Model-free non-invasive health assessment for battery energy storage assets

Increasing penetration of renewable energy generation in the modern power network introduces uncertainty about the energy available to maintain a balance between generation and demand due to its time-fluctuating output that is strongly dependent on the weather. With the development of energy storage technology, there is the potential for this technology to become a key element to help overcome this intermittency in a generation. However, the increasing penetration of battery energy storage within the power network introduces an additional challenge to asset owners on how to monitor and manage battery health. The accurate estimation of the health of this device is crucial in determining its reliability, power-delivering capability and ability to contribute to the operation of the whole power system. Generally, doing this requires invasive measurements or computationally expensive physics-based models, which do not scale up cost-effectively to a fleet of assets. As storage aggregation becomes more commonplace, there is a need for a health metric that will be able to predict battery health based only on the limited information available, eliminating the necessity of installation of extensive telemetry in the system. This work develops a solution to battery health prognostics by providing an alternative, a non-invasive approach to the estimation of battery health that estimates the extent to which a battery asset has been maloperated based only on the battery-operating regime imposed on the device. The model introduced in this work is based on the Hidden Markov Model, which stochastically models the battery limitations imposed by its chemistry as a combination of present and previous sequential charging actions, and articulates the preferred operating regime as a measure of health consequence. The resulting methodology is demonstrated on distribution network level electrical demand and generation data, accurately predicting maloperation under a number of battery technology scenarios. The effectiveness of the proposed battery maloperation model as a proxy for actual battery degradation for lithium-ion technology was also tested against lab tested battery degradation data, showing that the proposed health measure in terms of maloperation level reflected that measured in terms of capacity fade. The developed model can support condition monitoring and remaining useful life estimates, but in the wider context could also be used as the policy function in an automated scheduler to utilise assets while optimising their health.Increasing penetration of renewable energy generation in the modern power network introduces uncertainty about the energy available to maintain a balance between generation and demand due to its time-fluctuating output that is strongly dependent on the weather. With the development of energy storage technology, there is the potential for this technology to become a key element to help overcome this intermittency in a generation. However, the increasing penetration of battery energy storage within the power network introduces an additional challenge to asset owners on how to monitor and manage battery health. The accurate estimation of the health of this device is crucial in determining its reliability, power-delivering capability and ability to contribute to the operation of the whole power system. Generally, doing this requires invasive measurements or computationally expensive physics-based models, which do not scale up cost-effectively to a fleet of assets. As storage aggregation becomes more commonplace, there is a need for a health metric that will be able to predict battery health based only on the limited information available, eliminating the necessity of installation of extensive telemetry in the system. This work develops a solution to battery health prognostics by providing an alternative, a non-invasive approach to the estimation of battery health that estimates the extent to which a battery asset has been maloperated based only on the battery-operating regime imposed on the device. The model introduced in this work is based on the Hidden Markov Model, which stochastically models the battery limitations imposed by its chemistry as a combination of present and previous sequential charging actions, and articulates the preferred operating regime as a measure of health consequence. The resulting methodology is demonstrated on distribution network level electrical demand and generation data, accurately predicting maloperation under a number of battery technology scenarios. The effectiveness of the proposed battery maloperation model as a proxy for actual battery degradation for lithium-ion technology was also tested against lab tested battery degradation data, showing that the proposed health measure in terms of maloperation level reflected that measured in terms of capacity fade. The developed model can support condition monitoring and remaining useful life estimates, but in the wider context could also be used as the policy function in an automated scheduler to utilise assets while optimising their health

Batteries and Battery Management: Development of Low Cost, Rapid Impedance Measurement Equipment Suitable for Fast Analysis of Li-ion Cells

Batteries are a vital and integral part of modern life, installed in devices of every scale from personal portable electronic equipment to electricity grid energy storage. As people become increasingly dependent on battery energy sources, they also become increasingly reliant on accurate methods of quantifying the amount of useful energy available to them in order to function in their daily lives. Electrochemical Impedance Spectroscopy (EIS) is a branch of System Identification that can be used to identify battery metrics by which the remaining short-term (i.e. remaining charge) and long-term (battery lifetime remaining) life of a battery may be assessed. In this thesis the author gives an overview of battery chemistries and battery state estimation techniques before describing the development of a system capable of performing rapid impedance measurements. The development process spans two system designs, and results are given of impedance tests on lithium iron phosphate cells (LiFePO4) and lithium titanate (Li4Ti5O12) cells. A key feature of the method is to use Pseudo-Random Binary Sequences (PRBS) to approximate white noise, thereby providing equal stimulation of a wide band of frequencies simultaneously and reducing the required test-time. This document will be of use to those wishing to develop EIS test equipment at low-cost, and those who require a method of rapidly obtaining an impedance spectrum

