Control of Lithium-Ion Battery Warm-up from Sub-zero Temperatures

The archetype of rechargeable technology, Li-ion batteries have over the last decade benefited from improvements in material science through increased energy and power density. Although widely adopted, these batteries suffer from significant performance degradation at low temperatures, posing a challenge for automotive applications, especially during vehicle start-up. This begs the question: if one was to seek an energy optimal warm-up strategy, how would it look? Moreover, if as much as 22% of reduction in range of electric vehicles is attributable to onboard battery heating systems, would an optimal heating strategy alleviate this energy drain and at what drawback? This thesis addresses these questions. To that end, we pose and solve two energy-optimal warm-up strategies in addition to developing tools that will enable one to make prudent decisions on whether warm-up is feasible if the battery energy state falls too low. In this dissertation, we address the four main aspects of control design modeling, control, verification and adaptation. There are two primary control strategies that are designed in this dissertation and tools to analyze them are developed. The first warm-up scenario involves a receding horizon optimal control problem whose objective trades-offs increase in battery's temperature by self-heating against energy expended. The shape of battery current is restricted to be bi-directional pulses that charge and discharge the cell at relatively high frequencies via an external capacitor. The optimal control problem solves for the amplitude of the pulse train and the results clarify issues associated with capacitor size, time and lost energy stored. The second control policy is deduced by solving an optimal discharge control problem for the trajectory of power that could self-heat the cell and at the same time feed an external heater whilst minimizing the loss in state of charge. Batteries inevitably age as they are used and consequently their dynamics also change. Since both proposed methods are model based, the last of part of this dissertation proposes a novel augmented-state-space partitioning technique which can be used to design cascaded nonlinear estimators. Using this partitioning technique, the relative average estimability of the different states of the electrical and thermal model is studied and Dual Extended Kalman Filters are built and validated in simulations. All the methods developed are demonstrated via a combination of simulation and experiments on Iron Phosphate or Nickel Manganese Cobalt Li-ion battery cell which have high power capability and could be used in replacement of 12V starter batteries or 48V start-stop applications.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/136964/1/elemsn_1.pd

Improved Battery State Estimation Using Novel Sensing Techniques.

Lithium-ion batteries have been considered a great complement or substitute for gasoline engines due to their high energy and power density capabilities among other advantages. However, these types of energy storage devices are still yet not widespread, mainly because of their relatively high cost and safety issues, especially at elevated temperatures. This thesis extends existing methods of estimating critical battery states using model-based techniques augmented by real-time measurements from novel temperature and force sensors. Typically, temperature sensors are located near the edge of the battery, and away from the hottest core cell regions, which leads to slower response times and increased errors in the prediction of core temperatures. New sensor technology allows for flexible sensor placement at the cell surface between cells in a pack. This raises questions about the optimal locations of these sensors for best observability and temperature estimation. Using a validated model, which is developed and verified using experiments in laboratory fixtures that replicate vehicle pack conditions, it is shown that optimal sensor placement can lead to better and faster temperature estimation. Another equally important state is the state of health or the capacity fading of the cell. This thesis introduces a novel method of using force measurements for capacity fade estimation. Monitoring capacity is important for defining the range of electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs). Current capacity estimation techniques require a full discharge to monitor capacity. The proposed method can complement or replace current methods because it only requires a shallow discharge, which is especially useful in EVs and PHEVs. Using the accurate state estimation accomplished earlier, a method for downsizing a battery pack is shown to effectively reduce the number of cells in a pack without compromising safety. The influence on the battery performance (e.g. temperature, utilization, capacity fade, and cost) while downsizing and shifting the nominal operating SOC is demonstrated via simulations. The contributions in this thesis aim to make EVs, HEVs and PHEVs less costly while maintaining safety and reliability as more people are transitioning towards more environmentally friendly means of transportation.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120815/1/nassimab_1.pd

Electrode-Specific Degradation Diagnostics for Lithium-Ion Batteries with Practical Considerations

