Comparison of methodologies to estimate state-of-health of commercial Li-ion cells from electrochemical frequency response data

Various impedance-based and nonlinear frequency response-based methods for determining the state-of-health (SOH) of commercial lithium-ion cells are evaluated. Frequency response-based measurements provide a spectral representation of dynamics of underlying physicochemical processes in the cell, giving evidence about its internal physical state. The investigated methods can be carried out more rapidly than controlled full discharge and thus constitute prospectively more efficient measurement procedures to determine the SOH of aged lithium-ion cells. We systematically investigate direct use of electrochemical impedance spectroscopy (EIS) data, equivalent circuit fits to EIS, distribution of relaxation times analysis on EIS, and nonlinear frequency response analysis. SOH prediction models are developed by correlating key parameters of each method with conventional capacity measurement (i.e., current integration). The practical feasibility, reliability and uncertainty of each of the established SOH models are considered: all models show average RMS error in the range 0.75%–1.5% SOH units, attributable principally to cell-to-cell variation. Methods based on processed data (equivalent circuit, distribution of relaxation times) are more experimentally and numerically demanding but show lower average uncertainties and may offer more flexibility for future application

Interpretable Battery Lifetime Prediction Using Early Degradation Data

Battery lifetime prediction using early degradation data is crucial for optimizing the lifecycle management of batteries from cradle to grave, one example is the management of an increasing number of batteries at the end of their first lives at lower economic and technical risk.In this thesis, we first introduce quantile regression forests (QRF) model to provide both cycle life point prediction and range prediction with uncertainty quantified as the width of the prediction interval. Then two model-agnostic methods are employed to interpret the learned QRF model. Additionally, a machine learning pipeline is proposed to produce the best model among commonly-used machine learning models reported in the battery literature for battery cycle life early prediction. The experimental results illustrate that the QRF model provides the best range prediction performance using a relatively small lab dataset, thanks to its advantage of not assuming any specific distribution of cycle life. Moreover, the two most important input features are identified and their quantitative effect on predicted cycle life is investigated. Furthermore, a generalized capacity knee identification algorithm is developed to identify capacity knee and capacity knee-onset on the capacity fade curve. The proposed knee identification algorithm successfully identifies both the knee and knee-onset on synthetic degradation data as well as experimental degradation data of two chemistry types.In summary, the learned QRF model can facilitate decision-making under uncertainty by providing more information about cycle life prediction than single point prediction alone, for example, selecting a high-cycle-life fast-charging protocol. The two model-agnostic interpretation methods can be easily applied to other data-driven methods with the aim of identifying important features and revealing the battery degradation process. Lastly, the proposed capacity knee identification algorithm can contribute to a successful second-life battery market from multiple aspects

Investigation of different methods of online impedance spectroscopy of batteries

A key challenge in a battery energy storage system is understanding the availability and reliability of the system from the perspective of the end customer. A key task in this process is recognising when a battery or a module within a system starts to degrade and then mitigating against this using the control system or battery management system. Battery characterisation parameters such as internal impedance and state of health and state of charge of the battery are a useful representation of the battery conditions. This thesis investigates the feasibility of undertaking Electrochemical Impedance Spectroscopy (EIS) methods online to generate an understanding of battery impedance. In order to perform an EIS measurement, an excitation signal of fixed frequency must be generated and the voltage and current measured and used to calculate the impedance. This thesis proposed different methods of generating a low-frequency excitation signal using hardware found in most battery systems to extract the harmonic impedance of a battery cell to aim towards a low cost on-line impedance estimation. This work focuses on producing impedance spectroscopy measurements through the power electronics system, a battery balancing system and the earth leakage monitoring system to attempt to get comparable results to off-line EIS measurements under similar conditions. To generate an excitation signal through the power electronic circuit, different control methods were used including varying; the duty cycle, the switching frequency and the starting position of the switched wave and the addition of an impulse type function. Although utilising a variable duty cycle to generate a harmonic impedance has been previously published in literature, the other techniques analysed within this these have not previously been considered. The thesis looks at the theoretical analysis of the circuits and control techniques and then follows this up with simulation and experimental studies. The results showed that all the methods investigated have the capability to generate a low frequency perturbation signal to undertake online EIS measurement. However, there are potential trade-offs, for example increased inductor ripple current. Not all of the methods produce sufficiently accurate results experimentally .However, five of the methods were used to generate EIS plots similar to those undertaken offline

