Recently, lithium-ion batteries (LIBs) have achieved wide acceptance for various energy storage applications, such as electric vehicles (EVs) and smart grids. As a vital component in EVs, the performance of lithium-ion batteries in the last few decades has made significant progress. The development of a robust battery management system (BMS) has become a necessity to ensure the reliability and safety of battery packs. In addition, state of charge (SOC) estimation and thermal models with high-fidelity are essential to ensure efficient BMS performance. The SOC of a LIB is an essential factor that should be reported to the vehicle’s electronic control unit and the driver. Inaccurate reported SOC impacts the reliability and safety of the lithium-ion battery packs (LIBP) and the vehicle. Different algorithms are used to estimate the SOC of a LIBP, including measurement-based, adaptive filters and observers, and data-driven; however, there is a gap in feasibility studies of running these algorithms for multi-cell LIBP on BMS microprocessors. On the other hand, temperature sensors are utilized to monitor the temperature of the cells in LIBPs. Using a temperature sensor for every cell is often impractical due to cost and wiring complexity. Robust temperature estimation models can replace physical sensors and help the fault detection algorithms by providing a redundant monitoring system. In this thesis, an accurate SOC estimation and thermal modeling for lithium-ion batteries (LIBs) are presented using deep neural networks (DNNs). Firstly, two DNN-based SOC estimation algorithms, including a feedforward neural network (FNN) enhanced with external filters and a recurrent neural network with a long short-term memory layer (LSTM), are developed and benchmarked versus an extended Kalman filter (EKF) and EKF with recursive least squares filter (EKF-RLS) SOC estimation algorithms. The execution time of EKF, EKF-RLS, FNN, and LSTM SOC estimation algorithms with similar accuracy was found to be 0.24 ms, 0.25 ms, 0.14 ms, and 0.71 ms, respectively. The DNN SOC estimation algorithms were also demonstrated to have lower RAM use than the EKFs, with less than 1 kB RAM required to run one estimator. The proposed FNN and LSTM models are also used to predict the surface temperature of different lithium-ion cells. These DNN models are shown to be capable of estimating temperature with less than 2 ⁰C root mean square error for challenging low ambient temperature drive cycles and just 0.3 ⁰C for 4C rate fast charging conditions. In addition, a DNN model which is trained to estimate the temperature of a new battery cell, is found to still have a very low error of just 0.8 ⁰C when tested on an aged cell. Finally, an integrated physics, and neural network-based battery pack thermal model (LP+FNN) is developed and used to detect and identify different thermal faults of a LIBP. The proposed fault detection and identification method is validated using various thermal faults, including fan system failure, airflow lower and higher than setpoint, airflow blockage of submodule and temperature sensor reading faults. The proposed method is able to detect different cooling system faults within 10 to 35 minutes after fault occurrence. In addition, the proposed method demonstrated being capable of detecting temperature sensor reading offset and scale faults of ±3 ⁰C and ±0.15% or more, respectively with 100% accuracy. / Thesis / Doctor of Philosophy (PhD)

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This work reports the experimental and numerical study of the thermal management of Li-ion battery packs under the context of electric vehicle (EV) or hybrid EV (HEV) applications. Li-ion batteries have been extensively demonstrated as an important power source for EVs or HEVs. However, thermal management is a critical challenge for their widespread deployment, due to their highly dynamic operation and the wide range of environments under which they operate. To address these challenges, this work developed several experimental platforms to study adaptive thermal management strategies. Parallel to the experimental effort, multi-disciplinary models integrating heat transfer, fluid mechanics, and electro-thermal dynamics have been developed and validated, including detailed CFD models and lumped parameter models. The major contributions are twofold. First, this work developed actively controlled strategies and experimentally demonstrated their effectiveness on a practical sized battery pack and dynamic thermal loads. The results show that these strategies effectively reduced both the parasitic energy consumption and the temperature non-uniformity while maintaining the maximum temperature rise in the pack. Second, this work established a new two dimensional lumped parameter thermal model to overcome the limitations of existing thermal models and extend their applicable range. This new model provides accurate surface and core temperatures simulations comparable to detailed CFD models with a fraction of the computational cost. / Ph. D.

