Design and implementation of machine learning techniques for modeling and managing battery energy storage systems

The fast technological evolution and industrialization that have interested the humankind since the fifties has caused a progressive and exponential increase of CO2 emissions and Earth temperature. Therefore, the research community and the political authorities have recognized the need of a deep technological revolution in both the transportation and the energy distribution systems to hinder climate changes. Thus, pure and hybrid electric powertrains, smart grids, and microgrids are key technologies for achieving the expected goals. Nevertheless, the development of the above mentioned technologies require very effective and performing Battery Energy Storage Systems (BESSs), and even more effective Battery Management Systems (BMSs). Considering the above background, this Ph.D. thesis has focused on the development of an innovative and advanced BMS that involves the use of machine learning techniques for improving the BESS effectiveness and efficiency. Great attention has been paid to the State of Charge (SoC) estimation problem, aiming at investigating solutions for achieving more accurate and reliable estimations. To this aim, the main contribution has concerned the development of accurate and flexible models of electrochemical cells. Three main modeling requirements have been pursued for ensuring accurate SoC estimations: insight on the cell physics, nonlinear approximation capability, and flexible system identification procedures. Thus, the research activity has aimed at fulfilling these requirements by developing and investigating three different modeling approaches, namely black, white, and gray box techniques. Extreme Learning Machines, Radial Basis Function Neural Networks, and Wavelet Neural Networks were considered among the black box models, but none of them were able to achieve satisfactory SoC estimation performances. The white box Equivalent Circuit Models (ECMs) have achieved better results, proving the benefit that the insight on the cell physics provides to the SoC estimation task. Nevertheless, it has appeared clear that the linearity of ECMs has reduced their effectiveness in the SoC task. Thus, the gray box Neural Networks Ensemble (NNE) and the white box Equivalent Neural Networks Circuit (ENNC) models have been developed aiming at exploiting the neural networks theory in order to achieve accurate models, ensuring at the same time very flexible system identification procedures together with nonlinear approximation capabilities. The performances of NNE and ENNC have been compelling. In particular, the white box ENNC has reached the most effective performances, achieving accurate SoC estimations, together with a simple architecture and a flexible system identification procedure. The outcome of this thesis makes it possible the development of an interesting scenario in which a suitable cloud framework provides remote assistance to several BMSs in order to adapt the managing algorithms to the aging of BESSs, even considering different and distinct applications

SoC estimation for lithium-ion batteries : review and future challenges

ABSTRACT: Energy storage emerged as a top concern for the modern cities, and the choice of the lithium-ion chemistry battery technology as an effective solution for storage applications proved to be a highly efficient option. State of charge (SoC) represents the available battery capacity and is one of the most important states that need to be monitored to optimize the performance and extend the lifetime of batteries. This review summarizes the methods for SoC estimation for lithium-ion batteries (LiBs). The SoC estimation methods are presented focusing on the description of the techniques and the elaboration of their weaknesses for the use in on-line battery management systems (BMS) applications. SoC estimation is a challenging task hindered by considerable changes in battery characteristics over its lifetime due to aging and to the distinct nonlinear behavior. This has led scholars to propose different methods that clearly raised the challenge of establishing a relationship between the accuracy and robustness of the methods, and their low complexity to be implemented. This paper publishes an exhaustive review of the works presented during the last five years, where the tendency of the estimation techniques has been oriented toward a mixture of probabilistic techniques and some artificial intelligence

Modeling and Analysis of Permanent Magnet Spherical Motors by A Multi-task Gaussian Process Method and Finite Element Method for Output Torque

Permanent magnet spherical motors (PMSMs) operate on the principle of the dc excitation of stator coils and three freedom of motion in the rotor. Each coil generates the torque in a specific direction, collectively they move the rotor to a direction of motion. Modeling and analysis of the output torque are of critical importance for precise position control applications. The control of these motors requires precise output torques by all coils at a specific rotor position, which is difficult to achieve in the three-dimension space. This article is the first to apply the Gaussian process to establish the relationship of the rotor position and the output torque for PMSMs. Traditional methods are difficult to resolve such a complex three-dimensional problem with a reasonable computational accuracy and time. This article utilizes a data-driven method using only input and output data validated by experiments. The multitask Gaussian process is developed to calculate the total torque produced by multiple coils at the full operational range. The training data and test data are obtained by the finite-element method. The effectiveness of the proposed method is validated and compared with existing data-driven approaches. The results exhibit superior performance of accuracy

Multi-Scale Modeling, Surrogate-Based Analysis, and Optimization of Lithium-Ion Batteries for Vehicle Applications.

