Modelling and estimation of vanadium redox flow batteries: a review

Redox flow batteries are one of the most promising technologies for large-scale energy storage, especially in applications based on renewable energies. In this context, considerable efforts have been made in the last few years to overcome the limitations and optimise the performance of this technology, aiming to make it commercially competitive. From the monitoring point of view, one of the biggest challenges is the estimation of the system internal states, such as the state of charge and the state of health, given the complexity of obtaining such information directly from experimental measures. Therefore, many proposals have been recently developed to get rid of such inconvenient measurements and, instead, utilise an algorithm that makes use of a mathematical model in order to rely only on easily measurable variables such as the system’s voltage and current. This review provides a comprehensive study of the different types of dynamic models available in the literature, together with an analysis of the existing model-based estimation strategies. Finally, a discussion about the remaining challenges and possible future research lines on this field is presented.The research that gave rise to these results received support from “la Caixa” Foundation (ID 100010434. Fellowship code LCF/BQ/DI21/11860023) , the CSIC program for the Spanish Recovery, Transformation and Resilience Plan funded by the Recovery and Resilience Facility of the European Union, established by the Regulation (EU) 2020/2094, CSIC Interdisciplinary Thematic Platform (PTI+) Transición Energética Sostenible+ (PTI-TRANSENER+ project TRE2103000), the Spanish Ministry of Science and Innovation (project PID2021-126001OB-C31 funded by MCIN/AEI/10.13039/501100011033 / ERDF,EU) and the Spanish Ministry of Economy and Competitiveness under Project DOVELAR (ref. RTI2018-096001-B-C32).Peer ReviewedPostprint (published version

Control of voltage source converters connected to variable impedance grids

The increase in new renewable energy resources is key to achieving carbon reduction targets, however it also introduces new grid integration challenges. The best renewable resource in Scotland is found in remote parts of the country, and as a result new renewable based generation is increasingly subjected to high and variable levels of impedance. Impedances that cause resonances are also increasingly common, given the higher order characteristics of impedance when transformers, filters, subsea cables, compensators and so on are present in the network. For a better understanding of impedance related stability issues, the estimation of the grid impedance using both Thévenin equivalent and wide spectrum techniques is studied in this thesis and integrated into the converter’s control. These estimations inform the controller of the grid conditions, allowing for controller adaptation. In instances where weak grid conditions are severe and the local grid impedance is dominant, a disturbance rejection mechanism called the pre-emptive voltage decoupler (PVD) is proposed. The PVD feeds forward the active current reference and measured voltage, and adapts the reactive current reference as a function of the impedance estimation, to pre-emptively compensate the local voltage for changes in active power transfer. This is justified through small signal analysis using linearised state space models and validated in the laboratory using large inductors and a converter. The control is also made more resilient with an instability detector, proposed to prevent instability when significant grid disturbances occur. Through early detection of sudden power angle changes, stability can be maintained. This is achieved by momentarily reducing the power reference and re-establishing grid parameters. The implementation of the proposed changes improves the steady state stability region from -0.75 – 0.55 pu to -0.85 – 0.75 pu. Further, the nonlinear transient performance is much more resilient, and uninterrupted power flow can be maintained. When the local grid is not dominant, and higher order grid impedances cause undesired resonances, a detection of the resonant frequency allows for an adaptation of the outer loop gains, thus damping the resonances and improving stability. Such grids are also prone to instability, but a reduction of the power reference does not improve stability, on the contrary the reduction of the power reference shifts eigenvalues into the right hand plane. A better preventative measure is to reduce the outer loop gains, and once the frequency of the problematic resonances is identified, final decisions on outer loop tuning can be taken. With this implementation, the stability of the system is maintained and the power output can be recovered within about 1 second.The increase in new renewable energy resources is key to achieving carbon reduction targets, however it also introduces new grid integration challenges. The best renewable resource in Scotland is found in remote parts of the country, and as a result new renewable based generation is increasingly subjected to high and variable levels of impedance. Impedances that cause resonances are also increasingly common, given the higher order characteristics of impedance when transformers, filters, subsea cables, compensators and so on are present in the network. For a better understanding of impedance related stability issues, the estimation of the grid impedance using both Thévenin equivalent and wide spectrum techniques is studied in this thesis and integrated into the converter’s control. These estimations inform the controller of the grid conditions, allowing for controller adaptation. In instances where weak grid conditions are severe and the local grid impedance is dominant, a disturbance rejection mechanism called the pre-emptive voltage decoupler (PVD) is proposed. The PVD feeds forward the active current reference and measured voltage, and adapts the reactive current reference as a function of the impedance estimation, to pre-emptively compensate the local voltage for changes in active power transfer. This is justified through small signal analysis using linearised state space models and validated in the laboratory using large inductors and a converter. The control is also made more resilient with an instability detector, proposed to prevent instability when significant grid disturbances occur. Through early detection of sudden power angle changes, stability can be maintained. This is achieved by momentarily reducing the power reference and re-establishing grid parameters. The implementation of the proposed changes improves the steady state stability region from -0.75 – 0.55 pu to -0.85 – 0.75 pu. Further, the nonlinear transient performance is much more resilient, and uninterrupted power flow can be maintained. When the local grid is not dominant, and higher order grid impedances cause undesired resonances, a detection of the resonant frequency allows for an adaptation of the outer loop gains, thus damping the resonances and improving stability. Such grids are also prone to instability, but a reduction of the power reference does not improve stability, on the contrary the reduction of the power reference shifts eigenvalues into the right hand plane. A better preventative measure is to reduce the outer loop gains, and once the frequency of the problematic resonances is identified, final decisions on outer loop tuning can be taken. With this implementation, the stability of the system is maintained and the power output can be recovered within about 1 second