Among many smart grid technologies, demand response (DR) is gaining increasing popularity. Many utility companies provide a variety of programs to encourage DR participation. Under these circumstances, various building energy management (BEM) systems have emerged to facilitate the building control during a DR event. Nonetheless, due to the cost and return on investment, these solutions mainly target homes and large commercial buildings, leaving aside small- and medium-sized commercial buildings (SMCB). SMCB, however, accounts for 90% of commercial buildings in the US, and offer great potential of load reduction during peak hours. With the advent of Internet-of-Things (IoT) devices and technologies, low cost smart building solutions have become possible for the SMCB; nonetheless, related intelligent algorithms are not widely available. This dissertation work investigates automated building control algorithms, tailored for the SMCB, to realize automatic device control during DR events. To be specific, a control framework for Air-Conditioning (AC) units' coordination is proposed. The goal of such framework is to reduce the aggregated AC power consumption while maintaining the thermal comfort inside a building during DR events. To achieve this goal, three major components of the framework were studied: building thermal property modeling, AC power consumption modeling and control algorithms design. Firstly, to consider occupants' thermal comfort, a reverse thermal model was designed to predict the indoor temperature of thermal zones under different AC control signals. The model was trained with supervised learning using coarse-grained temperature data recorded by smart thermostats; thus, it requires no lengthy configuration as a forward model does. The cost efficiency and plug-and-play feature of the model make it appropriate for SMCB. Secondly, a power disaggregation algorithm is proposed to model the power-outdoor temperature relationship of multiple AC units, using data from a single power meter and thermostats. Finally, algorithms based on mixed integer linear programming (MILP) and reinforcement learning (RL) were devised to coordinate multiple AC units in a building during a DR event. Integrated with the thermal model and AC power consumption model, these algorithms minimize occupants' thermal discomfort while restricting the aggregated AC power consumption below the DR limit. The efficiency of these control algorithms was tested, which demonstrate that they can generate AC control schedule in short notice (5 minutes) ahead of a DR event. Verification and validation of the proposed framework was conducted in both simulation and actual building environments. In addition, though the framework is designed for SMCBs, it can also be applied to large homes with multiple AC units to coordinate. This work is expected to give an insight into the BEM sector, helping the popularization of implementing DR in buildings. The research findings from this dissertation work shows the validity of the proposed algorithms, which can be used in BEM systems and cloud-based smart thermostats to exploit the untapped DR resource in SMCB. / PHD / For power system operation, the demand and supply should be equal at all time. During peak hours, the demand becomes very high. One way to keep the balance is to provide more generation capacity, and thus more expensive and less efficient generators are brought online, which causes higher production cost and more pollution. Instead, an alternative is to encourage the load reduction via demand response (DR): customers reduce load upon receiving a signal sent by the utility company, usually in exchange for some monetary payback. For buildings to participate in DR, an affordable automation system and related control algorithms are needed. This dissertation proposed a cost-effective, self-learning and data-driven framework to facilitate small- and medium-sized commercial buildings or large homes in air-conditioner (AC) units control during DR events. The devised framework requires little human configuration; it learns the building behavior by analyzing the operation data. Two algorithms are proposed to coordinate multiple AC units in a building with two goals: firstly, reducing the total AC power consumption below certain limit, as agreed between the building owners and their utility company. Secondly, minimizing occupants’ thermal discomfort caused by limiting AC operation. The effectiveness of the framework is investigated in this dissertation based on data collected from a real building.

