Indiana University-Purdue University Indianapolis (IUPUI) / Accurate estimation of State of Charge (SOC) is crucial. With the ever-increasing usage of batteries, especially in safety critical applications, the requirement of accurate estimation of SOC is paramount. Most current methods of SOC estimation rely on data collected and calibrated offline, which could lead to inaccuracies in SOC estimation as the battery ages or under different operating conditions. This work aims at exploring the real-time estimation and optimization of SOC by applying Particle Swarm Optimization (PSO) to a detailed electrochemical model of a single cell. The goal is to develop a single cell model and PSO algorithm which can run on an embedded device with reasonable utilization of CPU and memory resources and still be able to estimate SOC with acceptable accuracy. The scope is to demonstrate the accurate estimation of SOC for 1C charge and discharge for both healthy and aged cell.

Renewable energy integration will become a significant issue as renewable penetration levels increase, and will require new generation support infrastructure; Energy storage provides one solution to this issue. Specifically, battery technologies offer a wide range of energy and power output abilities, making them ideal for a variety of integration applications. Distributed energy storage on distribution grids may be required in many areas of Canada where renewables will be installed. Peak shaving using distributed small (residential) energy storage can provide a reduction in peak loads and help renewable energy integration. To this end, a peak shaving model was developed for typical houses in several regions in Canada which provided sizing and performance results. An experimental battery bank and cycling apparatus was designed and constructed using these sizing results. This battery bank and cycling apparatus was then used to calibrate and validate a lithium iron phosphate battery energy storage system model.

In this thesis, we develop an approach to characterizing the uncertainty in energy use toward development of a real-time intelligent energy monitoring system for an autonomous under- water vehicle (AUV). The purpose of the intelligent energy monitoring system is to estimate current energy onboard the AUV, estimate energy needed to complete a desired mission, and to determine if and when the AUV should terminate the current mission and return to the recovery location due low energy reserves. In this work, we examine the relationship between water currents and energy used by the AUV, and we specifically address ways to characterize the relationship between uncertainty in water currents and uncertainty in energy use. We also examine the development of a battery model for the AUV, and test this model under simulated and real world conditions. We also develop a model for predicting future energy states, and evaluate this model using real world trials. / Master of Science / In this thesis, we develop an approach to characterizing the uncertainty in energy use for an energy monitoring system for an autonomous underwater vehicle (AUV). The purpose of the energy monitoring system is to estimate current energy onboard the AUV, estimate energy needed to complete a desired mission, and to determine if and when the AUV should cancel the mission and return to the recovery location due low energy levels. In this work, we examine the relationship between water currents and energy used by the AUV, and we specifically address ways to characterize the relationship between uncertainty in water currents and uncertainty in energy use. We also examine the development of a battery model for the AUV, and test this model under simulated and real world conditions, and develop a model for predicting future energy levels.

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The ability for domestic consumers to provide demand response to dynamic electricity pricing will become increasingly valuable for integrating the high penetrations of renewables that are expected to be connected to electricity networks in the future. The aim of this thesis is to investigate whether domestic consumers will be willing and able to provide demand response in such low-carbon futures. A broad approach is presented in this thesis, with research contributions on subjects including data privacy, behavioural economics, and battery modelling. The principle argument of the thesis is that studying the behaviour of consumers with grid-connected photovoltaic ('PV') systems can provide insight into how consumers might respond to dynamic pricing in future low-carbon power systems, as both experience irregular electricity prices that are correlated with intermittent renewable generation. Through a combination of statistical and qualitative methods, this thesis investigates the demand response behaviour of consumers with PV systems in the UK. The results demonstrate that these consumers exhibit demand response behaviour by increasing demand during the day and decreasing demand during the evening. Furthermore, this effect is more pronounced on days with higher irradiance. The results are novel in three ways. First, they provide quantified evidence that suggests that domestic consumers with PV systems engage in demand response behaviour. Second, they provide evidence of domestic consumers responding to irregular electricity prices that are correlated with intermittent renewable generation, thereby addressing the aim of this thesis, and supporting the assumption that consumers can be expected to respond to dynamic pricing in future markets with high penetrations of renewables. Third, they provide evidence of domestic consumers responding to dynamic pricing that is similar to real-time pricing, while prior evidence of this is rare and confined to the USA.

