Lithium-ion batteries (LiBs) are the most popular choice in the shift towardselectrification due to their high volumetric energy and power density. An importantaspect to study is the effect of swelling on the mechanical performance of LiBsas it plays an important role in determining the forces in the battery module.During charge/discharge a battery cell swells/shrinks and over the lifetime of thebattery, swelling becomes permanent. The swelling increases with cycling that causesincreasing forces in the module. Excessive pressure generated due to cycling in themodule may electrically short the cells and/or cause mechanical damage to the cells.Compression pads placed between cells in the battery module absorb the swelling. Thematerial properties and size of the compression pads used influence the module forcesat End of Life (EoL).In this study, a 1D phenomenological model is built to predict the swelling forces. Themodel differs from others in literature in a way that the swelling forces are predictedwith cycling rather than State of Charge (SoC) and a stress-strain based constitutivemodel is used rather than a spring model. A process to eliminate the need for multipletests is also proposed in the thesis to predict swelling forces for different compressionpads and preloads.The proposed model is relatively simple and can improve existing battery managementsystems by predicting the swelling and the magnitude of swelling forces for differentcompression pads and preloads during the operational life of the battery.

Yes / Technological advancement on the electricity grid has focused on maximizing its use. This has led to the introduction of energy storage. Energy storage could be used to provide both peak and off-peak services to the grid. Recent work on the use of small units of energy storage like battery has proposed the vehicle to grid system. It is propose in this work to have energy storage device embedded inside the house of the energy consumer. In such a system, consumers with battery energy storage can be aggregated in to a community virtual power plant. In this paper, an optimized energy resource allocation algorithm is presented for a virtual power plant using genetic algorithm. The results show that it is critical to have a pricing scheme that help achieve goals for grid, virtual power plant, and consumers. / Mr. Oghenovo Okpako is grateful to the Niger Delta Development Commission of Nigeria for funding the work. The work has been also supported by the British Council and the UK Department of Business innovations and Skills under the GII funding of the SITARA project.

This thesis deals with issues of the issue lead-acid batteries and their application in mode of hybrid electric vehicle. The experiment is focused on the production of electrodes to determine the effect five types of additives in negative active mass in PsoC(Partial State of Charge) mode. This simulates conditions occurring in the mode of hybrid electric vehicles (HEV).