Li-ion batteries inevitably degrade with cyclic usage and storage time. Close to end-of-life batteries can no longer meet their performance requirements and the likelihood of occurring catastrophic failures increases. Thus, an accurate diagnosis of their state of health over long-term use has become a critical function for reliable and safe battery management systems, especially for vehicle electrification and large scale energy storage systems. Degradation of batteries is typically quantified at the cell level with capacity loss and power fade, however different usage conditions and environmental factors can contribute to the degradation of batteries differently. Therefore, typical cell-level lumped degradation metrics are not sufficient to give a full explanation of battery state of health. This dissertation presents approaches for the diagnosis of electrode-specific degradation of Li-ion batteries considering a variety of practical aspects in real-world applications such as the half-cell potential change, the partial data availability, data acquisition method, and practical charging rate. The electrode-specific degradation diagnosis is performed by model-based identification of the individual electrode state-of-health (eSOH) parameters, electrode capacity and utilization range. The advancements contributed by this dissertation are summarized as follows. First, a novel diagnostic algorithm is proposed by combining the terminal voltage fitting process with the peak alignment method to improve electrode parameter estimation confidence. The proposed method addresses the half-cell potential change of the positive electrode due to the chemical aging of the metal oxide. The diagnostic result is experimentally verified with large-format prismatic commercial cells. The second practical consideration is partial data availability. In practice, the full range of OCV measurement is not obtainable without the designated offline diagnostic test. With the limited data, the accuracy of parameter estimation becomes questionable. Therefore, the achievable estimation error bound is analyzed with respect to partial data windows through the Cramer-Rao Bound and confidence interval. The result shows that the eSOH estimation improves when a data window includes slope changes of electrode half-cell potential. This fundamental limitation is applied in data-driven approach to provide data-requirements for machine learning of battery cycle life prediction. Third, continued from the partial window idea, a time-optimal current profile is proposed to enable direct measurement of pseudo-OCV data for the desired range without a long relaxation period. By allowing bi-directional charging, the proposed time-optimal control problem identifies a proper sequence of charge/discharge pulses and successfully reduces total data acquisition time by more than 60% in both simulation and experiment, showing a possible way to implement the developed OCV-based electrode degradation diagnostic algorithm. Fourth, the feasibility of the electrode-specific degradation diagnostics is studied for real-world charging conditions where the typical charging current rate is usually higher (e.g. C/5) than C/20 of pseudo-OCV data. With increasing charging rates, the individual electrode's electrochemical features is obscured, and the overpotential due to internal resistance needs to be estimated concurrently, making the eSOH estimation challenging. An adaptive algorithm with a data selection strategy is proposed to deal with the estimation of both resistance and electrode SOH parameters. Lastly, the potential of the physics-guided machine learning approaches is explored with two case studies for Li-ion battery degradation diagnostics and prognostics.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168114/1/suhaklee_1.pd

Advanced Diagnostics for Lithium-ion Batteries: Decoding the Information in Electrode Swelling

Lithium-ion batteries exhibit mechanical expansion and contraction during cycling, consisting of a reversible intercalation-induced expansion and an irreversible expansion as the battery ages. Prior experimental studies have shown that mechanical expansion contains valuable information that correlates strongly with cell aging. However, a number of fundamental questions remain on the usability of the expansion measurement in practice. For example, it is necessary to determine whether the expansion measurements provide information that can help the estimation of the electrode state of health (eSOH), given limits on data availability and sensor noise in the field. Furthermore, the viability of using expansion for cell diagnostics also needs more investigation considering the broad range of aging conditions in real-world applications. This dissertation focuses on the experimental and modeling study of the expansion measurements during aging in order to assess its ability in helping battery diagnostics. To this end, mechanistic voltage and expansion models based on the underlying physics of phase transitions are developed. For the first time, the identifiability of eSOH parameters is explored by incorporating the expansion/force measurement. It is shown that the expansion measurements enhance the estimation of eSOH parameters, especially with a limited data window, since it has a better signal-to-noise ratio compared to the voltage. Moreover, the increased identifiability is closely related to the phase transitions in the electrodes. A long-term experimental aging study of the expansion of the graphite/NMC pouch cells is conducted under a variety of conditions such as temperature, charging rate, and depth of discharge. The goals here are to validate the results of the identifiability analysis and record the reversible and irreversible expansion correlated with capacity loss for informing the electrochemical models. Firstly, the advantages of the expansion concerning the eSOH identifiability are confirmed. Secondly, the results of the expansion evolution reveal that the features in the reversible expansion are an excellent indicator of health and, specifically, capacity retention. The expansion feature is robust to charge conditions. Namely, it is mostly insensitive to the hysteresis effects of the various initial state of charge, and it is detectable at higher C-rates up to 1C. Additionally, the expansion feature occurs near the half-charged point and therefore diagnostics can be performed more often during naturalistic use cases. Thus, the expansion measurement facilitates more frequent capacity checks in the field. Furthermore, an electrochemical and expansion model suitable for model-based estimation is developed. In particular, a multi-particle modeling approach for the graphite electrode is considered. It is demonstrated that the new model is able to capture the peak smoothing effect observed in the differential voltage at higher C-rates above C/2. Model parameters are identified using experimental data from the graphite/NMC pouch cell. The proposed model produces an excellent fit for the observed electric and mechanical swelling response of the cells and could enable physics-based data-driven degradation studies at practical charging rates. Finally, a fast-charging method based on the constant current constant voltage (CC-CV) charging scheme, called CC-CVησT (VEST), is developed. The new approach is simpler to implement and can be used with any model to impose varying levels of constraints on variables pertinent to degradation, such as plating potential and mechanical stress. The capabilities of the new CC-CVησT charging are demonstrated using the physics-based model developed in this dissertation.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169953/1/pmohtat_1.pd