Machine Learning assisted Digital Twin for event identification in electrical power system

The challenges of stable operation in the electrical power system are increasing with the infrastructure shifting of the power grid from the centralized energy supply with fossil fuels towards sustainable energy generation. The predominantly RES plants, due to the non-linear electronic switch, have brought harmonic oscillations into the power grid. These changes lead to difficulties in stable operation, reduction of outages and management of variations in electric power systems. The emergence of the Digital Twin in the power system brings the opportunity to overcome these challenges. Digital Twin is a digital information model that accurately represents the state of every asset in a physical system. It can be used not only to monitor the operation states with actionable insights of physical components to drive optimized operation but also to generate abundant data by simulation according to the guidance on design limits of physical systems. The work addresses the topic of the origin of the Digital Twin concept and how it can be utilized in the optimization of power grid operation.Die Herausforderungen für den zuverfässigen Betrieb des elektrischen Energiesystems werden mit der Umwandlung der Infrastruktur in Stromnetz von der zentralen Energieversorgung mit fossilen Brennstoffen hin zu der regenerativen Energieeinspeisung stetig zugenommen. Der Ausbau der erneuerbaren Energien im Zuge der klimapolitischen Zielsetzung zur CO²-Reduzierung und des Ausstiegs aus der Kernenergie wird in Deutschland zügig vorangetrieben. Aufgrund der nichtlinearen elektronischen Schaltanlagen werden die aus EE-Anlagen hervorgegangenen Oberschwingungen in das Stromnetz eingebracht, was nicht nur die Komplexität des Stromnetzes erhöht, sondern auch die Stabilität des Systems beeinflusst. Diese Entwicklungen erschweren den stabilen Betrieb, die Verringerung der Ausfälle und das Management der Netzschwankungen im elektrischen Energiesystem. Das Auftauchen von Digital Twin bringt die Gelegenheit zur Behebung dieser Herausforderung. Digital Twin ist ein digitales Informationsmodell, das den Zustand des physikalischen genau abbildet. Es kann nicht nur zur Überwachung der Betriebszustände mit nachvollziehbarem Einsichten über physischen Komponenten sondern auch zur Generierung der Daten durch Simulationen unter der Berücksichtigung der Auslegungsgrenze verwendet werden. Diesbezüglich widmet sich die Arbeit zunächste der Fragestellung, woher das Digital Twin Konzept stammt und wie das Digitan Twin für die Optimierung des Stromnetzes eingesetzt wird. Hierfür werden die Perspektiven über die dynamische Zustandsschätzung, die Überwachung des des Betriebszustands, die Erkennung der Anomalien usw. im Stromnetz mit Digital Twin spezifiziert. Dementsprechend wird die Umsetzung dieser Applikationen auf dem Lebenszyklus-Management basiert. Im Rahmen des Lebenszyklusschemas von Digital Twin sind drei wesentliche Verfahren von der Modellierung des Digital Twins zur deren Applizierung erforderlich: Parametrierungsprozess für die Modellierung des Digital Twins, Datengenerierung mit Digital Twin Simulation und Anwendung mit Machine Learning Algorithmus für die Erkennung der Anomalie. Die Validierung der Zuverlässigkeit der Parametrierung für Digital Twin und der Eventserkennung erfolgt mittels numerischer Fallstudien. Dazu werden die Algorithmen für Online und Offline zur Parametrierung des Digital Twins untersucht. Im Rahmen dieser Arbeit wird das auf CIGRÉ basierende Referenznetz zur Abbildung des Digital Twin hinsichtlich der Referenzmessdaten parametriert. So sind neben der Synchronmaschine und Umrichter basierende Einspeisung sowie Erreger und Turbine auch regler von Umrichter für den Parametrierungsprozess berücksichtigt. Nach der Validierung des Digital Twins werden die zahlreichen Simulationen zur Datengenerierung durchgeführt. Jedes Event wird mittels der Daten vo Digital Twin mit einem "Fingerprint" erfasst. Das Training des Machine Learning Algorithmus wird dazu mit den simulierten Daten von Digital Twin abgewickelt. Das Erkennungsergebnis wird durch die Fallstudien validiert und bewertet