Hybrid Electric Vehicles (HEVs) and Electric Vehicles (EVs) are crucial technologies for the automotive industry to meet society’s demands for cleaner, more energy efficient transportation. Meeting the need to provide power which sustains HEVs and EVs is an immediate area of concern that research and development within the automotive community must address. Electric batteries and electrical motors are the key components in HEV and EV power generation and transmission, and their performance plays very important role in the overall performance of the modern high efficiency vehicles. Therefore, in this dissertation, we are motivated to study the electric batteries, interior permanent motor (IPM), in the context of modern hybrid electric/electric drive systems, from both multi-physics and system level perspectives. Electrical circuit theory, electromagnetic Finite Element Analysis (FEA), and Computational Fluid Dynamic (CFD) finite volume method will be used primarily in this work. The work has total of five parts, and they are introduced in the following. Firstly, Battery thermal management design is critical in HEV and EV development. Accurate temperature distribution of the battery cells during vehicle operation is required for achieving optimized design. We propose a novel electrical-thermal battery modeling technique that couples a temperature dependent battery circuit model and a physics-based CFD model to meet this need. The electrical circuit model serves as a heat generation mechanism for the CFD model, and the CFD model provides the temperature distribution of the battery cells, which can also impact the heat generation of the electrical battery model. In this part of work, simulation data has been derived from the model respective to electrical performance of the battery as well iv as the temperature distribution simultaneously in consideration of the physical dimensions, material properties, and cooling conditions. The proposed model is validated against a battery model that couples the same electrical model with a known equivalent thermal model. Secondly, we propose an accurate system level Foster network thermal model. The parameters of the model are extracted from step responses of the CFD battery thermal model. The Foster network model and the CFD model give the same results. The Foster network can couple with battery circuit model to form an electric-thermal battery model for system simulation. Thirdly, IPM electric machines are important in high performance drive systems. During normal operations, irreversible demagnetization can occur due to temperature rise and various loading conditions. We investigate the performance of an IPM using 3d time stepping electromagnetic FEA considering magnet’s temperature dependency. Torque, flux linkage, induced voltage, inductance and saliency of the IPM will be studied in details. Finally, we use CFD to predict the non-uniform temperature distribution of the IPM machine and the impact of this distribution on motor performance. Fourthly, we will switch gear to investigate the IPM motor on the system level. A reduced order IPM model is proposed to consider the effect of demagnetization of permanent magnet due to temperature effect. The proposed model is validated by comparing its results to the FEA results. Finally, a HEV is a vehicle that has both conventional mechanical (i.e. internal combustion engine) and electrical propulsion systems. The electrical powertrain is used to work with the conventional powertrain to achieve higher fuel economy and lower emissions. v Computer based modeling and simulation techniques are therefore essential to help reduce the design cost and optimize system performance. Due to the complexity of hybrid vehicles, multidomain modeling ability is preferred for both component modeling and system simulation. We present a HEV library developed using VHDL-AMS.

The growing interest in more electrified vehicles has been pushing the industry and academia to pursue new and more accurate ways to estimate the xEV batteries State-of-Charge (SOC). The battery system still represents one of the many technical barriers that need to be eliminated or reduced to enable the proliferation of more xEV in the market, which in turn can help reduce CO2 emissions. Battery modelling and SOC estimation of Lithium-ion batteries (Li-ion) at a wide temperature range, including negative temperatures, has been a challenge for many engineers. For SOC estimation, several models configurations and approaches were developed and tested as results of this work, including different non-recurrent neural networks, such as Feedforward deep neural networks (FNN) and recurrent neural networks based on long short-term memory recurrent neural networks (LSTM-RNN). The approaches have considerably improved the accuracy presented in the previous state-of-the-art. They have expanded the application throughout five different Li-ion at a wide temperature range, achieving error as low as 0.66% Root Mean Square Error at -10⁰C using an FNN approach and 0.90% using LSTM-RNN. Therefore, the use of deep neural networks developed in this work can increase the potential for xEV application, especially where accuracy at negative temperatures is essential. For Li-ion modelling, a cell model using LSTM-RNN (LSTM-VM) was developed for the first time to estimate the battery cell terminal voltage and is compared against a gated recurrent unit (GRU-VM) approach and a Third-order Equivalent Circuit Model based on Thevenin theorem (ECM). The models were extensively compared for different Li-ion at a wide range of temperature conditions. The LSTM-VM has shown to be more accurate than the two other benchmarks, where could achieve 43 (mV) Root Mean Square Error at -20⁰C, a third when compared to the same situation using ECM. Although the difference between LSTM-VM and GRU-VM is not that steep. Finally, throughout the work, several methods to improve robustness, accuracy and training time have been introduced, including Transfer Learning applied to the development of SOC estimation models, showing great potential to reduce the amount of data necessary to train LSTM-RNN as well as improve its accuracy. / Thesis / Doctor of Philosophy (PhD) / For electric vehicle State-of-Charge estimation, several models configurations and approaches were developed and tested as results of this work, including different non-recurrent neural networks, such as Feedforward deep neural networks (FNN) and recurrent neural networks based on long short-term memory recurrent neural networks (LSTM-RNN). The approaches have considerably improved the accuracy presented in the previous state-of-the-art. They have expanded the application throughout five different Li-ion at a wide temperature range, achieving error as low as 0.66% Root Mean Square Error at -10⁰C using an FNN approach and 0.90% using LSTM-RNN. Therefore, the use of deep neural networks developed in this work can increase the potential for xEV application, especially where accuracy at negative temperatures is essential. For Li-ion modelling, a cell model using LSTM-RNN (LSTM-VM) was developed for the first time to estimate the battery cell terminal voltage and is compared against a gated recurrent unit (GRU-VM) approach and a Third-order Equivalent Circuit Model based on Thevenin theorem (ECM). The models were extensively compared for different Li-ion at a wide range of temperature conditions. The LSTM-VM has shown to be more accurate than the two other benchmarks, where could achieve 43 (mV) Root Mean Square Error at -20⁰C, a third when compared to the same situation using ECM. Although the difference between LSTM-VM and GRU-VM is not that steep.

