Data-driven nonparametric Li-ion battery ageing model aiming at learning from real operation data - Part B : cycling operation

Conventional Li-ion battery ageing models, such as electrochemical, semi-empirical and empirical models, require a significant amount of time and experimental resources to provide accurate predictions under realistic operating conditions. At the same time, there is significant interest from industry in the introduction of new data collection telemetry technology. This implies the forthcoming availability of a significant amount of real-world battery operation data. In this context, the development of ageing models able to learn from in-field battery operation data is an interesting solution to mitigate the need for exhaustive laboratory testing. In a series of two papers, a data-driven ageing model is developed for Li-ion batteries under the Gaussian Process framework. A special emphasis is placed on illustrating the ability of the Gaussian Process model to learn from new data observations, providing more accurate and confident predictions, and extending the operating window of the model. The first paper of the series focussed on the systematic modelling and experimental verification of cell degradation through calendar ageing. Conversantly, this second paper addresses the same research challenge when the cell is electrically cycled. A specific covariance function is composed, tailored for use in a battery ageing application. Over an extensive dataset involving 124 cells tested during more than three years, different training possibilities are contemplated in order to quantify the minimal number of laboratory tests required for the design of an accurate ageing model. A model trained with only 26 tested cells achieves an overall mean-absolute-error of 1.04% in the capacity curve prediction, after being validated under a broad window of both dynamic and static cycling temperatures, Depth-of-Discharge, middle-SOC, charging and discharging C-rates

Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial

On top of machine learning models, uncertainty quantification (UQ) functions as an essential layer of safety assurance that could lead to more principled decision making by enabling sound risk assessment and management. The safety and reliability improvement of ML models empowered by UQ has the potential to significantly facilitate the broad adoption of ML solutions in high-stakes decision settings, such as healthcare, manufacturing, and aviation, to name a few. In this tutorial, we aim to provide a holistic lens on emerging UQ methods for ML models with a particular focus on neural networks and the applications of these UQ methods in tackling engineering design as well as prognostics and health management problems. Toward this goal, we start with a comprehensive classification of uncertainty types, sources, and causes pertaining to UQ of ML models. Next, we provide a tutorial-style description of several state-of-the-art UQ methods: Gaussian process regression, Bayesian neural network, neural network ensemble, and deterministic UQ methods focusing on spectral-normalized neural Gaussian process. Established upon the mathematical formulations, we subsequently examine the soundness of these UQ methods quantitatively and qualitatively (by a toy regression example) to examine their strengths and shortcomings from different dimensions. Then, we review quantitative metrics commonly used to assess the quality of predictive uncertainty in classification and regression problems. Afterward, we discuss the increasingly important role of UQ of ML models in solving challenging problems in engineering design and health prognostics. Two case studies with source codes available on GitHub are used to demonstrate these UQ methods and compare their performance in the life prediction of lithium-ion batteries at the early stage and the remaining useful life prediction of turbofan engines

Data-driven nonparametric Li-ion battery ageing model aiming at learningfrom real operation data - Part B: Cycling operation