Power-Performance Modeling and Adaptive Management of Heterogeneous Mobile Platforms​

abstract: Nearly 60% of the world population uses a mobile phone, which is typically powered by a system-on-chip (SoC). While the mobile platform capabilities range widely, responsiveness, long battery life and reliability are common design concerns that are crucial to remain competitive. Consequently, state-of-the-art mobile platforms have become highly heterogeneous by combining a powerful SoC with numerous other resources, including display, memory, power management IC, battery and wireless modems. Furthermore, the SoC itself is a heterogeneous resource that integrates many processing elements, such as CPU cores, GPU, video, image, and audio processors. Therefore, CPU cores do not dominate the platform power consumption under many application scenarios. Competitive performance requires higher operating frequency, and leads to larger power consumption. In turn, power consumption increases the junction and skin temperatures, which have adverse effects on the device reliability and user experience. As a result, allocating the power budget among the major platform resources and temperature control have become fundamental consideration for mobile platforms. Dynamic thermal and power management algorithms address this problem by putting a subset of the processing elements or shared resources to sleep states, or throttling their frequencies. However, an adhoc approach could easily cripple the performance, if it slows down the performance-critical processing element. Furthermore, mobile platforms run a wide range of applications with time varying workload characteristics, unlike early generations, which supported only limited functionality. As a result, there is a need for adaptive power and performance management approaches that consider the platform as a whole, rather than focusing on a subset. Towards this need, our specific contributions include (a) a framework to dynamically select the Pareto-optimal frequency and active cores for the heterogeneous CPUs, such as ARM big.Little architecture, (b) a dynamic power budgeting approach for allocating optimal power consumption to the CPU and GPU using performance sensitivity models for each PE, (c) an adaptive GPU frame time sensitivity prediction model to aid power management algorithms, and (d) an online learning algorithm that constructs adaptive run-time models for non-stationary workloads.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Nonlinear self-tuning control for power oscillation damping

Power systems exhibit nonlinear behavior especially during disturbances, necessitating the application of appropriate nonlinear control techniques. Lack of availability of accurate and updated models for the whole power system adds to the challenge. Conventional damping control design approaches consider a single operating condition of the system, which are obviously simple but tend to lack performance robustness. Objective of this research work is to design a measurement based self-tuning controller, which does not rely on accurate models and deals with nonlinearities in system response. Designed controller is required to ensure settling of inter-area oscillations within 10−12s, following disturbance such as a line outage. The neural network (NN) model is illustrated for the representation of nonlinear power systems. An optimization based algorithm, Levenberg-Marquardt (LM), for online estimation of power system dynamic behavior is proposed in batch mode to improve the model estimation. Careful study shows that the LM algorithm yields better closed loop performance, compared to conventional recursive least square (RLS) approach with the pole-shifting controller (PSC) in linear framework. Exploiting the capability of LM, a special form of neural network compatible with feedback linearization technique, is applied. Validation of the performance of proposed algorithm is done through the modeling and simulating heavy loading of transmission lines, when the nonlinearities are pronounced. Nonlinear NN model in the Feedback Linearization (FLNN) form gives better estimation than the autoregressive with an external input (ARX) form. The proposed identifier (FLNN with LM algorithm) is then tested on a 4−machine, 2−area power system in conjunction with the feedback linearization controller (FBLC) under varying operating conditions. This case study indicates that the developed closed loop strategy performs better than the linear NN with PSC. Extension of FLNN with FBLC structure in a multi-variable setup is also done. LM algorithm is successfully employed with the multi-input multi-output FLNN structure in a sliding window batch mode, and FBLC controller generates multiple control signals for FACTS. Case studies on a large scale 16−machine, 5−area power system are reported for different power flow scenarios, to prove the superiority of proposed schemes: both MIMO and MISO against a conventional model based controller. A coefficient vector for FBLC is derived, and utilized online at each time instant, to enhance the damping performance of controller, transforming into a time varying controller

Modeling and State Estimation of Lithium-Ion Battery Packs for Application in Battery Management Systems

