Modeling of Lithium-ion Battery Considering Temperature and Aging Uncertainties

This dissertation provides a systematic methodology for analyzing and solving the temperature and aging uncertainties in Li-ion battery modeling and states estimation in the electric vehicle applications. This topic is motivated by the needs of enhancing the performance and adaptability of battery management systems. In particular, temperature and aging are the most crucial factors that influence battery performance, modeling, and control. First, the basic theoretical knowledge of Li-ion battery modeling and State of Charge (SoC) estimation are introduced. The thesis presents an equivalent circuit battery model based SoC estimation using Adaptive Extended Kalman Filter (AEKF) algorithm to solve the initial SoC problem and provide good estimation result. Second, the thesis focuses on the understanding of the temperature-dependent performance of Li-ion battery. The temperature influence is investigated through Electrochemical Impedance Spectroscopy (EIS) tests to enhance the theoretical basis understanding and to derive model compensation functions for better model adaptability at different temperatures. Third, the battery aging mechanisms are revisited first and then a series of aging tests are conducted to understand the degradation path of Lithium-ion battery. Moreover, the incremental capacity analysis (ICA) based State of Health (SoH) estimation method xiv are applied to track battery aging level and develop the bias correction modeling method for aged battery. In the final phase, the study of parallel-connected battery packs is presented. The inconsistency problem due to different battery aging levels and its influence to parallel-connected packs are discussed. Based on simulation and experimental test results, it shows that the current difference in parallel connected cells is increased significantly at low SoC, despite the battery aging levels and the number of cells in parallel. In total, this dissertation utilizes physics-based battery modeling and states estimation method to optimize battery management under temperature and aging uncertainties in electric vehicle applications. The unique contributions include developing analytical compensation functions to improve equivalent circuit battery model adaptability under temperature uncertainty and developing ICA based SoH estimation and battery modeling method to overcome aging uncertainty.Ph.D.CECS Automotive Systems EngineeringUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/134041/1/Gong Dissertation Final.pdfDescription of Gong Dissertation Final.pdf : Dissertatio

A Circular Economy of Electrochemical Energy Storage Systems: Critical Review of SOH/RUL Estimation Methods for Second-Life Batteries

Humanity is facing a gloomy scenario due to global warming, which is increasing at unprecedented rates. Energy generation with renewable sources and electric mobility (EM) are considered two of the main strategies to cut down emissions of greenhouse gasses. These paradigm shifts will only be possible with efficient energy storage systems such as Li-ion batteries (LIBs). However, among other factors, some raw materials used on LIB production, such as cobalt and lithium, have geopolitical and environmental issues. Thus, in a context of a circular economy, the reuse of LIBs from EM for other applications (i.e., second-life batteries, SLBs) could be a way to overcome this problem, considering that they reach their end of life (EoL) when they get to a state of health (SOH) of 70–80% and still have energy storage capabilities that could last several years. The aim of this chapter is to make a review of the estimation methods employed in the diagnosis of LIB, such as SOH and remaining useful life (RUL). The correct characterization of these variables is crucial for the reassembly of SLBs and to extend the LIBs operational lifetime