Conventional Li-ion battery ageing models, such as electrochemical, semi-empirical and empirical models, require a significant amount of time and experimental resources to provide accurate predictions under realistic operating conditions. At the same time, there is significant interest from industry in the introduction of new data collection telemetry technology. This implies the forthcoming availability of a significant amount of real-world battery operation data. In this context, the development of ageing models able to learn from in-field battery operation data is an interesting solution to mitigate the need for exhaustive laboratory testing. In a series of two papers, a data-driven ageing model is developed for Li-ion batteries under the Gaussian Process framework. A special emphasis is placed on illustrating the ability of the Gaussian Process model to learn from new data observations, providing more accurate and confident predictions, and extending the operating window of the model. The first paper of the series focussed on the systematic modelling and experimental verification of cell degradation through calendar ageing. Conversantly, this second paper addresses the same research challenge when the cell is electrically cycled. A specific covariance function is composed, tailored for use in a battery ageing application. Over an extensive dataset involving 124 cells tested during more than three years, different training possibilities are contemplated in order to quantify the minimal number of laboratory tests required for the design of an accurate ageing model. A model trained with only 26 tested cells achieves an overall mean-absolute-error of 1.04% in the capacity curve prediction, after being validated under a broad window of both dynamic and static cycling temperatures, Depth-of-Discharge, middle-SOC, charging and discharging C-rates.This investigation work was financially supported by ELKARTEK (CICe2018 - Desarrollo de actividades de investigacion fundamental estrategica en almacenamiento de energia electroquimica y termica para sistemas de almacenamiento hibridos, KK-2018/00098) and EMAITEK Strategic Programs of the Basque Government. In addition, the research was undertaken as a part of ELEVATE project (EP/M009394/1) funded by the Engineering and Physical Sciences Research Council (EPSRC) and partnership with the WMG High Value Manufacturing (HVM) Catapult. Authors would like to thank the FP7 European project Batteries 2020 consortium (grant agreement No. 608936) for the valuable battery ageing data provided during the project

Remaining Useful Life Estimation Based on Asynchronous Multisource Monitoring Information Fusion

An asynchronous RUL fusion estimation algorithm is presented for the hidden degradation process with multiple asynchronous monitoring sensors based on multisource information fusion. Firstly, a state-space type model is established by modeling the stochastic degradation as a Wiener process and transforming asynchronous indirectly observations in the fusion period to the fusion time. The statistical characteristics of involved noises and their correlations are analyzed. Secondly, the estimate of the hidden degradation state is obtained by applying Kalman filtering with correlated noises to the established state-space model, where the synchronized observations are fused. Also, the unknown model parameters are recursively identified based on the Expectation-Maximization (EM) algorithm with the Generic Algorithm (GA) adopted to solve the maximization problem. Finally, the probability distribution of RUL is obtained using the fused degradation state estimation and the updated identification result of the model parameters. Simulation results show that the proposed fusion method has better performance than the RUL estimation with single sensor