Electric vehicles have received substantial attention in the past few years since they provide a more sustainable, efficient, and greener transportation alternative in comparison to conventional fossil-fuel powered vehicles. Lithium-Ion batteries represent the most important component in the electric vehicle powertrain and thus require accurate monitoring and control. Many challenges are still facing the mass market production of electric vehicles; these challenges include battery cost, range anxiety, safety, and reliability. These challenges can be significantly mitigated by incorporating an efficient battery management system. The battery management system is responsible for estimating, in real-time, the battery state of charge, state of health, and remaining useful life in addition to communicating with other vehicle components and subsystems. In order for the battery management system to effectively perform these tasks, a high-fidelity battery model along with an accurate, robust estimation strategy must work collaboratively at various power demands, temperatures, and states of life. Lithium ion batteries are considered in this research. For these batteries, electrochemical models represent an attractive approach since they are capable of modeling lithium diffusion processes and track changes in lithium concentrations and potentials inside the electrodes and the electrolyte. Therefore, electrochemical models provide a connection to the physical reactions that occur in the battery thus favoured in state of charge and state of health estimation in comparison to other modeling techniques. The research presented in this thesis focuses on advancing the development and implementation of battery models, state of charge, and state of health estimation strategies. Most electrochemical battery models have been verified using simulation data and have rarely been experimentally applied. This is because most electrochemical battery model parameters are considered proprietary information to their manufacturers. In addition, most battery models have not accounted for battery aging and degradation over the lifetime of the vehicle using real-world driving cycles. Therefore, the first major contribution of this research is the formulation of a new battery state of charge parameterization strategy. Using this strategy, a full-set of parameters for a reduced-order electrochemical model can be estimated using real-world driving cycles while accurately calculating the state of charge. The developed electrochemical model-based state of charge parameterization strategy depends on a number of spherical shells (model states) in conjunction with the final value theorem. The final value theorem is applied in order to calculate the initial values of lithium concentrations at various shells of the electrode. Then, this value is used in setting up constraints for the optimizer in order to achieve accurate state of charge estimation. Developed battery models at various battery states of life can be utilized in a real-time battery management system. Based on the developed models, estimation of the battery critical surface charge using a relatively new estimation strategy known as the Smooth Variable Structure Filter has been effectively applied. The technique has been extended to estimate the state of charge for aged batteries in addition to healthy ones. In addition, the thesis introduces a new battery aging model based on electrochemistry. The model is capable of capturing battery degradation by varying the effective electrode volume, open circuit potential-state of charge relationship, diffusion coefficients, and solid-electrolyte interface resistance. Extensive experiments for a range of aging scenarios have been carried out over a period of 12 months to emulate the entire life of the battery. The applications of the proposed parameterization method combined with experimental aging results significantly improve the reduced-order electrochemical model to adapt to various battery states of life. Furthermore, online and offline battery model parameters identification and state of charge estimation at various states of life has been implemented. A technique for tracking changes in the battery OCV-R-RC model parameters as battery ages in addition to estimation of the battery SOC using the relatively new Smooth Variable Structure Filter is presented. The strategy has been validated at both healthy and aged battery states of life using driving scenarios of an average North-American driver. Furthermore, online estimation of the battery model parameters using square-root recursive least square (SR-RLS) with forgetting factor methodology is conducted. Based on the estimated model parameters, estimation of the battery state of charge using regressed-voltage-based estimation strategy at various states of life is applied. The developed models provide a mechanism for combining the standalone estimation strategy that provide terminal voltage, state of charge, and state of health estimates based on one model to incorporate these different aspects at various battery states of life. Accordingly, a new model-based estimation strategy known as the interacting multiple model (IMM) method has been applied by utilizing multiple models at various states of life. The method is able to improve the state of charge estimation accuracy and stability, when compared with the most commonly used strategy. This research results in a number of novel contributions, and significantly advances the development of robust strategies that can be effectively applied in real-time on-board of a battery management system. / Thesis / Doctor of Philosophy (PhD)

Current consumption measurements are useful in a wide variety of applications, including power monitoring and fault detection within a lithium battery management system (BMS). This measurement is typically taken using either a shunt resistor or a Hall-effect current transducer. Although both methods have achieved accurate current measurements, shunt resistors have inherent power loss and often require isolation circuitry, and Hall-effect sensors are generally expensive. This work explores a novel alternative to sensing battery current by measuring terminal voltages and cell temperatures and using an unknown input observer (UIO) to estimate the battery current. An accurate model of a LiFePO4 cell is created and is then used to characterize a model of the proposed current estimation technique. Finally, the current estimation technique is implemented in hardware and tested in an online BMS environment. Results show that the current estimation technique is sufficiently accurate for a variety of applications including fault detection and power profiling.

This thesis studies State-of-Charge (SOC) method for widely used lead-acid batteries and the most prospective lithium-ion batteries. First, the relationship between the battery capacity and the open-circuit-voltage under different charging/discharging currents is investigated based on the equivalent circuit. Experimental results indicate that the open-circuit-voltage of the lead-acid battery varies regularly with the charging/discharging current and the duration of time for the battery disconnected from the load. Accordingly, a dynamic open-circuit-voltage method in considerations the open-circuit-time and the previous operating current is capable of precisely estimating the battery capacity in a shorter time. As for the lithium-ion batteries, their charging/discharging characteristics reveal that the Coulomb/Ampere-Hour Counting method is capable of yielding accurate estimations. Finally, through the experiments that emulate practical operations, the SOC estimations of batteries are verified to demonstrate the effectiveness and accuracy of the proposed methods.

The thesis includes the basic principles of lead-acid, NiCd, NiMH and Li-Ion batteries. There is a description of their features and phenomena that processes in the batteries during charging and discharging. The most important part is devoted to dynamic models of these types of batteries, the parameters that are necessary for their modeling, and testing of absolute error models. Further, the model in Matlab was created which allows the simulation of the GRID-OFF photovoltaic system using the battery model.