Online condition monitoring of lithium-ion and lead acid batteries for renewable energy applications

Electrochemical Impedance Spectroscopy (EIS) has been largely employed for the study of reaction kinetics and condition monitoring of batteries during different operational conditions, such as: Temperature, State of Charge (SoC) and State of Health (SoH) etc. The EIS plot translates to the impedance profile of a battery and is fitted to an Equivalent Electric Circuit (EEC) that model the physicochemical processes occurring in the batteries. To precisely monitor the condition of the batteries, Kramers-Kronig relation: linearity, stability and causality as well as the appropriate perturbation amplitude applied during EIS should be adhered to. Regardless of the accuracy of EIS, its lengthy acquisition time makes it impracticable for online measurement. Different broadband signals have been proposed in literature to shorten EIS measurement time, with different researchers favouring one technique over the other. Nonetheless, broadband signals applied to characterize a battery must be reasonably accurate, with little effect on the systems instrumentation. The major objective of this study is to explore the differences in the internal chemistries of the lithium-ion and lead acid batteries and to reduce the time associated with their condition monitoring using EIS. In this regard, this study firstly queries the methodology for EIS experiments, by investigating the optimum perturbation amplitude for EIS measurement on both the lead acid and lithium-ion batteries. Secondly, this study utilizes electrochemical equations to predict the dynamics and operational conditions associated with batteries. It also investigates the effect of different operational conditions on the lead acid and lithium-ion batteries after EEC parameters have been extracted from EIS measurements. Furthermore, different broadband excitation techniques for rapid diagnostics are explored. An online condition monitoring system is implemented through the utilization of a DC-DC converter that is used to interface the battery with the load. The online system is applied alongside the different broadband signals. The deviation in the broadband impedance spectroscopy result is compared against the Frequency Response Analyzer (FRA) to determine the most suitable technique for battery state estimation. Based on the comparisons, the adoption of a novel technique – Chirp Broadband Signal Excitation (CBSE) is proposed for online condition monitoring of batteries, as it has the advantage of being faster and precise at the most important frequency decade of the impedance spectrum of batteries

Artificial Intelligence Opportunities to Diagnose Degradation Modes for Safety Operation in Lithium Batteries

The degradation and safety study of lithium-ion batteries is becoming increasingly important given that these batteries are widely used not only in electronic devices but also in automotive vehicles. Consequently, the detection of degradation modes that could lead to safety alerts is essential. Existing methodologies are diverse, experimental based, model based, and the new trends of artificial intelligence. This review aims to analyze the existing methodologies and compare them, opening the spectrum to those based on artificial intelligence (AI). AI-based studies are increasing in number and have a wide variety of applications, but no classification, in-depth analysis, or comparison with existing methodologies is yet available

Automatic Identification Algorithm of Equivalent Electrochemical Circuit Based on Electroscopic Impedance Data for a Lead Acid Battery

Obtaining tools to analyze and predict the performance of batteries is a non-trivial challenge because it involves non-destructive evaluation procedures. At the research level, the development of sensors to allow cell-level monitoring is an innovative path, and electrochemical impedance spectrometry (EIS) has been identified as one of the most promising tools, as is the generation of advanced multivariable models that integrate environmental and internal-battery information. In this article, we describe an algorithm that automatically identifies a battery-equivalent electrochemical model based on electroscopic impedance data. This algorithm allows in operando monitoring of variations in the equivalent circuit parameters that will be used to further estimate variations in the state of health (SoH) and state of charge (SoC) of the battery based on a correlation with experimental aging data corresponding to states of failure or degradation. In the current work, the authors propose a two-step parameter identification algorithm. The first consists of a rough differential evolution algorithm-based identification. The second is based on the Nelder–Mead Simplex search method, which gives a fine parameter estimation. These algorithm results were compared with those of the commercially available Z-view, an equivalent circuit tool estimation that requires expert human input.Special thanks should also be expressed for the Torres Quevedo (PTQ) 2019 Aid from the State Research Agency, within the framework of the State Program for the Promotion of Talent and its Employability in R + D + i, Ref. PTQ2019-010787 /AEI/10.13039/501100011033