A common attribute of electric-powered aerospace vehicles and systems such as unmanned aerial vehicles, hybrid- and fully-electric aircraft, and satellites is that their performance is usually limited by the energy density of their batteries. Although lithium-ion batteries offer distinct advantages such as high voltage and low weight over other battery technologies, they are a relatively new development, and thus significant gaps in the understanding of the physical phenomena that govern battery performance remain. As a result of this limited understanding, batteries must often undergo a cumbersome design process involving many manual iterations based on rules of thumb and ad-hoc design principles. A systematic study of the relationship between operational, geometric, morphological, and material-dependent properties and performance metrics such as energy and power density is non-trivial due to the multiphysics, multiphase, and multiscale nature of the battery system. To address these challenges, two numerical frameworks are established in this dissertation: a process for analyzing and optimizing several key design variables using surrogate modeling tools and gradient-based optimizers, and a multi-scale model that incorporates more detailed microstructural information into the computationally efficient but limited macro-homogeneous model. In the surrogate modeling process, multi-dimensional maps for the cell energy density with respect to design variables such as the particle size, ion diffusivity, and electron conductivity of the porous cathode material are created. A combined surrogate- and gradient-based approach is employed to identify optimal values for cathode thickness and porosity under various operating conditions, and quantify the uncertainty in the surrogate model. The performance of multiple cathode materials is also compared by defining dimensionless transport parameters. The multi-scale model makes use of detailed 3-D FEM simulations conducted at the particle-level. A monodisperse system of ellipsoidal particles is used to simulate the effective transport coefficients and interfacial reaction current density within the porous microstructure. Microscopic simulation results are shown to match well with experimental measurements, while differing significantly from homogenization approximations used in the macroscopic model. Global sensitivity analysis and surrogate modeling tools are applied to couple the two length scales and complete the multi-scale model.PhDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99980/1/wenbodu_1.pd

Lithium-Ion Batteries

Lithium-ion batteries (LIBs), as a key part of the 2019 Nobel Prize in Chemistry, have become increasingly important in recent years, owing to their potential impact on building a more sustainable future. Compared with other batteries developed, LIBs offer high energy density, high discharge power, and a long service life. These characteristics have facilitated a remarkable advance of LIBs in many frontiers, including electric vehicles, portable and flexible electronics, and stationary applications. Since the field of LIBs is advancing rapidly and attracting an increasing number of researchers, it is necessary to often provide the community with the latest updates. Therefore, this book was designed to focus on updating the electrochemical community with the latest advances and prospects on various aspects of LIBs. The materials presented in this book cover advances in several fronts of the technology, ranging from detailed fundamental studies of the electrochemical cell to investigations to better improve parameters related to battery packs

Numerical Analysis of Lithium-ion Battery Thermal Management System Towards Fire Safety Improvement

The development of alternative energy sources aims to tackle the energy crisis and climate change. Due to the intermittent nature of renewable energy, energy storage systems find antidotes to the current flaws for ensuring a stable and consistent power supply and reducing our reliance on fossil fuels. Lithium-ion batteries are the most used energy storage unit and have been applied in many fields, such as portable devices, building infrastructure, automotive industries, etc. Nevertheless, there remain significant safety concerns and fire risks. Thus, this has created much interest particularly in developing a comprehensive numerical tool to effectively assess the thermal behaviour and safety performance of battery thermal management systems (BTMs). In this thesis, a modelling framework was built by integrating the artificial neural network model with the computational fluid dynamics analysis. This includes (i) a comparison of natural ventilation and forced air cooling under various ambient pressures; (ii) an analysis of thermal behaviour and cooling performance with different ambient temperatures and ventilation velocities; and (iii) optimisation of battery pack layout for enhancing the cooling efficiency and reducing the risks of thermal runaway and fire outbreak. The optimal battery design achieved a 1.9% decrease in maximum temperature and a 4.5% drop in temperature difference. Moreover, this thesis delivered an overall review of BTMs employing machine learning (ML) techniques and the application of various ML models in battery fire diagnosis and early warning, which brings new insights into BTMs design and anticipates further smart battery systems. In addition, the battery thermal propagation effect under various abnormal heat generation locations was demonstrated to investigate several stipulating thermal propagation scenarios for enhancing battery thermal performances. The results indicated that various abnormal heat locations disperse heat to the surrounding coolant and other cells, affecting the cooling performance of the battery pack. The feasibility of compiling all pertinent information, including battery parameters and operation conditions, was studied in this thesis since ML models can build non-related factors relationships. The integrated numerical model offers a promising and efficient tool for simultaneously optimising multiple factors in battery design and facilitates a constructive understanding of battery performance and potential risks

