform future cell design and cooling systems for  large-format cells, which are crucial for meeting the energy requirements of applications like  electric vehicles. While in-plane thermal gradients strongly influence active material utilization,  the lithium plating severity was found to be very similar to an isothermal case at the same mean  temperature. By contrast, inter-electrode thermal gradients cause a shifting on the solid phase  potential at each electrode during charging, related to the increase or decrease in overpotential due  to local temperature variation. An experiment is then performed on a commercial multi-layer  pouch cell, in which it was found that applied thermal gradients provide a slight reduction in  lithium plating severity and degradation rate when compared to an isothermal cell at the same  mean temperature. The presence of a thermal gradient causes heterogeneous lithium plating  deposition within the cell, with colder regions experiencing higher quantities of plating and larger  thermal gradients leading to more severe heterogeneity.   </p>

While the use of Artificial Intelligence (AI) has faced a sharp up-rise in popularity in ground-based industries, such as for autonomous navigation in the automotive industry and predictive maintenance in manufacturing processes, it is yet only rarely used in space industry. Hence, this thesis aims to investigate the possibilities of using AI for processing on-board Earth-orbiting satellites while in orbit. In a first step, the interests and trends of deploying AI on-board satellites are studied, followed by challenges that are hindering the progression of its development. In a second step, five potential on-board applications are selected for investigation of their overall relevance to space industry, as well as their benefits compared to traditional approaches. Out of these, the possibility of using AI for predicting the degradation of batteries is selected for further study, as it shows the highest potential. Today’s approaches for monitoring battery degradation on satellites are highly insufficient and there is a great demand for a new approach. Several AI-based methods have been proposed in literature, but only rarely for processing directly on-board. Thus, I investigate the feasibility of adopting such an algorithm for on-board use, including an evaluation of the suitability of different algorithms, as well as the choice of input parameters and training data. I find that the use of AI could highly improve various aspects of satellite performance both on a platform and a payload level, by making them more efficient, but also more capable, such as for in-orbit battery prediction on-board. However, its implementation is still heavily hampered by the lack of validation and verification standards for AI in space, along with limitations imposed by the space environment, restricting the satellite design. In the investigation of using AI for on-board battery prediction, I find that this would be a suitable application for constellation satellites in LEO, in particular for prolonging their operations beyond their planned lifetime while still being able to ensure safe decommissioning. I estimate that this would lead to a yearly minimal average saved satellite replacement cost of $ 22 million in a constellation with 500 satellites, assuming an extension of the satellite lifetime from 7 to 7.5 years when using this application. Based on references in literature, I find that using a Long Short-Term Memory (LSTM) algorithm could make the most intricate predictions, whereas a Gated Recurrent Unit (GRU) algorithm would be less processing-heavy at the cost of a loss in accuracy. Training needs to be done on ground, either on telemetry data from past, similar missions or on synthetic data from simulations. Its implementation needs to be investigated in future research, including the selection of a suitable framework, but also benchmarking for evaluating the necessary processing power and memory space.

<div>A physics-based, multiscale framework is presented to describe the degradation in rechargeable lithium-ion batteries. The framework goes beyond traditional (empirical) coulomb-counting approaches and enables the identification of different degradation regimes of behavior. Macroscopically, five degradation mechanisms: (i) solid electrolyte interphase (SEI) growth on the anode, (ii) electrolyte oxidation on the cathode, (iii) anode active material loss and (iv) cathode active material loss due to chemomechanical fracture, and (v) dendrite growth were identified and modeled. Great emphasis was placed on describing the physics of lithium dendrite growth in order to demonstrate five distinguishable regimes: thermodynamic suppression regime, incubation regime, tip-controlled growth regime, base-controlled growth regime, and mixed growth regime. Mesoscopically, three local dendrite growth mechanisms are identified: 1) electrochemical shielding, where there is practically no electrodeposition/electrodissolution, 2) stress-induced electrodissolution and electrodeposition on those interfaces directly facing each other, generating a self-sustained overpotential that pushes the dendrites towards the counter electrode, and 3) lateral plastic extrusion in those side branches experiencing non-hydrostatic stresses. Overall, the experimentally validated theoretical framework allows to fundamentally understand battery degradation and sets the stage to design high energy density and fast charging rechargeable batteries. </div><div><br></div>

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