Die Modellierung natürlicher und technischer Systeme spielt eine wichtige Rolle, um deren Verhalten zu simulieren und vorherzusagen. Die Impedanzspektroskopie ist in diesem Zusammenhang eine interessante Methode, da die Impedanz oft einfach messbar ist. Die herausfordernde Aufgabe ist die Interpretation gemessener Daten. Das Verständnis des Zusammenhanges zwischen realen Effekten und gemessener Impedanz anhand eines Impedanzmodells ist eine zentrale Problemstellung. Die Herleitung solcher Modelle wird in dieser Arbeit anhand drei verschiedenartiger Beispiele aus dem Gebiet der Messtechnik untersucht. Wirbelstromsensoren werden allgemein zur Messung von Abstand und Materialeigenschaften eingesetzt. Anhand eines Modells wird untersucht, wie diese Größen simultan bestimmbar sind. Die Messung der Zusammensetzung von Materialgemischen ist vielfach technisch relevant. Am Beispiel von Waschlaugen und Dispersionen mit Carbon Nano Tubes wird gezeigt, wie deren Zusammensetzung die Impedanz beeinflusst und welche Eigenschaften messbar sind. Batterien spielen eine wichtige Rolle zur Speicherung elektrischer Energie. Mit einem fraktionalen Differentialgleichungsmodell erfolgt eine Simulation der Batteriespannung unter wechselnden Einsatzbedingungen. Anhand der Anwendungen wird deutlich, dass es keinen Automatismus zur Modellerstellung und kein Modell für alles geben kann. Um so mehr liefert das vorgeschlagene Vorgehen einen Einstieg in die Modellerstellung. / Modeling natural and technical systems is important in order to simulate and predict their behavior. Impedance spectroscopy is an interesting method in the field of modeling because the impedance often is easily measurable. Nevertheless, interpretation of measured data is the challenging task in this field. The fundamental problem is understanding the relationship between real physical effects, measured impedance and impedance model. Fundamentals and advanced methods for deriving impedance models are investigated for three different problems in the field of measurement and sensor technology in this work. Eddy current sensors are commonly used to measure distance and material properties. Based on a model, it is investigated how these quantities can be determined simultaneously. Measuring the composition of material mixtures has many technical applications. Using the example of dispersions containing laundry detergents and dispersions with carbon nanotubes shows how their composition effects the impedance and measurable quantities. Batteries play an important role for storing electrical energy. Applying a fractional differential equation model allows a simulation of the battery voltage under varying operating conditions. Based on these applications it becomes clear, there can be no fully automated model creation method. A scientific analysis of the underlying problem is always required. The more the proposed approach provides an introduction to modeling.

Doctoral thesis deals with domain of electric energy storage. It seeks to define the methods of accumulation, which can be used in industrial applications and define the conditions for the use of storage systems in electric power systems with extended penetration of renewable energy sources. In the context of current developments in this field is analyzed detail one of the perspective storage systems - Vanadium Redox Battery (VRB). One of the outcomes of this work is economic and energy analysis of storage systems, which are conceived with a disproportion between production and consumption of energy. The work was supported by the Centre for Research and Utilization of Renewable Energy (CVVOZE) no. CZ.1.05/2.1.00/01.0014 and research project no. FEKT S-11-9.

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