Multi-Physics Modeling of Lithium-Ion Battery Electrodes

Lithium-ion batteries (LIBs) dominated the market due to their relatively high energy/power density, and long cycle life. However, a multitude of factors need to be addressed which have hindered further development of LIBs such as limited current density and safety issues. One of the effective methodologies to enhance the LIBs energy/power density is to employ alloy-based anode materials with higher theoretical capacity compared with graphite which is the common anode active material in LIBs. For instance, silicon has approximately ten times more capacity than graphite; however, intrinsic issues of silicon, such as high volume change during cycling and an unstable solid-electrolyte interphase (SEI) layer, lead to poor cyclability and cell degradation. One of the common strategies to alleviate the aforementioned silicon challenges is to use a composite graphite/silicon electrode. On one hand, experimental design and optimization of composite electrodes can be time-consuming, and in some cases, such as measuring stress evolution at the particle level of composite electrodes, unfeasible. On the other hand, incorporating multi-physics simulation can shed light on the chemo-mechanical behavior of composite electrodes and provide invaluable insights regarding lithiation-induced stress evolution and ultimately pave the path toward design and optimization of composite electrodes. Moreover, one of the main drawbacks of LIBs is safety concerns because of flammable liquid electrolytes. All-solid-state lithium-ion batteries (ASSBs) are a safer alternative to the conventional liquid electrolyte LIBs. ASSBs are based on utilizing a solid electrolyte to eliminate safety concerns such as thermal runaway and leakage of flammable liquid electrolytes. Additionally, the solid electrolyte can facilitate using high-capacity anode active materials, such as silicon and lithium plate, by inhibiting lithium dendrite formation and suppressing silicon volume expansion during the battery operation. Despite the clear advantages of ASSBs, critical challenges hinder their widespread application, including poor solid electrolyte/solid active material interfacial contact, low ionic conductivity of solid electrolytes, and poor electrochemical stability. Solid electrolyte/active material (SE/AM) interface adversly affects the performance of the ASSBs. Since the two solid phases are not perfectly in contact with each other, void spaces block the ion pathways at the SE/AM interface. Moreover, due to the solid/solid nature of this interface, lithiation-induced stress during the battery operation can cause stress peak points at the interface which leads to crack propagation within the solid electrolyte, loss of contact, and subsequently capacity fade and mechanical degradation. Therefore, ASSB microstructural investigation can enlighten the multi-physics behavior of ASSBs. Using electrode imaging techniques, such as focused ion beam-scanning electron microscopy (FIB-SEM) and X-ray computed tomography (XCT), can accurately capture the microstructures of electrodes. In particular, the XCT method is non-destructive and can provide a quantitative analysis of the electrode morphology such as particle and pore size distribution, porosity, and surface area. Moreover, the XCT reconstructed morphology can be adopted as the multi-physics simulation domain. The modeling framework in this study is comprised of an electrochemical model including conservation of mass/charge and a solid mechanics model based on the thermal-mass analogy to obtain lithiation-induced stress within the electrode microstructure. The presented work aims to adopt the 3D reconstructed morphology of the electrode to study the physical, mechanical, and electrochemical properties of LIBs. In the first study, a multiscale framework was developed and validated for a composite graphite/silicon electrode. The model is an electrochemical-solid mechanics integration used to estimate the composite electrode performance, silicon deformation, and stress evolution. The effects of silicon percentage and current on cell performance, hydrostatic stress, lithium concentration, and deformation are investigated. Considering the effect of stress on the lithium chemical potential within silicon particles in microscale modeling can shed light on the formation of a lithium concentration gradient due to the stress, and thus can enhance the composite electrode model accuracy. Moreover, physical constraints can cause the co-existence of compressive and tensile stress, while lithiation-induced stress inside the silicon particles retard the lithiation process. In fact, lithiation retardation would form a core-shell structure that comprises a lithiated shell and an unlithiated core with an incompatible strain at the interface, causing higher von Mises stress. Physical constraints highly affect the hydrostatic stress formation in silicon particles and may impact the cell life cycle due to the anisotropic swelling of particles. The developed methodology is compatible with different composite electrodes, considers the effect of active material expansion/contraction, and can pave the path for developing physics-based battery state estimation models for composite Si-based electrodes. In the second study, a synchrotron transmission X-ray microscopy tomography system has been utilized to reconstruct the 3D morphology of ASSB electrodes. The electrode was fabricated with a mixture of Li(Ni1/3Mn1/3Co1/3)O2, Li1.3Ti1.7Al0.3(PO4)3, and super-P. For the first time, a 3D numerical multi-physics model was developed to simulate the galvanostatic discharge performance of an ASSB, elucidating the spatial distribution of physical and electrochemical properties inside the electrode microstructure. The 3D model shows a wide distribution of electrochemical properties in the solid electrolyte and the active material which might have a negative effect on ASSB performance. The results show that at high current rates, the void space hinders the ions’ movement and causes local inhomogeneity in the lithium-ion distribution. The simulation results for electrodes fabricated under two pressing pressures reveal that higher pressure decreases the void spaces, leading to a more uniform distribution of lithium-ions in the SE due to more facile lithium-ion transport. The approach in this study is a key step moving forward in the design of 3D ASSBs and sheds light on the physical and electrochemical property distribution in the solid electrolyte, active material, and their interface. In the last study, a chemo-mechanical model was developed for the ASSBs’ composite electrode using the reconstructed morphologies in the second study. This study aimed to shed light on the effects of the electrode microstructure and solid electrolyte/active material interface on the stress evolution during the battery operation. The simulation results show that active material particles encounter compressive hydrostatic stress up to 4 GPa at the solid electrolyte/active material interface during lithiation while solid electrolyte limits their expansion. While, void spaces can partially accommodate active material volume expansion, and areas near void spaces have tensile stress within the range of 0-1 Gpa. Therefore, the electrode with the higher external pressing pressure experiences a relatively higher hydrostatic stress due to a higher solid electrolyte/active material interface and less void space volume fraction. In other words, although increasing the external pressing pressure may alleviate contact resistances and improve the ion pathways, it can intensify lithiation induced stress within the electrode microstructure and causes fracture formation, contact loss, and mechanical degradation. For instance, at the end of lithiation, the von Mises stress in the active material particles is approximately zero while at the surface, AM confronts up to 4.9 GPa stress and the average von Mises stress within the microstructure with higher pressing pressure is 2.4 GPa compared to 1.5 GPa. Thus, microstructural investigation of ASSBs is critical to find an optimal design to maximize the ion pathways and limit the stress evolution within an acceptable range. Integrating the developed multi-physics models with data-driven methods can decrease the computational cost and leads to a holistic modeling framework for LIBs. Incorporating the self-learning feature of data-driven methods can mimic the experimental performance of batteries and predict the behavior of batteries with high fidelity