Doctor of Philosophy / Department of Mechanical and Nuclear Engineering / Steven J. Eckels / The research presents the investigation of personal cooling systems (PCS) and their effects on humans from a thermodynamic perspective. The original focus of this study was to determine the most appropriate PCS for dismounted U.S. Army soldiers in a desert environment. Soldiers were experiencing heat stress due to a combination of interrelated factors including: environmental variables, activity levels, and clothing/personal protective equipment (PPE), which contributed to the buildup of thermal energy in the body, resulting in heat stress. This is also a common problem in industry, recreation, and sports. A PCS can serve as a technological solution to mitigate the effects of heat stress when other solutions are not possible. Viable PCS were selected from the KSU PCS database, expanded to over 300 PCS in the course of this study. A cooling effectiveness score was developed incorporating the logistical burdens of a PCS. Fourteen different PCS configurations were tested according to ASTM F2370 on a sweating thermal manikin. Four top systems were chosen for ASTM F2300 human subject testing on 22 male and 2 female soldiers in simulated desert conditions: dry air temperature = 42.2 ºC, mean radiant temperature = 54.4 ºC, air velocity = 2.0 m/s, relative humidity = 20%. Subjects wore military body armor, helmets and battle dress uniforms walking on treadmills at a metabolic rate of approximately 375-400W. All the PCS conditions showed significant reductions in core temperature rise, heart rate, and total sweat produced compared to the baseline (p<0.05). The expected mean body temperature was higher in the human subjects than expected based on the cooling obtained from the sweating manikin test. Lowered sweat production was determined to be the likely cause, reducing the body’s natural heat dissipation. The ASHRAE two-node model and TAITherm commercial human thermal models were used to investigate this theory. A method to account for fabric saturation from dripping sweat was developed and is presented as part of a new model. This study highlights that the response of the human body is highly complex in high-activity, high-temperature environments. The modeling efforts show the PCS moved the body from uncompensable to compensable heat stress and the body also reduced sweating rates when the PCS was used. Most models assume constant sweating (or natural heat loss) thus the PCS sweat reduction is the likely cause of the higher than expected core temperatures, and is an important aspect when determining the purpose of a PCS.

Doctor of Philosophy / Department of Mechanical and Nuclear Engineering / Steven J. Eckels / The research presents the investigation of personal cooling systems (PCS) and their effects on humans from a thermodynamic perspective. The original focus of this study was to determine the most appropriate PCS for dismounted U.S. Army soldiers in a desert environment. Soldiers were experiencing heat stress due to a combination of interrelated factors including: environmental variables, activity levels, and clothing/personal protective equipment (PPE), which contributed to the buildup of thermal energy in the body, resulting in heat stress. This is also a common problem in industry, recreation, and sports. A PCS can serve as a technological solution to mitigate the effects of heat stress when other solutions are not possible. Viable PCS were selected from the KSU PCS database, expanded to over 300 PCS in the course of this study. A cooling effectiveness score was developed incorporating the logistical burdens of a PCS. Fourteen different PCS configurations were tested according to ASTM F2370 on a sweating thermal manikin. Four top systems were chosen for ASTM F2300 human subject testing on 22 male and 2 female soldiers in simulated desert conditions: dry air temperature = 42.2 ºC, mean radiant temperature = 54.4 ºC, air velocity = 2.0 m/s, relative humidity = 20%. Subjects wore military body armor, helmets and battle dress uniforms walking on treadmills at a metabolic rate of approximately 375-400W. All the PCS conditions showed significant reductions in core temperature rise, heart rate, and total sweat produced compared to the baseline (p<0.05). The expected mean body temperature was higher in the human subjects than expected based on the cooling obtained from the sweating manikin test. Lowered sweat production was determined to be the likely cause, reducing the body’s natural heat dissipation. The ASHRAE two-node model and TAITherm commercial human thermal models were used to investigate this theory. A method to account for fabric saturation from dripping sweat was developed and is presented as part of a new model. This study highlights that the response of the human body is highly complex in high-activity, high-temperature environments. The modeling efforts show the PCS moved the body from uncompensable to compensable heat stress and the body also reduced sweating rates when the PCS was used. Most models assume constant sweating (or natural heat loss) thus the PCS sweat reduction is the likely cause of the higher than expected core temperatures, and is an important aspect when determining the purpose of a PCS.