Study of the Potential of Electrified Powertrains with Dual-Fuel Combustion to Achieve the 2025 Emissions Targets in Heavy-Duty Applications

[ES] El transporte de personas, así como de carga ha evolucionado y crecido tremendamente en los últimos años. El desarrollo tecnológico debió ser adaptado a las diferentes medidas gubernamentales en términos de control de emisiones contaminantes. Desde el acuerdo de Paris en 2015 para mantener el crecimiento de la temperatura global por debajo de 1.5oC, se han impuesto también límites para las emisiones de CO2 por parte de vehículos de carretera. Para el sector del transporte pesado, se han impuesto límites de flota de 15% para 2025 y 30% para 2030 de reducción del CO2 con respecto a 2019. Por lo tanto, esta doble restricción de muy bajos niveles de emisiones contaminantes, así como de gases de efecto invernadero hacen que el sector del transporte este ante un gran desafío tecnológico. En 2022, el transporte de carga tiene un 99% de vehículos propulsados a motor de combustión interna con Diesel como combustible y sin ningún tipo de ayuda eléctrica en el sistema de propulsión. Los límites de emisiones contaminantes como Euro 6 son alcanzados con complejos sistemas de postratamiento que además agregan el consumo de Urea. Trabajos previos en la bibliografía, así como sistemas prototipo han demostrado que es posible alcanzar los objetivos de emisiones contaminantes con métodos avanzados de control de la combustión y así disminuyendo la complejidad del post tratamiento en la salida de gases. Con mayor éxito, el concepto de Reactivity Controlled Combustion Ignition puede alcanzar valores por debajo de Euro 6 con eficiencia similar a la combustión de Diesel. Sin embargo, no soluciona los problemas de emisiones de CO2. Por otro lado, en vehículos de pasajeros fue demostrado con suceso la aplicación de motores eléctricos en el sistema de propulsión para mejorar la eficiencia global del vehículo. El caso extremo son los vehículos puramente electicos donde se alcanza eficiencias por arriba del 70% contra 35% de los vehículos no electrificados. Sin embargo, limitaciones de autonomía, tiempo de carga y la no clara reducción global de la contaminación debido a las emisiones de la energía de la red eléctrica y la contaminación de las baterías de ion-litio hacen que este sistema de propulsión este bajo discusión. Para los vehículos con algún grado de electrificación, las emisiones de gases contaminantes siguen siendo un problema como para el caso no electrificado. Por lo tanto, esta tesis doctoral aborda el problema de emisiones contaminantes, así como de CO2 combinado modos avanzados de combustión con sistemas de propulsión electrificado. La aplicación de estas tecnologías se centra en el sector del transporte de carretera pesado. En particular, un camión de 18 toneladas de carga máxima que originalmente en 2022 equipa un motor seis cilindros de 8 litros con combustión convencional Diesel. El presente trabajo utiliza herramientas experimentales como son medidas en banco motor, así como en carretera para alimentar y validar modelos numéricos de motor, sistema de postratamiento, así como de vehículo. Este último es el punto central del trabajo ya que permite abordar sistemas como el mild hybrid, full hybrid y plug-in hybrid. Calibración de motor experimental dedicada a sistemas de propulsión hibrido es presentada con combustibles sintéticos y/o para llegar a los límites de Euro 7.[CA] El transport de persones, així com de càrrega ha evolucionat i crescut tremendament en els últims anys. El desenvolupament tecnològic degué ser adaptat a les diferents mesures governamentals en termes de control d'emissions contaminants. Des de l'acord de Paris en 2015 per a mantindre el creixement de la temperatura global per davall de 1.5oC, s'han imposat també límits per a les emissions de CO¿ per part de vehicles de carretera. Per al sector del transport pesat, s'han imposat limites de flota de 15% per a 2025 i 30% per a 2030 de reducció del CO¿ respecte a 2019. Per tant, aquesta doble restricció de molt baixos nivells d'emissions contaminants, així com de gasos d'efecte d'hivernacle fan que el sector del transport aquest davant un gran desafiament tecnològic. En 2022, el transport de càrrega té un 99% de vehicles propulsats a motor de combustió interna amb Dièsel com a combustible i sense cap mena d'ajuda elèctrica en el sistema de propulsió. Els limites d'emissions contaminants com a Euro 6 són aconseguits amb complexos sistemes de posttractament que a més agreguen el consum d'Urea. Treballs previs en la bibliografia, així com sistemes prototip han demostrat que és possible aconseguir els objectius d'emissions contaminants amb mètodes avançats de control de la combustió i així disminuint la complexitat del post tractament en l'eixida de gasos. Amb major èxit, el concepte de Reactivity Controlled Combustion Ignition pot aconseguir valors per davall d'Euro 6 amb eficiència similar a la combustió de Dièsel. No obstant això, no soluciona els problemes d'emissions de