Multiple-Phase Modeling of Degradation Signal for Condition Monitoring and Remaining Useful Life Prediction

Remaining useful life prediction plays an important role in ensuring the safety, availability, and efficiency of various engineering systems. In this paper, we propose a flexible Bayesian multiple-phase modeling approach to characterize degradation signals for prognosis. The priors are specified with a novel stochastic process and the multiple-phase model is formulated to a novel state-space model to facilitate online monitoring and prediction. A particle filtering algorithm with stratified sampling and partial Gibbs resample-move strategy is developed for online model updating and residual life prediction. The advantages of the proposed method are demonstrated through extensive numerical studies and real case studies

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

Data driven techniques for on-board performance estimation and prediction in vehicular applications.

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Advanced methodologies for reliability-based design optimization and structural health prognostics

Failures of engineered systems can lead to significant economic and societal losses. To minimize the losses, reliability must be ensured throughout the system's lifecycle in the presence of manufacturing variability and uncertain operational conditions. Many reliability-based design optimization (RBDO) techniques have been developed to ensure high reliability of engineered system design under manufacturing variability. Schedule-based maintenance, although expensive, has been a popular method to maintain highly reliable engineered systems under uncertain operational conditions. However, so far there is no cost-effective and systematic approach to ensure high reliability of engineered systems throughout their lifecycles while accounting for both the manufacturing variability and uncertain operational conditions. Inspired by an intrinsic ability of systems in ecology, economics, and other fields that is able to proactively adjust their functioning to avoid potential system failures, this dissertation attempts to adaptively manage engineered system reliability during its lifecycle by advancing two essential and co-related research areas: system RBDO and prognostics and health management (PHM). System RBDO ensures high reliability of an engineered system in the early design stage, whereas capitalizing on PHM technology enables the system to proactively avoid failures in its operation stage. Extensive literature reviews in these areas have identified four key research issues: (1) how system failure modes and their interactions can be analyzed in a statistical sense; (2) how limited data for input manufacturing variability can be used for RBDO; (3) how sensor networks can be designed to effectively monitor system health degradation under highly uncertain operational conditions; and (4) how accurate and timely remaining useful lives of systems can be predicted under highly uncertain operational conditions. To properly address these key research issues, this dissertation lays out four research thrusts in the following chapters: Chapter 3 - Complementary Intersection Method for System Reliability Analysis, Chapter 4 - Bayesian Approach to RBDO, Chapter 5 - Sensing Function Design for Structural Health Prognostics, and Chapter 6 - A Generic Framework for Structural Health Prognostics. Multiple engineering case studies are presented to demonstrate the feasibility and effectiveness of the proposed RBDO and PHM techniques for ensuring and improving the reliability of engineered systems within their lifecycles