Switching-based state-of-charge estimation of lithium-ion batteries

The objective of this thesis is to explore a switching-based approach to estimate the state of charge (SOC) of Li-ion batteries. The knowledge of SOC can be utilized to significantly enhance battery performance and longevity. The thesis first presents a brief discussion on various SOC estimation methods, such as coulomb counting, use of electrochemical model combined with Kalman Filtering and open-circuit voltage (OCV). Subsequently, emphasis is placed on the OCV-based method. The advantage of the OCV method lies in its simplicity. It obviates the need for modeling and lowers computational burden compared to model-based approaches. The method yields accurate SOC estimates if a long period of battery resting time (switch-off time) is allowed. For smaller switch-off durations, the accuracy of SOC estimation reduces. However, experiments show that Li-ion batteries could give acceptable SOC estimates due to their fast transient response during switch-off. In traditional usage scenarios, a switch-off interval may not be practical. However, in distributed power systems with multiple storage elements, a switch-off interval could be provided. Experiments are conducted to characterize the estimation error versus the switch-off time. To reduce the switch-off time to 30 second switch-off time and to increase the accuracy of SOC estimation, a method is proposed to extrapolate the OCV at infinite time from the measured OCV using a time constant. This leads to predicted OCV for infinite switch-off intervals. Experiments are conducted to confirm the improved SOC estimation using the proposed method. For experimentation, a commercially available LiFeMgPO4 battery module as well as a single cell LiFePO4 battery, is used

Nanobead-reinforced outmost shell of solid-electrolyte interphase layers for suppressing dendritic growth of lithium metal

Department of Energy EngineeringDesign of catalyst support for high durability of oxygen electrocatalystPlating-stripping reversibility of lithium metal was improved by reinforcing the solid-electrolyte interphase (SEI) layer by inorganic nanobeads during formation of the SEI layer. The outmost SEI shell (OSS) was clearly identified, which is the SEI layer formed on current collectors (or lithium metal) before the first lithium metal deposition. The OSS was intrinsically brittle and fragile so that the OSS was easily broken by lithium metal dendrites growing along the progress of plating. Lithium metal deposit was not completely stripped back to lithium ions. On the other hand, lithium metal cells containing inorganic nanobeads in electrolyte showed high reversibility between plating and stripping. The nanobeads were incorporated into the OSS during the OSS formation. The nanobead-reinforced OSS having mechanically durable toughness suppressed dendritic growth of lithium metal, not allowing the dendrites to penetrate the OSS. In addition to the mechanical effect of nanobeads, the LiF-rich SEI layer formation was triggered by HF generated by the reaction of the moisture adsorbed on oxide nanobeads with PF6-. The LiF-rich composition was responsible for facile lithium ion transfer through the SEI layer and the OSS in the presence of nanobeads.clos

Phase Transformation Dynamics in Porous Battery Electrodes

Porous electrodes composed of multiphase active materials are widely used in Li-ion batteries, but their dynamics are poorly understood. Two-phase models are largely empirical, and no models exist for three or more phases. Using a modified porous electrode theory based on non-equilibrium thermodynamics, we show that experimental phase behavior can be accurately predicted from free energy models, without artificially placing phase boundaries or fitting the open circuit voltage. First, we simulate lithium intercalation in porous iron phosphate, a popular two-phase cathode, and show that the zero-current voltage gap, sloping voltage plateau and under-estimated exchange currents all result from size-dependent nucleation and mosaic instability. Next, we simulate porous graphite, the standard anode with three stable phases, and reproduce experimentally observed fronts of color-changing phase transformations. These results provide a framework for physics-based design and control for electrochemical systems with complex thermodynamics

Size-dependent Failure Behavior of Lithium-Iron Phosphate Battery under Mechanical Abuse