Rechargeable lithium battery energy storage systems for vehicular applications

Batteries are used on-board vehicles for broadly two applications – starting-lighting-ignition (SLI) and vehicle traction. This thesis examines the suitability of the rechargeable lithium battery for both these applications, and develops algorithms for runtime prediction of the remaining battery charge. The largest market-share of rechargeable batteries is for the SLI application. Lead-acid batteries rule this market presently, although a handful of lithium SLI batteries have recently appeared on the market. The practicality of different lithium battery chemistries has been evaluated for this application over wide-ranging criteria and it has been found that the batteries based on lithium iron phosphate and lithium titanate oxide chemistries commercially available in the market are the most suitable. Lithium SLI batteries would require a higher initial cost and additional electronic hardware in the form of battery management and thermal management systems, but would last the life-time of the vehicle. In fact, with the decrease in the cost of lithium SLI batteries with higher volumes, over the life-time of the vehicle, the total costs of the existing lead-acid battery and the lithium battery would be about the same. The electric traction application is probably the most demanding of all battery applications and imposes the harshest requirements on the battery cells and the battery management system. Algorithms to manage the battery cells for consumer power electronics, for example, do not perform satisfactorily for the electric traction application. This thesis presents algorithms to accurately determine the remaining charge of a lithium battery cell during runtime on-board the vehicle. The algorithm changes slightly depending upon the type of lithium chemistry and could be used in conjunction with different power management strategies on a vehicle with electric traction — whether a pure electric vehicle, hybrid electric vehicle (HEV) or a plug-in hybrid electric vehicle. An accurate estimate of battery charge is important for the battery management system; allows the battery pack to be used more efficiently, reliably and safely; and also provides a reasonably accurate estimate of the remaining distance that could be travelled to the driver. It also prevents over-charging or over-discharging the battery, which are detrimental to its life, and provides an indication when the battery would need to be replaced. The central contribution of this thesis is in developing an algorithm based on an electrical equivalent circuit model of a rechargeable lithium cell that includes thermal dependence, is accurate, yet simple enough to require low on-board processing capacity. The algorithm has been validated through extensive experimental tests for the lithium nickel-manganese-cobalt and the lithium iron phosphate chemistries at the University of Pisa labs. The algorithm was also successfully implemented using an adaptive state estimator (extended Kalman filter) for overcoming the difficulties imposed by the lithium iron phosphate chemistry. The algorithm was also developed into a model in collaboration with Mathworks for their toolbox and shall be commercially launched later this year. The model algorithm also forms the core of the battery management algorithm for the European Union’s Hybrid Commercial Vehicle (HCV) Project for future HEV trucks and buses by Volvo, Iveco, Daf and Solaris. The model was also used (as part of a battery model) for hybridizing the power-train of passenger buses for Bredamenarinibus (an Italian bus manufacturer) through modelling and simulation. The conventional power trains of three different buses representing different market segments were hybridised using a series-hybrid electric architecture and simulated with different power management strategies over different types of duty cycles, including real-life duty cycles provided by the manufacturer. Even with the increased weight of the hybrid buses (due to additional batteries and electrical equipment) the simulation predicts fuel savings between 22 to 25% depending upon the power management strategy for the hybrid buses. The prototypes of these series-hybrid buses are under production and would be tested in different Italian cities this year, before entering commercial production

Powering the future: a comprehensive review of battery energy storage systems

Global society is significantly speeding up the adoption of renewable energy sources and their integration into the current existing grid in order to counteract growing environmental problems, particularly the increased carbon dioxide emission of the last century. Renewable energy sources have a tremendous potential to reduce carbon dioxide emissions because they practically never produce any carbon dioxide or other pollutants. On the other hand, these energy sources are usually influenced by geographical location, weather, and other factors that are of stochastic nature. The battery energy storage system can be applied to store the energy produced by RESs and then utilized regularly and within limits as necessary to lessen the impact of the intermittent nature of renewable energy sources. The main purpose of the review paper is to present the current state of the art of battery energy storage systems and identify their advantages and disadvantages. At the same time, this helps researchers and engineers in the field to find out the most appropriate configuration for a particular application. This study offers a thorough analysis of the battery energy storage system with regard to battery chemistries, power electronics, and management approaches. This paper also offers a detailed analysis of battery energy storage system applications and investigates the shortcomings of the current best battery energy storage system architectures to pinpoint areas that require further study.This publication is part of the project TED2021-132864A-I00, funded by MCIN/ AEI/10.13039/501100011033 and by the European Union “NextGenerationEU”/PRTR”.Peer ReviewedPostprint (published version