In diesem Beitrag wird ein neuer Ansatz zur Modellierung von Lithium-Ionen-Zellen vorgestellt, bei dem neben einem Modell zur Beschreibung des Nominalverhaltens der Zelle ein Unbestimmtheitsmodell parametriert wird, welches die unvermeidbare Abweichung zwischen dem Nominalmodell und dem tatsächlichen Zellverhalten quantifiziert. Für diese Modellbeschreibung wird ein neuer Algorithmus zur Ladezustandsdiagnose entwickelt, der anstelle eines einzelnen (fehlerbehafteten) Wertes für den Ladezustand ein Vertrauensintervall angibt sowie Artefakte im zeitlichen Verlauf des geschätzten Ladezustandes vermeidet. Die Eigenschaften der Ladezustandsschätzung werden an einer Lithium-Ionen-Zelle und einem Einsatzszenario aus dem automobilen Bereich demonstriert. / In this paper, a new approach to modeling lithium ion cells is presented. In addition to a model that describes the nominal behavior of the cell, an uncertainty model is parameterized which quantifies the unavoidable difference between the nominal model and the true system behavior. For this model description a new algorithm for state of charge estimation is developed, which provides a confidence interval instead of a single unreliable value for the state of charge and avoids artifacts in the progression of the estimated state of charge over time. The properties of the state of charge estimation are demonstrated on a lithium-ion cell in an automotive application scenario.

Rechargeable batteries that are powering our everyday electronics go through their lifetime with countless charge/discharge cycles, after a few years the capacity has dropped noticeably. But how about batteries that are working as a back-up power system for when connection to the AC mains is not possible, they might only perform a handful of cycles but does this mean they last forever? On behalf of Getinge AB, this bachelor thesis aims to explore the subject named as ‘calendar aging’. Focus of the project has been mainly the impact of charging capacity, State of charge, on battery life for calendar aging and secondly the impact of heat on battery. Elevated environmental temperature during calendar aging test would imply acceleration in battery aging. The study is made with partly literature study and partly experimental testing on batteries during relatively limited time, though the results could be benchmarked with likely studies by others. The test was to investigate 12 units of NMC Li-ion batteries charged to 3 different levels of State of charge, 90 %, 95 % and 100 %, where 2 from each State of charge level were in a controlled heated environment and the other 2 were at room temperature. The test was in progress for 8 weeks and every two weeks measurement and data logging were collected and, if required, the batteries were recharged to origin voltage level. All data was documented in  and analyzed in Excel.  The results acquired were not significant enough to draw any conclusions but did show some tendency as would be expected from the literature studied during the time of the project. Future work is recommended to continue the test in this project to acquire more useful results. Further improvements in testing equipment should also be considered.

One feature that is hoped for in the smart grid is the participation of energy prosumers in a power market through demand response program. In this work, we consider a third-party virtual power plant (VPP) that has “real-time” control over a number of prosumers’ storage units within an envisaged free market. Typically, a VPP with domestic energy storage will involve a bidirectional flow of energy, where energy can either flow from the grid to the prosumers’ battery or from the prosumers’ battery to the grid. Such a system requires prices to be set correctly in order to meet the market objectives of all the VPP stakeholders (VPP Aggregator, prosumers, and grid). Previous work has shown how VPPs could operate, and the benefits of using energy storage, coupled with pricing, in terms of reducing energy cost for stakeholders and providing the grid with its required load shape. The published work either assumes prices or costs or then optimises for least cost within the grid parameters i.e. losses, voltage limits, etc. However, the setting of prices in such a way that energy can be traded among VPP stakeholders that satisfies all stakeholders’ objectives has not been fully explored in the literature, particularly with real-time VPP aggregators. In this thesis, we present novel strategies for evaluating and setting the prices of a community VPP with domestic storage based on the bidirectional flow of energy through the VPP aggregator between the grid and the prosumers that mutually meet all VPP stakeholders’ objectives. This showed that depending on pricing and the VPP objectives, demand-side management could be attractive. However, the effect on the grid in terms of the load was not what was desired. A new performance index called the “Cumulative Performance Index” CPI is proposed to measure the VPP’s performance. Using the CPI, it was possible to compare and contrast between the VPP technical performance and its business case for stakeholders. Optimizing with respect to the grid’s requirement for DSM from the VPP, it was possible to achieve a CPI of 100%. This work was implemented using a novel approach on a genetic algorithm platform. / Niger Delta Development Commission of Nigeria