The use of battery electric vehicles is one of the green solutions to reduce environmental pollution and save the Earth. Based on the power, speed, and space constraints, the battery geometries (size and shape) are decided in the battery electric vehicles. However, battery failure assessment and abuse testing are much needed to ensure its safe operation. Herein, four types of lithium-iron phosphate batteries viz. 18650, 22650, 26650, and 32650 are considered to conduct lateral, longitudinal compression, and nail penetration tests. The mechanical failure is characterized by the voltage drop and temperature rise at the onset of the first short-circuit is identified by Aurdino-based voltage sensor module and temperature measurement module. The battery failure load and peak temperature at the onset of internal short-circuit during different mechanical abuse conditions are found to rely on the battery size strongly. The failure due to the onset of internal short circuit is observed to be delayed for small-sized 18650 batteries during lateral compression, unlike longitudinal compression and nail penetration test. At the onset of the short circuit, the LFPBs showed variation in temperature above the ambient value of 28 degree C. Among the LFPBs considered, the lowest variation of temperature rise (considering ambient temperature) is found to be 5.25 degree C for type 26650. The outcome of this work is anticipated to demonstrate the significance of the choice of battery sizes for different desired applications safely.Comment: 15 pages, 6 Figures, 2 tabl

Study on electronchemical impedance spectroscopy equipment for checking state of battery

Recently, the International Maritime Organization has tightened emission restrictions on greenhouse gases and air pollutants (NOx, SOx, etc.) and has referred to reducing ship carbon emissions. Today, eco-friendly vessels such as hybrid or electric propulsion vessels using large batteries as power sources are being developed to reduce these emissions. Batteries used in hybrid ships continue to be exposed to harmonics. These harmonics affect the battery life. If the condition of the battery is abnormal, the whole system will be affected. To prevent this, estimate the battery's life or health and replace the battery before an accident occurs. In this paper, we propose an EIS (Electrochemical Impedance Spectroscopy) device to estimate the state of a battery.1. Introduction 1 1.1 Background 1 1.2 Market Status 2 1.3 Purpose of study 4 2. Related theory of battery 5 2.1 Battery 5 2.2 Type of battery 6 2.2.1 Primary battery 6 2.2.2 Secondary battery 6 2.2.3 Fuel cell 11 2.3 BMS theory 11 2.3.1 C-rate 12 2.3.2 DOD(Depth of discharge) 13 2.3.3 SOC(State of charge) 14 2.3.4 SOH(State of health) 16 2.4 EIS(Electrochemical Impedance Spectroscopy) 18 2.4.1 CV(Cyclic Voltammetry) 18 2.4.2 EIS theory 19 3. EIS equipment configuration for battery condition estimation 24 3.1 Hardware 24 3.2 Software 29 3.3 Temperature measuring equipment 31 3.4 Battery impedance measurement experiment 33 3.5 Impedance according to battery condition 34 4. Conclusion 38 Reference 40 감사의 글 43Maste

Thermal Modeling and Optimization of Lithium-Ion Batteries for Electric Vehicles

This dissertation contributes to the modeling and optimization of Lithium-ion battery’s thermal management for electrified vehicles (EVs). EVs in automotive technology is one of the principal solutions to today’s environmental concerns such as air pollution and greenhouse impacts. Light duty and heavy duty EVs can decrease the amount of the pollution efficiently. EV’s receive their power from installed rechargeable batteries in the car. These batteries are not just utilized to power the car but used for the functioning of lights, wipers and other electrical accessories. The Lithium-ion batteries (LIBs) have attracted a lot of research interest in recent years, due to their high potential as compared to the conventional aqueous based batteries, high gravimetric and volumetric energy density, and high power capability. However, Li-ion batteries suffer from high self-heating, particularly during high power applications and fast charging, which confines their lifetime and cause safety, reliability and environmental concerns. Therefore, the first part of this study consists of the experimental investigation of the charge-discharge behavior and heat generation rate of lithium ion cells at different C-rates to monitor and record the thermal behavior of the cell. A further concern regarding LIBs is strongly dependent on the quality and efficiency of battery thermal management system. Hence, this is extremely important to identify a reliable and accurate battery management system (BMS). Here in the second part, we show that thermal management and the reliability of Li-ion batteries can be drastically improved using optimization technique. Furthermore, a LIB is a compact system including high energy materials which may undergo thermal runaway and explode the battery if overcharged due to the decomposition of battery materials within the electrolyte and electrodes that generate flammable gaseous species. The application of this kind of technology needs many laboratory experiments and simulations to identify the fundamental thermal characteristics of the system before passing it to the real use. An accurate battery model proposes a method to simulate the complex situations of the system without performing time consuming actual tests, thus a reliable scheme to identify the source of heat generation and required parameters to optimize the cell performance is necessary. For this reason, the latest phase of this research covers the development and comparison of a model based on adjustable design parameters to predict and optimize battery performances. This kind of model provides a relationship with the accuracy and simplicity to estimate the cell dynamics during charge and discharge