Improvements in Testing and Performance of Batteries for Automotive Applications

Batteries are increasingly important in modern technologies. This is particularly true in the automotive sector, with hybrid vehicles using batteries to augment the traction power traditionally provided by the internal combustion engine. In such applications, one of the most important factors is the Dynamic Charge Acceptance (DCA) performance of the battery. This study investigates the standard method for establishing DCA performance and determines how the individual parameters of the test procedure and external factors influence the performance of lead-acid cells. This work identifies shortcomings of the standard test, which result in the true DCA performance being better than the standard test suggests. A series of modifications are proposed, which are shown to produce a more representative result. An investigation is performed to determine the effect of cell degradation on charge acceptance. This shows that the DCA test itself is not well suited to assessing the effects of degradation on DCA, and causes the results to appear worse than reality. The work also demonstrates that the usual methods of characterising degradation do not correlate well with DCA performance, and there is very little reduction in charge acceptance over the operational life of the cell. Investigations are undertaken into methods by which DCA performance may be improved. This shows that the application of ac ripple currents to batteries causes a significant increase in charge acceptance, and demonstrates how the frequency of the ripple is important in achieving the best results. This study also shows that the ripple currents have no detrimental effects on the health of the battery. Finally, the work is extended to cover lithium cells. This shows that whilst the DCA performance of lithium is more consistent, maximum charge acceptance is less than lead. It is shown that, by reducing maximum charge voltage, cycle life of cells can be extended without significant loss of stored energy

Adaptive Techniques for Estimation and Online Monitoring of Battery Energy Storage Devices

The battery management system (BMS) plays a defining role in the safety and proper operation of any battery energy storage system (BESS). Without significant advances in the state-of-the-art of BMS algorithms, the future uptake of high power/energy density battery chemistries by consumers in safety-critical applications, is not feasible. Therefore, this thesis aims to provide a coherent body of work on the enhancement of the most important tasks performed by a modern BMS, that is, the estimation and monitoring of various battery states, e.g. state-of-charge (SOC), state-of-health (SOH) and state-of-power (SOP). The Kalman Filter is an elegant set of robust equations that is often utilised by designers in modern BMS, to estimate the battery states and parameters in real time. A nonlinear version of the KF technique, namely the Extended Kalman Filter (EKF) is applied throughout this thesis to estimate the battery’s states including SOC, as well as the battery’s impedance parameters. To this end, a suitable model structure for online battery modelling and identification is selected through a comparative study of the most popular electrical equivalent-circuit battery models for real-time applications. Then, a novel improvement to the EKF-based battery parameters identification technique is made through a deterministic initialisation of the battery model parameters through a broadband system identification technique, namely the pseudorandom binary sequences (PRBS). In addition, a novel decentralised framework for the enhancement of the EKF-based SOC estimation for those lithium-ion batteries with an inherently flat open-circuit voltage (OCV) response is formulated. By combining these techniques, it is possible to develop a more reliable battery states monitoring system, which can achieve estimation errors of less than 1%. Finally, the proposed BMS algorithms in this thesis are embedded on a low-cost microprocessor hardware platform to demonstrated the usefulness of the developed EKF-based battery states estimator in a practical setting. This a significant achievement when compared to those costly BMS development platforms, such as those based on FPGAs (field-programmable gate arrays)

A practical contribution to quantitative accelerated testing of multi-failure mode products under multiple stresses

La mise en place d'un programme de tests accélérés (AT) est accompagnée de plusieurs préoccupations et incertitudes quant à l'estimation de la fiabilité qui peut causer un écart par rapport au service réel. Cette thèse vise à présenter les outils nécessaires et auxiliaires antérieurs aux tests, ainsi qu'à proposer des approches techniques et des analyses pour la mise en oeuvre de tests accélérés pour l'estimation de la fiabilité, la cornparaison de produits, l'identification des modes de défaillances critiques ainsi que la vérification de l'amélioration de la fiabilité (après modification de la conception). Tout programme de tests accélérés doit faire l'objet d'une investigation économique, de même que la similitude entre tests et modes de défaillances doit être vérifiée. L'existence de variables aléatoires dans le service en utilisant le profil et le temps de défaillance dans les tests accélérés sont les causes de l'incertitude pour estimer la fiabilité qui doit être résolu numériquement. La plupart des programmes de tests de dégradation accélérés ont été mis en oeuvre à des fins qualitatives et d'analyse de comparaison, de sorte que le concept de tests de dégradation accélérés doivent être étendus et généralisés au cas de produits sujets à de multiples modes de défaillance, avec ou sans modes de défaillance dépendants. Si des échantillons, neufs ou usagés, d'un produit sont disponibles; la méthode de vieillissement partielle est proposée afin de diminuer considérablement le temps de test

