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.

Batteries are becoming popular in the trend of electrification. Performance andlifespan of a battery pack are closely related to how it has been utilized. Withproper balancing control to slow down aging process, variances of capacity andresistance between battery cells can be maintained at a better level. Among balancing methods, dissipative balancing is still the most common method for itssimplicity in control, low cost and high speed. Non-dissipative balancing methods like converter-based and capacitor-based are of researchers’ interest becauseof less heat generated and superior efficiency. In this thesis, the converter-based balancing method is investigated. A modular multilevel converter (MMC) with Pulse-Width Modulation (PWM) pattern iscompared with another MMC with Nearest-Level Modulation (NLM). The speedto balance six battery sub-modules, output power and battery current harmonicsare examined.

In this paper, an algorithm for estimating lead-acid battery state of charge (SOC) is implemented. The algorithm, named “Improved Coulomb Counting Algorithm”, was developed within a master thesis project (M. M. Samolyk & J. Sobczak, “Development of an algorithm for estimating Lead-acid Battery State of Charge and State of Health”, M.S. thesis, Dept. Signal Processing, Blekinge Institute of Technology, Karlskrona, Sweden, 2013) with cooperation of a Swedish company – Micropower – Research and Development department.  Currently used method at Micropower is Coulomb Counting; implemented algorithm compares coulomb counting method with open circuit voltage method and uses current, terminal voltage and temperature measurements to finally produce improvement for the very same coulomb counting method and get a better estimation of SOC.  The algorithm was implemented on Micropower Access Battery Monitoring Unit (BMU) using C programming language, so that it can be tested in real time application of the regular battery operation. In the end specific gravity measurements were also presented to comparing the methods.

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. / 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.

No / The current global challenge of climate change has made renewable energy usage very important. There is an ongoing drive for the deployment of renewable energy resource at the domestic level through feed-in tariff, etc. However the intermittent nature of renewable energy has made storage a key priority. In this work, a community having a solar farm with energy storage embedded in the house of the energy consumers is considered. Consumers within the community are aggregated in to a local virtual power plant. Genetic algorithm was used to develop an optimized energy transaction for the virtual power plant. The results shows that it is feasible to have a virtual power plant setup in a local community that involve the use of renewable generation and embedded storage. The result also show that when maximization of battery state of charge is considered as part of an optimization problem in a day ahead market, certain trade-off would have to be made on the profit of the virtual power plant, the incentive of the prosumer, as well as the provision of peak service to the grid.

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<p>Lithium-ion (Li-ion) cells are increasingly used in many applications affecting our</p> <p>daily life, such as laptops computers, cell phones, digital cameras, and other portable</p> <p>electronic devices. Lithium-ion batteries are increasingly being considered for their use in Electric Vehicles (EV), Hybrid Electric Vehicles (HEV) and Plug-in Hybrid Electric Vehicles (PHEV) due to their high energy density, slow loss of charge when not in use, and for lack of hysteresis effect. New application domains for these batteries has placed greater emphasis on their energy management, monitoring and control strategies.</p> <p>In this thesis, a comparative study between different models and state of charge (SOC) estimation strategies is performed. Battery models range from black-box representation to detailed electrochemical reaction models that consider the underlying physics. The state of charge is estimated using the Extended Kalman filter (EKF) and the Smooth Variable Structure Filter (SVSF). The models and SOC estimation strategies are applied to experimental results from BMW Electrical and Hybrid Research and Development center and validated using a simulation model from AVL CRUISE software.</p> <p>Overall, different models and SOC estimation scenarios were studied. An average improvement of 30% in the estimation accuracy was shown by the SVSF SOC method when compared with the EKF SOC strategy. In general, the SVSF SOC estimation technique demonstrates excellent capability and a fast speed of convergence.</p> / Master of Applied Science (MASc)

The accurate and reliable estimation of the State-of-Charge (SOC) and State-of-Health (SOH) of Li-ion batteries is paramount to the safe and reliable operation of any electrified vehicle. Not only is accuracy and reliability necessary, but these estimation techniques must also be practical and intelligent since their use in real world applications can include noisy input signals, varying ambient conditions and incomplete or partial sequences of measured battery data. To that end, a novel framework, utilizing deep learning techniques, is considered whereby battery modelling and state estimation are performed in a single unified step. For SOC estimation, two different deep learning techniques are used with experimental data. These include a Recurrent Neural Network with Long Short-Term Memory (LSTM-RNN) and a Deep Feedforward Neural Network (DNN); each one possessing its own set of advantages. The LSTM-RNN achieves a Mean Absolute Error (MAE) of 0.57% over a fixed ambient temperature and a MAE of 1.61% over a dataset with ambient temperatures increasing from 10°C to 25°C. The DNN algorithm, on the other hand, achieves a MAE of 1.10% over a 25°C dataset while, at -20°C, a MAE of 2.17% is obtained. A Convolutional Neural Network (CNN), which has the advantage of shared weights, is used with randomized battery usage data to map raw battery measurements directly to an estimated SOH value. Using this strategy, average errors of below 1% are obtained when using fixed reference charge profiles. To further increase the practicality of this algorithm, the CNN is trained and validated over partial reference charge curves. SOH is estimated with a partial reference profile with the SOC ranging from 60% to 95% and achieves a MAE of 0.81%. A smaller SOC range is then used where the partial charge profile spans a SOC of 85% to 95% and a MAE of 1.60% is obtained. Finally, a fused convolutional recurrent neural network (CNN-RNN) is used to perform combined SOC and SOH estimation over constant charge profiles. This is performed by feeding the estimated SOH from the CNN into a LSTM-RNN, which, in turn, estimates SOC with a MAE of less than 0.5% over the lifetime of the battery. / Thesis / Doctor of Philosophy (PhD)