Human thermal environments constitute complex combinations of various interacting thermal factors. The transient and non-uniform nature of those thermal factors further increases the complexity of the thermal comfort problem. The conventional approach to the thermal comfort problem has been simplifying the problem and providing steady thermal environments which would satisfy the majority of the people in a given space. However, several problems emerged with this approach. People became finely tuned to the narrow range of conditions and developed expectations for the same conditions which made them uncomfortable when there were slight deviations from those conditions. Also, the steady approach didn't solve the comfort problem because, in practice, people move between spaces, and thermal conditions such as metabolic rate, surface temperatures, airflow speed and direction vary in a typical day. A human subject test was designed to determine the transient relationship between the people and their environments. In the first part, thermal perceptions of people were taken during various metabolic rate conditions. In the second and the third parts, transient conditions of different thermal factors were created. Various combinations of airflow frequencies, airflow location around the body, metabolic rate, and room temperatures were tested for their individual and interaction effects of providing thermal comfort. The concept of Localized Dynamic Airflow was proposed in which room airflow was simply redirected to different parts of the body with a varying airflow speed. Results showed that males and females respond differently to the thermal conditions. The room temperatures they found neutral were significantly different. People‟s thermal comfort during transient metabolic conditions was similar to high metabolic conditions. This heightened response extended into the next ten minutes after the high metabolic conditions ended. Test results suggested that people tolerate higher temperatures during transient environmental conditions. The average response was for comfortable even during the high temperature (83°F) and high metabolic rate (4 met) conditions. Low energy use of the localized dynamic airflow and the increased room temperatures has significant potential for monetary savings.

abstract: Current hybrid vehicle and/or Fuel Cell Vehicle (FCV) use both FC and an electric system. The sequence of the electric power train with the FC system is intended to achieve both better fuel economies than the conventional vehicles and higher performance. Current hybrids use regenerative braking technology, which converts the vehicles kinetic energy into electric energy instead of wasting it. A hybrid vehicle is much more fuel efficient than conventional Internal Combustion (IC) engine and has less environmental impact The new hybrid vehicle technology with it's advanced with configurations (i.e. Mechanical intricacy, advanced driving modes etc) inflict an intrusion with the existing Thermal Management System (TMS) of the conventional vehicles. This leaves for the opportunity for now thermal management issues which needed to be addressed. Till date, there has not been complete literature on thermal management issued of FC vehicles. The primary focus of this dissertation is on providing better cooling strategy for the advanced power trains. One of the cooling strategies discussed here is the thermo-electric modules. The 3D Thermal modeling of the FC stack utilizes a Finite Differencing heat approach method augmented with empirical boundary conditions is employed to develop 3D thermal model for the integration of thermoelectric modules with Proton Exchange Membrane fuel cell stack. Hardware-in-Loop was designed under pre-defined drive cycle to obtain fuel cell performance parameters along with anode and cathode gas flow-rates and surface temperatures. The FC model, combined experimental and finite differencing nodal net work simulation modeling approach which implemented heat generation across the stack to depict the chemical composition process. The structural and temporal temperature contours obtained from this model are in compliance with the actual recordings obtained from the infrared detector and thermocouples. The Thermography detectors were set-up through dual band thermography to neutralize the emissivity and to give several dynamic ranges to achieve accurate temperature measurements. The thermocouples network was installed to provide a reference signal. The model is harmonized with thermo-electric modules with a modeling strategy, which enables optimize better temporal profile across the stack. This study presents the improvement of a 3D thermal model for proton exchange membrane fuel cell stack along with the interfaced thermo-electric module. The model provided a virtual environment using a model-based design approach to assist the design engineers to manipulate the design correction earlier in the process and eliminate the need for costly and time consuming prototypes. / Dissertation/Thesis / Masters Thesis Technology 2014