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

Contribution to the improvement of the dissolved gas analysis techniques

There is a general agreement that in service conditions the quality of mineral insulating oils gradually deteriorates under the impact of electrical, thermal and environmental stresses. It is also widely accepted that only the incipient electrical failures such as hot spots and partial discharges are responsible for the gassing of oil. Knowing that the resulting fault gases dissolve in the oil, the technique of Dissolved Gas Analysis (DGA) was developed to detect incipient failures in the transformer. DGA has now become a standard in the utility industry throughout the world and is considered to be the most important oil test for insulating liquids in electrical apparatus. More importantly, an oil sample can be taken at anytime from most equipment without having to take it out of service, allowing a "window" inside the electrical apparatus that helps with diagnosing and trouble-shooting potential problems. This thesis intends to show that the gassing of oil is a more complex phenomenon. In order to emphasize the role played by contaminants in the gassing of oil, fundamental investigations were undertaken. The amount of gases evolved under the impact of electrical stress (ASTM D6180) by a sample of new and aged oil with/without paper was accurately measured along with some physicochemical properties, to assess the relationship between the cause and the symptoms of oil or oil-paper insulation deterioration. The outcome of these investigations provided experimental evidence that the chemical composition of hydrocarbon blend, the oil born decay products and the solid insulation are also contributing factors to oil gassing. Since this finding may affect the diagnostics predicted by some DGA techniques, some thorough investigations were performed. New, aged oil and reclaimed aged oil samples were submitted to thermal and electrical stresses (considering various scenarios) and the dissolved gases analyzed by chromatography. Three of the most used DGA techniques, namely the Duval's Triangle Roger's and Domenburg's ratios were implemented in Labview based software to predict the diagnostic. The obtained results provide experimental evidence that oil born decay products may affect the diagnostics predicted by some DGA techniques. Although such a research is still in a preliminary stage, some very stimulating results have been obtained. - II est généralement admis, qu'en conditions de service, la qualité des huiles minérales isolantes se détériore progressivement sous l'effet des contraintes électriques, thermiques et environnementales. Il est également largement admis que seules les défaillances électriques naissantes telles que les points chauds et les décharges partielles sont responsables du dégazage de l'huile. Sachant que les gaz ainsi produits par les défauts se dissolvent dans l'huile, la technique d'analyse de gaz dissous (AGD) a été mise au point pour détecter les défaillances dans le transformateur. L'AGD est maintenant devenu un standard dans l'industrie à travers le monde et elle est considérée comme le test le plus important dans les appareillages électriques isolés à l'huile. Plus important encore, un échantillon d'huile peut être pris à tout moment, de la plupart des équipements, sans avoir à le mettre hors service, pour le diagnostic et le dépannage d'éventuels problèmes. Ce mémoire se propose de montrer que le gazage dans l'huile est un phénomène complexe. Afin de souligner le rôle joué par les contaminants dans le dégazage de l'huile, des investigations fondamentales ont été entreprises. La quantité de gaz qui se dégage sous l'effet de la contrainte électrique (ASTM D6180) d'un échantillon d'huile neuf ou vieilli avec/sans papier a été mesurée avec précision ainsi que certaines propriétés physico-chimiques, afin d'évaluer la relation entre la cause et les effets de la détérioration de l'isolation de l'huile ou de l'huile-papier. Le résultat de ces investigations a fourni des preuves expérimentales que la composition chimique d'un mélange d'hydrocarbures, les produits issus de la décomposition de l'huile et de l'isolation solide sont également des facteurs qui contribuent à la génération de gaz dans l'huile. Etant donné que cette découverte pourrait affecter les diagnostics prédits par certaines techniques de l'ADG, certaines investigations approfondies ont été réalisées. Des échantillons d'huile neuve, âgée et régénérée ont été soumis à des contraintes thermiques et électriques (en considérant différents scénarios) et les gaz dissous analysés par chromatographie. Trois des techniques de l'ADG les plus utilisées à savoir le Triangle de Duval, Roger et le Ratio de Dôrnenburg ont été implémentées dans le logiciel Labview pour prédire le diagnostic. Les résultats obtenus fournissent la preuve expérimentale que les produits de la décomposition de l'huile peuvent affecter les résultats de diagnostic prédis par certaines techniques de l'ADG. Bien qu'une telle recherche soit encore à un stade préliminaire, certains résultats encourageants ont été obtenus