Battery Management in Electric Vehicles: Current Status and Future Trends

Lithium-ion batteries are an indispensable component of the global transition to zero-carbon energy and are instrumental in achieving COP26's objective of attaining global net-zero emissions by the mid-century. However, their rapid expansion comes with significant challenges. The continuous demand for lithium-ion batteries in electric vehicles (EVs) is expected to raise global environmental and supply chain concerns, given that the critical materials required for their production are finite and predominantly mined in limited regions worldwide. Consequently, significant battery waste management will eventually become necessary. By implementing appropriate and enhanced battery management techniques in electric vehicles, the performance of batteries can be improved, their lifespan extended, secondary uses enabled, and the recycling and reuse of EV batteries promoted, thereby mitigating global environmental and supply chain concerns. Therefore, this reprint was crafted to update the scientific community on recent advancements and future trajectories in battery management for electric vehicles. The content of this reprint spans a spectrum of EV battery advancements, ranging from fundamental battery studies to the utilization of neural network modeling and machine learning to optimize battery performance, enhance efficiency, and ensure prolonged lifespan

Lithium-ion cell modeling, state estimation, and fault detection considering state of health for battery management systems

Lithium-ion batteries (LIBs) with high energy density and longer cycle life enable a comparable driving range per charge for electric vehicles (EVs) with their gas counterparts. However, the LIBs are very sensitive to variations in operating conditions, such as overcharge/discharge, high/low temperatures, and mechanical abuse. A battery management system (BMS) is employed to orchestrate safe and reliable operation by monitoring the voltage, current, temperature, state of charge (SOC), and state of health (SOH) and optimizing the charging and discharging cycles. The SOC and SOH, which determine the performance of the LIB, are governed by several stress-inducing factors, such as operating temperature, C-rate, aging, and internal faults. So, it is important to estimate the SOC and SOH in real time, considering the factors affecting the degradation of the battery. On the other hand, an internal fault in LIB leads to thermal runaway. Early detection and diagnosis of these faults are necessary to avoid catastrophic failures of LIBs.In this dissertation, we developed health-inclusive dynamic models for simultaneous state and parameter estimations and fault detection (FD) schemes. First, we proposed a nonlinear parameter-varying equivalent circuit model (ECM) integrated with the parameter dynamics for simultaneous state and parameter estimation using nonlinear observer-based approaches. Second, the proposed model is extended to integrate the SOH and thermal behavior with ECM. The SOH-coupled nonlinear electric-thermal-aging model comprehends the interplay between the SOC and SOH and couples the ECM dynamics with capacity fade. The proposed model is further extended by integrating the ohmic resistance dynamics for simultaneous SOC, SOH, and parameter estimation using filtering algorithms. Finally, two FD schemes, based on the SOC-based and SOH-coupled models, are proposed to detect internal (thermal and side-reaction) faults by tracking the temperature and parameter residuals of the battery. Adaptive thresholds are designed to account for modeling uncertainties and the effect of degradation in the residuals and avoid false positives. In addition, a novel neural network-based observer is proposed to learn the fault dynamics and estimate the SOC, SOH, and core temperature under internal faults. Experimental and numerical validation results are presented to corroborate the designs

Data Science-Based Full-Lifespan Management of Lithium-Ion Battery

This open access book comprehensively consolidates studies in the rapidly emerging field of battery management. The primary focus is to overview the new and emerging data science technologies for full-lifespan management of Li-ion batteries, which are categorized into three groups, namely (i) battery manufacturing management, (ii) battery operation management, and (iii) battery reutilization management. The key challenges, future trends as well as promising data-science technologies to further improve this research field are discussed. As battery full-lifespan (manufacturing, operation, and reutilization) management is a hot research topic in both energy and AI fields and none specific book has focused on systematically describing this particular from a data science perspective before, this book can attract the attention of academics, scientists, engineers, and practitioners. It is useful as a reference book for students and graduates working in related fields. Specifically, the audience could not only get the basics of battery manufacturing, operation, and reutilization but also the information of related data-science technologies. The step-by-step guidance, comprehensive introduction, and case studies to the topic make it accessible to audiences of different levels, from graduates to experienced engineers