Power management strategies have impacts on fuel economy, greenhouse gasses (GHG) emission, as well as effects on the durability of power-train components. This is why different off-line and real-time optimal control approaches are being developed. However, real-time control seems to be more attractive than off-line control because it can be directly implemented for managing power and energy flow inside an actual vehicle. One interesting illustration of these power management strategies is the model predictive control (MPC) based algorithm. Inside an MPC, a cost function is optimized while system constraints are validated in real time. The MPC algorithm relies on dynamic models of the vehicle and the battery. The complexity and accuracy of the battery model are usually neglected to benefit the development of new cost functions or better MPC search algorithms. In fact, the literature does not deal with the impact of the battery model on MPC. This is why this Ph.D. dissertation evaluates the impact of different battery models of a plug-in hybrid electric vehicle (PHEV) through a sensitivity analysis to reach optimal performance for an MPC. The required fidelity of the battery might depend on different factors:the prediction horizon also called look-ahead step time the vehicle states update time the vehicle model step time the objective function The results of simulations show that higher fidelity model improves the capability to predict accurately the battery aging. As the battery pack is currently one of the most expensive components of an electric vehicle and lithium is a limited natural resource, being able to manage precisely the battery aging is a crucial point for both the automotive company and the battery manufacturer. Another important aspect highlighted by this PhD dissertation is that higher battery fidelity model reduces the possibility to violate the SoC constraint, which is greatly desirable. In fact, this constraint is usually defined by battery manufacturers for safety and battery aging management reasons. Last but not least, it has been proven that the impact of the battery modeling for the MPC controller depends on what the objective function aims to optimize. For instance, battery modeling have limited impact if the objective function takes into account the fuel consumption but far more for if it considers the battery degradation.

Lithium-ion batteries are a rapidly growing power source for mobile applications such as electric vehicles. A battery model algorithm that estimates and predicts important battery parameters like terminal voltage and state-of-charge is necessary to maintain safe operation during discharge. Hence, a semi-empirical electrochemical-based model was proposed and implemented in MATLAB for discharge simulation and parameter estimation. This thesis also investigated several essential factors like internal resistance and operational temperature, which impact a battery cell during discharge.  The proposed model was a modification of Shepherd’s model that included both kinetic and diffusive components representing the total battery overpotential and a temperature- dependent coefficient. These were used for the determination of the battery’s internal resistance and the temperature effect. The model accounts for all dynamic characteristics of a Li-ion battery, including non-linear open-circuit voltage, internal resistance, discharge current, and capacity.  Model validation was performed using test profiles, including data provided by the battery manufacturer and experimental data for a test profile provided by Saab Dynamics. The simulated profiles were found to match the measured profiles. Although, some deviations occurred, especially during rapid changes in C-rates. The proposed model in this work shows that the simulation results compared to the experimental data had deviations within ~2% for the constant current discharge test, and the dynamic model managed to cover the experimental discharge voltage during different temperatures with good consistency and minor errors. Therefore, the proposed model can compete with other battery modeling methods.

The field of energy storage has improved drastically within the last two decades. Batteries of various chemistries have been relied on to provide energy for numerous portable electronic devices. Lithium-ion cells, when compared to other chemistries have been known to provide outstanding energy-to-weight ratios and exhibit low self-discharge when not in use [1]. The aforementioned benefits in conjunction with decreasing costs have made lithium-ion cells an exceptional choice for use in electrical vehicles (EVs). Battery Management Systems (BMS) in EVs are responsible for providing estimates for values that are indicative of the battery pack’s present operating condition. The current operating condition could be described by State of Charge, power fade, capacity fade and various other parameters [2]. Importantly, it is essential for the estimation technique to adjust to fluctuating cell characteristics as the cell ages, in pursuance of having available accurate estimates for the life time of the pack. In order for the estimation technique to properly estimate the desired quantities, a mathematical model capable of capturing cell dynamics is desired. There are various proposed methods recommended for mathematically modeling a cell, namely equivalent Circuit modeling, electro-chemical modeling and impedance spectroscopy. Consequently, in order to ensure mathematical models are accurate and further to have the ability to compare the proposed models, it is essential to have available data gathered from a given cell at specific operating conditions. This Master’s thesis outlines the development of a lithium-ion cell tester that is capable of controlling, monitoring and recording parameters such as current, voltage and temperature. The Dual capability of obtaining data from standardized cell tests as well as high frequency cell tests is fascinating and intriguing. As this capability holds the possibility of reducing cost otherwise spent on man hours and equipment which are both paramount in any industrially automated process. / Thesis / Master of Applied Science (MASc)

No description available.

Energy-storing electrochemical batteries are the most critical components of high energy density storage systems for stationary and mobile applications. Lithium-ion batteries have received considerable interest for hybrid electric vehicles (HEV) because of their high specific energy, but face inherent thermal management challenges that have not been adequately addressed. In the present investigation, a fully coupled electrochemical and thermal model for lithium-ion batteries is developed to investigate the impact of different thermal management strategies on battery performance. This work represents the first ever study of these coupled electrochemical-thermal phenomena in batteries from the electrochemical heat generation all the way to the dynamic heat removal in actual HEV drive cycles. In contrast to previous modeling efforts focused either exclusively on particle electrochemistry on the one hand or overall vehicle simulations on the other, the present work predicts local electrochemical reaction rates using temperature-dependent data on commercially available batteries designed for high rates (C/LiFePO4) in a computationally efficient manner. Simulation results show that conventional external cooling systems for these batteries, which have a low composite thermal conductivity (~1 W/m-K), cause either large temperature rises or internal temperature gradients. Thus, a novel, passive internal cooling system that uses heat removal through liquid-vapor phase change is developed. Although there have been prior investigations of phase change at the microscales, fluid flow at the conditions expected here is not well understood. A first-principles based cooling system performance model is developed and validated experimentally, and is integrated into the coupled electrochemical-thermal model for assessment of performance improvement relative to conventional thermal management strategies. The proposed cooling system passively removes heat almost isothermally with negligible thermal resistances between the heat source and cooling fluid. Thus, the minimization of peak temperatures and gradients within batteries allow increased power and energy densities unencumbered by thermal limitations.