Review of the Li-Ion Battery, Thermal Management, and AI-Based Battery Management System for EV Application

With the large-scale commercialization and growing market share of electric vehicles (EVs), many studies have been dedicated to battery systems design and development. Their focus has been on higher energy efficiency, improved thermal performance and optimized multi-material battery enclosure designs. The integration of simulation-based design optimization of the battery pack and Battery Management System (BMS) is evolving and has expanded to include novelties such as artificial intelligence/machine learning (AI/ML) to improve efficiencies in design, manufacturing, and operations for their application in electric vehicles and energy storage systems. Specific to BMS, these advanced concepts enable a more accurate prediction of battery performance such as its State of Health (SOH), State of Charge (SOC), and State of Power (SOP). This study presents a comprehensive review of the latest developments and technologies in battery design, thermal management, and the application of AI in Battery Management Systems (BMS) for Electric Vehicles (EV)

Review of the Li-Ion Battery, Thermal Management, and AI-Based Battery Management System for EV Application

With the large-scale commercialization and growing market share of electric vehicles (EVs), many studies have been dedicated to battery systems design and development. Their focus has been on higher energy efficiency, improved thermal performance and optimized multi-material battery enclosure designs. The integration of simulation-based design optimization of the battery pack and Battery Management System (BMS) is evolving and has expanded to include novelties such as artificial intelligence/machine learning (AI/ML) to improve efficiencies in design, manufacturing, and operations for their application in electric vehicles and energy storage systems. Specific to BMS, these advanced concepts enable a more accurate prediction of battery performance such as its State of Health (SOH), State of Charge (SOC), and State of Power (SOP). This study presents a comprehensive review of the latest developments and technologies in battery design, thermal management, and the application of AI in Battery Management Systems (BMS) for Electric Vehicles (EV)

Electrochemical–Thermal Model of Pouch-type Lithium-ion Batteries

In this paper, a 3D (three-dimensional) layer structure of a pouch-type cell is modeled to understand the distribution of temperature and current density across the pouch type Lithium-Ion Battery (LIB). The electrochemical-thermal characteristics are studied, using 1D (one-dimensional) multiphysics model, and simulation results are validated with experimental results. Three-dimensional (3D) modeling of the battery gives the most efficient estimation of energy density, temperature response, overall heat generation and distribution inside the battery. One such 3D electro-thermal model was developed in this work, and the results obtained by the 3D model were validated by using experimental results obtained from LIBs. Temperature profiles of LIB obtained from 3D modeling indicated that the most heat is accumulated around the positive tab of the battery due to non-uniform current distribution and local internal resistance. The presented model can be used as a fast, yet accurate tool, to optimize the cell design for a particular application and for developing battery thermal management systems

Heat response of prismatic Li-ion cells

The experimental studies of the charge-discharge behavior and heat generation rate of lithium ion cells at different C-rates are conducted in this paper. We are extending this process to monitor and control the thermal behavior of Liion batteries by using phase change material (PCM) as a passive thermal management method to absorb and conduct heat to and from lithium-ion battery modules

High electrochemical detection of dopamine based on Cu doped single phase hexagonally ZnO plates

Dopamine is a chemical that plays a key role in various neurological diseases such as Parkinson's, depression, and some types of cancer. Hence, sensitive detection methods of dopamine are necessary for early discernment of diseases related to abnormal levels. In this study, Cu doped ZnO (Cu/ZnO) nanostructures, immobilized onto the surface of glassy carbon electrode (GCE), have been investigated as a highly efficient electrode material for the electrochemical detection of dopamine (DA). A simple hydrothermal process was used for the synthesis of the ZnO and Cu/ZnO nanostructures. Detailed characterization revealed that addition of Cu on the ZnO changed the morphology of ZnO creating a highly microporous nanostructure. The electrochemical response of DA on the Cu/ZnO/GC electrodes, determined using cyclic voltammetry (CV) and differential pulsed voltammetry (DPV), indicated that on these materials it is possible to achieve lower over-potentials for the DA oxidation and higher catalytic activity. Furthermore, the GCE modified with 50 % Cu doped ZnO showed the most promising performance with high stability in wide range of pH values (2–8 pH), and linear response for DA from 0.1–20 μM with high sensitivity of 2630 nA/μM and detection limit as low as 55 nM. The analytical performance of the developed sensor showed its potential capability for the DA quantification in complex biological systems