Optimal Power Management Based on Q-Learning and Neuro-Dynamic Programming for Plug-in Hybrid Electric Vehicles

Energy optimization for plug-in hybrid electric vehicles (PHEVs) is a challenging problem due to its system complexity and various constraints. In this research, we present a Q-learning based in-vehicle model-free solution that can robustly converge to the optimal control. The proposed algorithms combine neuro-dynamic programming (NDP) with future trip information to effectively estimate the expected future energy cost (expected cost-to-go) for a given vehicle state and control actions. The convergence of those learning algorithms is demonstrated on both fixed and randomly selected drive cycles. Based on the characteristics of these learning algorithms, we propose a two-stage deployment solution for PHEV power management applications. We will also introduce a new initialization strategy that combines optimal learning with a properly selected penalty function. Such initialization can reduce the learning convergence time by 70%, which has huge impact on in-vehicle implementation. Finally, we develop a neural network (NN) for the battery state-of-charge (SoC) prediction, rendering our power management controller completely model-free.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/140754/1/Chang Liu Final Dissertation.pdfDescription of Chang Liu Final Dissertation.pdf : Dissertatio

Advances in Sensors, Big Data and Machine Learning in Intelligent Animal Farming

Animal production (e.g., milk, meat, and eggs) provides valuable protein production for human beings and animals. However, animal production is facing several challenges worldwide such as environmental impacts and animal welfare/health concerns. In animal farming operations, accurate and efficient monitoring of animal information and behavior can help analyze the health and welfare status of animals and identify sick or abnormal individuals at an early stage to reduce economic losses and protect animal welfare. In recent years, there has been growing interest in animal welfare. At present, sensors, big data, machine learning, and artificial intelligence are used to improve management efficiency, reduce production costs, and enhance animal welfare. Although these technologies still have challenges and limitations, the application and exploration of these technologies in animal farms will greatly promote the intelligent management of farms. Therefore, this Special Issue will collect original papers with novel contributions based on technologies such as sensors, big data, machine learning, and artificial intelligence to study animal behavior monitoring and recognition, environmental monitoring, health evaluation, etc., to promote intelligent and accurate animal farm management

Space Station Systems: a Bibliography with Indexes (Supplement 8)

This bibliography lists 950 reports, articles, and other documents introduced into the NASA scientific and technical information system between July 1, 1989 and December 31, 1989. Its purpose is to provide helpful information to researchers, designers and managers engaged in Space Station technology development and mission design. Coverage includes documents that define major systems and subsystems related to structures and dynamic control, electronics and power supplies, propulsion, and payload integration. In addition, orbital construction methods, servicing and support requirements, procedures and operations, and missions for the current and future Space Station are included

A complex systems approach to education in Switzerland

The insights gained from the study of complex systems in biological, social, and engineered systems enables us not only to observe and understand, but also to actively design systems which will be capable of successfully coping with complex and dynamically changing situations. The methods and mindset required for this approach have been applied to educational systems with their diverse levels of scale and complexity. Based on the general case made by Yaneer Bar-Yam, this paper applies the complex systems approach to the educational system in Switzerland. It confirms that the complex systems approach is valid. Indeed, many recommendations made for the general case have already been implemented in the Swiss education system. To address existing problems and difficulties, further steps are recommended. This paper contributes to the further establishment complex systems approach by shedding light on an area which concerns us all, which is a frequent topic of discussion and dispute among politicians and the public, where billions of dollars have been spent without achieving the desired results, and where it is difficult to directly derive consequences from actions taken. The analysis of the education system's different levels, their complexity and scale will clarify how such a dynamic system should be approached, and how it can be guided towards the desired performance