CO¿. D'altra banda, en vehicles de passatgers va ser demostrat amb succés l'aplicació de motors elèctrics en el sistema de propulsió per a millorar l'eficiència global del vehicle. El cas extrem són els vehicles purament electicos on s'aconsegueix eficiències per dalt del 70% contra 35% dels vehicles no electrificats. No obstant això, limitacions d'autonomia, temps de càrrega i la no clara reducció global de la contaminació a causa de les emissions de l'energia de la xarxa elèctrica i la contaminació de les bateries d'ió-liti fan que aquest sistema de propulsió aquest baix discussió. Per als vehicles amb algun grau d'electrificació, les emissions de gasos contaminants continuen sent un problema com per al cas no electrificat. Per tant, aquesta tesi doctoral aborda el problema d'emissions contaminants, així com de CO¿ combinat maneres avançades de combustió amb sistemes de propulsió electrificat. L'aplicació d'aquestes tecnologies se centra en el sector del transport de carretera pesat. En particular, un camió de 18 tones de càrrega màxima que originalment en 2022 equipa un motor sis cilindres de 8 litres amb combustió convencional Dièsel. El present treball utilitza eines experimentals com són mesures en banc motor, així com en carretera per a alimentar i validar models numèrics de motor, sistema de posttractament, així com de vehicle. Est ultime és el punt central del treball ja que permet abordar sistemes com el mild hybrid, full *hybrid i plug-in hybrid. Calibratge de motor experimental dedicada a sistemes de propulsió hibride és presentada amb combustibles sintètics i/o per a arribar als límits d'Euro 7.[EN] The transport of people, as well as cargo, has evolved and grown tremendously over the recent years. Technological development had to be adapted to the different government measures for controlling polluting emissions. Since the Paris agreement in 2015 limits have also been imposed on the CO2 emissions from road vehicles to keep global temperature growth below 1.5oC. For the heavy transport sector, fleet limits of 15% for 2025 and 30% for 2030 CO2 reduction have been introduced with respect to the limits of 2019. Therefore, the current restriction of very low levels of polluting emissions, as well as greenhouse gases, makes the transport sector face a great technological challenge. In 2021, 99% of freight transport was powered by an internal combustion engine with Diesel as fuel and without any type of electrical assistance in the propulsion system. Moreover, polluting emission limits such as the Euro 6 are achieved with complex post-treatment systems that also add to the consumption of Urea. Previous research and prototype systems have shown that it is possible to achieve polluting emission targets with advanced combustion control methods, thus reducing the complexity of post-treatment in the exhaust gas. With greater success, the concept of Reactivity Controlled Combustion Ignition can reach values below the Euro 6 with similar efficiency to Diesel combustion. Unfortunately, it does not solve the CO2 emission problems. On the other hand, in passenger vehicles, the application of electric motors in the propulsion system has been shown to successfully improve the overall efficiency of the vehicle. The extreme case is the purely electric vehicles, where efficiencies above 70% are achieved against 35% of the non-electrified vehicles. However, limitations of vehicle range, charging time, payload reduction and an unclear overall reduction in greenhouse emissions bring this propulsion system under discussion. For vehicles with some degree of electrification, polluting gas emissions continue to be a problem as for the non-electrified case. Therefore, this doctoral Thesis addresses the problem of polluting emissions and CO2 combined with advanced modes of combustion with electrified propulsion systems. The application of these technologies focuses on the heavy road transport sector. In particular, an 18-ton maximum load truck that originally was equipped with an 8-liter six-cylinder engine with conventional Diesel combustion. The present work uses experimental tools such as measurements on the engine bench as well as on the road to feed and validate numerical models of the engine, after-treatment system, and the vehicle. The latter is the central point of the work since it allows addressing systems such as mild hybrid, full hybrid, and plug-in hybrid. Experimental engine calibration dedicated to hybrid propulsion systems is presented with synthetic fuels in order to reach the limits of the Euro 7.This Doctoral Thesis has been partially supported by the Universitat Politècnica de València through the predoctoral contract of the author (Subprograma 2), which is included within the framework of Programa de Apoyo para la Investigación y Desarrollo (PAID)Martínez Boggio, SD. (2022). Study of the Potential of Electrified Powertrains with Dual-Fuel Combustion to Achieve the 2025 Emissions Targets in Heavy-Duty Applications [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/18883