Design and Operation of Stationary Distributed Battery Micro-storage Systems

Due to some technical and environmental constraints, expanding the current electric power generation and transmission system is being challenged by even increasing the deployment of distributed renewable generation and storage systems. Energy storage can be used to store energy from utility during low-demand (off-peak) hours and deliver this energy back to the utility during high-demand (on-peak) hours. Furthermore, energy storage can be used with renewable sources to overcome some of their limitations such as their strong dependence on the weather conditions, which cannot be perfectly predicted, and their unmatched or out-of-synchronization generation peaks with the demand peaks. Generally, energy storage enhances the performance of distributed renewable sources and increases the efficiency of the entire power system. Moreover, energy storage allows for leveling the load, shaving peak demands, and furthermore, transacting power with the utility grid. This research proposes an energy management system (EMS) to manage the operation of distributed grid-tied battery micro-storage systems for stationary applications when operated with and without renewable sources. The term micro refers to the capacity of the energy storage compared to the grid capacity. The proposed management system employs four dynamic models; economic model, battery model, and load and weather forecasting models. These models, which are the main contribution of this research, are used in order to optimally control the operation of the micro-storage system (MSS) to maximize the economic return for the end-user when operated in an electricity spot market system. Chapter 1 presents an introduction to the drawbacks of the current power system, the role of energy storage in deregulated electricity markets, limitations of renewable sources, ways for participating in spot electricity markets, and an outline of the main contributions in this dissertation. In Chapter 2, some hardware design considerations for distributed micro-storage systems as well as some economic analyses are presented. Chapters 3 and 4 propose a battery management system (BMS) that handles three main functions: battery charging, state-of-charge (SOC) estimation and state-of-health (SOH) estimation. Chapter 5 proposes load and weather forecasting models using artificial neural networks (ANNs) to develop an energy management strategy to control the operation of the MSS in a spot market system when incorporated with other renewable energy sources. Finally, conclusions and future work are presented in Chapter 6

ENERGY REDUCTION IN AUTOMOTIVE PAINT SHOPS A REVIEW OF HYBRID/ELECTRIC VEHICLE BATTERY MANUFACTURING

Automotive industry is facing fundamental challenges due to the rapid depletion of fossil fuels, energy saving and environmental concerns. The need of sustainable energy development has motivated the research of energy reduction and renewable energy sources. Efficient use of energy in vehicle manufacturing is demanded, as well as an alternative energy source to replace gasoline powered engines. In this thesis, we introduce a case study at an automotive paint shop, where the largest amount of energy consumption of an automotive assembly plant takes place. Additionally, we present a summary of recent advances in the area of hybrid and electrical vehicles battery manufacturing, review commonly used battery technologies, their manufacturing processes, and related recycling and environmental issues. Our study shows that energy consumption in paint shops can be reduced substantially by selecting the appropriate repair capacity, reducing the number of repainted jobs and consuming less material and energy. Also, it is seen that considerable effort needs to be devoted to the development of batteries for hybrid and electric vehicles in the near future, which will make this area challenging and research opportunities promising

Online Identification of VLRA Battery Model Parameters Using Electrochemical Impedance Spectroscopy

This paper introduces the use of a new low-computation cost algorithm combining neural networks with the Nelder–Mead simplex method to monitor the variations of the parameters of a previously selected equivalent circuit calculated from Electrochemical Impedance Spectroscopy (EIS) corresponding to a series of battery aging experiments. These variations could be correlated with variations in the battery state over time and, therefore, identify or predict battery degradation patterns or failure modes. The authors have benchmarked four different Electrical Equivalent Circuit (EEC) parameter identification algorithms: plain neural network mapping EIS raw data to EEC parameters, Particle Swarm Optimization, Zview, and the proposed new one. In order to improve the prediction accuracy of the neural network, a data augmentation method has been proposed to improve the neural network training error. The proposed parameter identification algorithms have been compared and validated through real data obtained from a six-month aging test experiment carried out with a set of six commercial 80 Ah VLRA batteries under different cycling and temperature operation conditions.Special thanks should also be expressed to 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