As lithium-ion (Li-Ion) battery packs grow in popularity, so do the concerns of its safety, reliability, and cost. An efficient and robust battery management system (BMS) can help ease these concerns. By measuring the voltage, temperature, and current for each cell, the BMS can balance the battery pack, and ensure it is operating within the safety limits. In addition, these measurements can be used to estimate the remaining charge in the battery (state-of-charge (SOC)) and determine the health of the battery (state-of-health (SOH)). Accurate estimation of these battery and system variables can help improve the safety and reliability of the energy storage system (ESS). This research aims to develop high-fidelity battery models and robust SOC and SOH algorithms that have low computational cost and require minimal training data. More specifically, this work will focus on SOC and SOH estimation at the pack-level, as well as modeling and simulation of a battery pack. An accurate and computationally efficient Li-Ion battery model can be highly beneficial when developing SOC and SOH algorithms on the BMS. These models allow for software-in-the-loop (SIL) and hardware-in-the-loop (HIL) testing, where the battery pack is simulated in software. However, development of these battery models can be time-consuming, especially when trying to model the effect of temperature and SOC on the equivalent circuit model (ECM) parameters. Estimation of this relationship is often accomplished by carrying out a large number of experiments, which can be too costly for many BMS manufacturers. Therefore, the first contribution of this research is to develop a comprehensive battery model, where the ECM parameter surface is generated using a set of carefully designed experiments. This technique is compared with existing approaches from literature, and it is shown that by using the proposed method, the same degree of accuracy can be obtained while requiring significantly less experimental runs. This can be advantageous for BMS manufacturers that require a high-fidelity model but cannot afford to carry out a large number of experiments. Once a comprehensive model has been developed for SIL and HIL testing, research was carried out in advancing SOH and SOC algorithms. With respect to SOH, research was conducted in developing a steady and reliable SOH metric that can be determined at the cell level and is stable at different battery operating conditions. To meet these requirements, a moving window direct resistance estimation (DRE) algorithm is utilized, where the resistance is estimated only when the battery experiences rapid current transients. The DRE approach is then compared with more advanced resistance estimation techniques such as extended Kalman filter (EKF) and recursive least squares (RLS). It is shown that by using the proposed algorithm, the same degree of accuracy can be achieved as the more advanced methods. The DRE algorithm does, however, have a much lower computational complexity and therefore, can be implemented on a battery pack composed of hundreds of cells. Research has also been conducted in converting these raw resistance values into a stable SOH metric. First, an outlier removal technique is proposed for removing any outliers in the resistance estimates; specifically, outliers that are an artifact of the sampling rate. The technique involves using an adaptive control chart, where the bounds on the control chart change as the internal resistance of the battery varies during operation. An exponentially weighted moving average (EWMA) is then applied to filter out the noise present in the raw estimates. Finally, the resistance values are filtered once more based on temperature and battery SOC. This additional filtering ensures that the SOH value is independent of the battery operating conditions. The proposed SOH framework was validated over a 27-day period for a lithium iron phosphate (LFP) battery. The results show an accurate estimation of battery resistance over time with a mean error of 1.1% as well as a stable SOH metric. The findings are significant for BMS developers who have limited computational resources but still require a robust and reliable SOH algorithm. Concerning SOC, most publications in literature examine SOC estimation at the cell level. Determining the SOC for a battery pack can be challenging, especially an estimate that behaves logically to the battery user. This work proposes a three-level approach, where the final output from the algorithm is a well-behaved pack SOC estimate. The first level utilizes an EKF for estimating SOC while an RLS approach is used to adapt the model parameters. To reduce computational time, both algorithms will be executed on two specific cells: the first cell to charge to full and the first cell to discharge to empty. The second level consists of using the SOC estimates from these two cells and estimating a pack SOC value. Finally, a novel adaptive coulomb counting approach is proposed to ensure the pack SOC estimate behaves logically. The accuracy of the algorithm is tested using a 40 Ah Li-Ion battery. The results show that the algorithm produces accurate and stable SOC estimates. Finally, this work extends the developed comprehensive battery model to examine the effect of replacing damaged cells in a battery pack with new ones. The cells within the battery pack vary stochastically, and the performance of the entire pack is evaluated under different conditions. The results show that by changing out cells in the battery pack, the SOH of the pack can be maintained indefinitely above a specific threshold value. In situations where the cells are checked for replacement at discrete intervals, referred to as maintenance event intervals, it is found that the length of the interval is dependent on the mean time to failure of the individual cells. The simulation framework, as well as the results from this paper, can be utilized to better optimize Li-ion battery pack design in electric vehicles (EVs) and make long-term deployment of EVs more economically feasible