A solution to reduce exhaust emissions from heavy commercial vehicles are to haul the vehicles completely or partially electric. This means that the vehicle must contain a significant electric energy source. The large capacity of the energy source causes the vehicle to either sacrifice a large part of its up time to charge the source or apply a higher charge power at the cost of power losses and lifetime of the energy source. This thesis contains a pre-study of high-power DC-charge of hybrid batteries from existing infrastructure suited to electric hybrid cars. Following parts are included in the thesis: modeling of a battery pack and a DC-DC converter, formulation of a MPC controller for the battery pack, analysis of charging strategies and battery restrictions through simulations. The thesis results shows that a longer charging time increases the energy efficiency and reduces the degradation in the battery. It also shows that a charging strategy similar to constant-current-constant-voltage charging should be used for a full charge of an empty battery.

This work presents novel modeling and experimental techniques that provide insight into liquid-phase mass transport and electron transfer processes in lithium-ion batteries. These included liquid-phase ionic mass transport (conduction and diffusion), lithium diffuion in the solid phase and electronic transport in the solid phase. Fundamental understanding of these processes is necessary to efficiently design and optimize lithium-ion batteries for different applications. To understand the effect of electrode structure on the electronic resistance of the cathode, we tested power performance of cathodes with combinations of three different carbon conductivity additives: vapor-grown carbon fibers (CF), carbon black (CB) and graphite (GR). With all other factors held constant, cathodes with a mixture of CF+CB were found to have the best power-performance, followed by cells containing CF only and then by CB+GR. Thus, the use of carbon fibers as conductive additive was found to improve the power performance of cells compared to the baseline (CB+GR). The enhanced electrode performance due to the fibers also allows an increase in energy density while still meeting power goals. About one-third of the available energy was lost to irreversible processes when cells were pulse-charged or discharged at the maximum rate allowed by voltage-cutoff constraints. We developed modeling and experimental techniques to quantify tortuosity in electrolyte-filled porous battery structures (separator and active-material film). Tortuosities of separators were measured by two methods, AC impedance and polarization-interrupt, which produced consistent results. The polarization-interrupt experiment was used similarly to measure effective electrolyte transport in porous films of cathode materials, particularly films containing lithium iron phosphate. An empirical relationship between porosity and the tortuosity of the porous structures was developed. Our results demonstrate that the tortuosity-dependent mass transport resistance in porous separators and electrodes is significantly higher than that predicted by the oft-used Bruggeman relationship. To understand the dominant resistances in a lithium battery, we developed and validated a model for lithium iron phosphate cathode. In doing so we considered unique physical features of this active material. Our model is unusual in terms of the range of experimental conditions for which it is validated. Various submodel and experimental techniques were used to find physically realistic parameters. The model was tested with different discharge rates and thicknesses of cathodes, in all cases showing good agreement, which suggests that the model takes into account physical realities with different thicknesses. The model was then used to find the dominant resistance for the tested cathodes. The model suggests that the inter-particle contact resistance between carbon and the active-material particles was a dominant resistance for the tested cathodes.

No description available.

No description available.

In the past decade, automobile manufacturers have gone through the initial adoption phase of electric mobility. The increasing momentum behind electric vehicles (EV) suggests that electrified storage systems will play an important role in electric mobility going forward. Lithium ion batteries have become one of the most common solutions for energy storage due to their light weight, high specific energy, low self-discharge rate, and non-memory effect. To fully benefit from a lithium-ion energy storage system and avoid its physical limitations, an accurate battery management system (BMS) is required. One of the key issues for successful BMS implementation is the battery model. A robust, accurate, and high fidelity battery model is required to mimic the battery dynamic behavior in a harsh environment. This dissertation introduces a robust and accurate model-based approach for lithium-ion battery management system. Many strategies for modeling the electrochemical processes in the battery have been proposed in the literature. The proposed models are often highly complex, requiring long computational time, large memory allocations, and real-time control. Thus, model-order reduction and minimization of the CPU run-time while maintaining the model accuracy are critical requirements for real-time implementation of lithium-ion electrochemical battery models. In this dissertation, different modeling techniques are developed. The proposed models reduce the model complexity while maintaining the accuracy. The thermal management of the lithium ion batteries is another important consideration for a successful BMS. Operating the battery pack outside the recommended operating conditions could result in unsafe operating conditions with undesirable consequences. In order to keep the battery within its safe operating range, the temperature of the cell core must be monitored and controlled. The dissertation implements a real-time electrochemical, thermal model for large prismatic cells used in electric vehicles' energy storage systems. The presented model accurately predicts the battery's core temperature and terminal voltage. / Thesis / Doctor of Philosophy (PhD)