New advances in vehicular technology and automotive engineering

An automobile was seen as a simple accessory of luxury in the early years of the past century. Therefore, it was an expensive asset which none of the common citizen could afford. It was necessary to pass a long period and waiting for Henry Ford to establish the first plants with the series fabrication. This new industrial paradigm makes easy to the common American to acquire an automobile, either for running away or for working purposes. Since that date, the automotive research grown exponentially to the levels observed in the actuality. Now, the automobiles are indispensable goods; saying with other words, the automobile is a first necessity article in a wide number of aspects of living: for workers to allow them to move from their homes into their workplaces, for transportation of students, for allowing the domestic women in their home tasks, for ambulances to carry people with decease to the hospitals, for transportation of materials, and so on, the list don’t ends. The new goal pursued by the automotive industry is to provide electric vehicles at low cost and with high reliability. This commitment is justified by the oil’s peak extraction on 50s of this century and also by the necessity to reduce the emissions of CO2 to the atmosphere, as well as to reduce the needs of this even more valuable natural resource. In order to achieve this task and to improve the regular cars based on oil, the automotive industry is even more concerned on doing applied research on technology and on fundamental research of new materials. The most important idea to retain from the previous introduction is to clarify the minds of the potential readers for the direct and indirect penetration of the vehicles and the vehicular industry in the today’s life. In this sequence of ideas, this book tries not only to fill a gap by presenting fresh subjects related to the vehicular technology and to the automotive engineering but to provide guidelines for future research. This book account with valuable contributions from worldwide experts of automotive’s field. The amount and type of contributions were judiciously selected to cover a broad range of research. The reader can found the most recent and cutting-edge sources of information divided in four major groups: electronics (power, communications, optics, batteries, alternators and sensors), mechanics (suspension control, torque converters, deformation analysis, structural monitoring), materials (nanotechnology, nanocomposites, lubrificants, biodegradable, composites, structural monitoring) and manufacturing (supply chains). We are sure that you will enjoy this book and will profit with the technical and scientific contents. To finish, we are thankful to all of those who contributed to this book and who made it possible.info:eu-repo/semantics/publishedVersio

Electric Vehicle Efficient Power and Propulsion Systems

Vehicle electrification has been identified as one of the main technology trends in this second decade of the 21st century. Nearly 10% of global car sales in 2021 were electric, and this figure would be 50% by 2030 to reduce the oil import dependency and transport emissions in line with countries’ climate goals. This book addresses the efficient power and propulsion systems which cover essential topics for research and development on EVs, HEVs and fuel cell electric vehicles (FCEV), including: Energy storage systems (battery, fuel cell, supercapacitors, and their hybrid systems); Power electronics devices and converters; Electric machine drive control, optimization, and design; Energy system advanced management methods Primarily intended for professionals and advanced students who are working on EV/HEV/FCEV power and propulsion systems, this edited book surveys state of the art novel control/optimization techniques for different components, as well as for vehicle as a whole system. New readers may also find valuable information on the structure and methodologies in such an interdisciplinary field. Contributed by experienced authors from different research laboratory around the world, these 11 chapters provide balanced materials from theorical background to methodologies and practical implementation to deal with various issues of this challenging technology. This reprint encourages researchers working in this field to stay actualized on the latest developments on electric vehicle efficient power and propulsion systems, for road and rail, both manned and unmanned vehicles