36 research outputs found

    Adaptive model-based battery management - Predicting energy and power capability

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    The battery is the limiting system for automotive electrication due to cost, size, and uncertain degradation. To be competitive the battery must therefore be used optimally. This thesis address the on-line battery management problem, with primary objectives to:(i) enable optimal usage of the battery by providing accurate estimates of its power and energy capability, while (ii) ensuring durability by keeping the battery inside predefined operating limits at all times. This means translating measurable information of current, voltage, and temperature into cell related quantities such as state-of-charge (SOC) and state-of health (SOH), and vehicle related quantities such as power capability and available energy.The main difficulty of battery management is that battery cells have complex, non-linear dynamics that changes with both operating conditions, usage history, and age. This thesis and he appended papers proposes a system of adaptive algorithms for on-line battery estimation. Several aspects are considered, from modelling and parameter estimation to estimation of SOC, energy, and power. Recursive algorithms are proposed for estimation of parameters and SOC, while power and energy are estimated using algebraic expressions derived from equivalent circuit battery models. The algorithms are evaluated on lithium-ion battery cell data collected laboratory tests. For the cell chemistries considered, the evaluation indicates that accuracy within 2% can be achieved for both SOC and power, also in cases with limited prior information about the cell

    Battery Management Systems of Electric and Hybrid Electric Vehicles

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    The topics of interest in this book include significant challenges in the BMS design of EV/HEV. The equivalent models developed for several types of integrated Li-ion batteries consider the environmental temperature and ageing effects. Different current profiles for testing the robustness of the Kalman filter type estimators of the battery state of charge are used in this book. Additionally, the BMS can integrate a real-time model-based sensor Fault Detection and Isolation (FDI) scheme for a Li-ion cell undergoing degradation, which uses the recursive least squares (RLS) method to estimate the equivalent circuit model (ECM) parameters. This book will fully meet the demands of a large community of readers and specialists working in the field due to its attractiveness and scientific content with a great openness to the side of practical applicability. This covers various interesting aspects, especially related to the characterization of commercial batteries, diagnosis and optimization of their performance, experimental testing and statistical analysis, thermal modelling, and implementation of the most suitable Kalman filter type estimators of high accuracy to estimate the state of charg

    Parametrizaci贸n de modelo de circuito equivalente de polarizaci贸n dual de una celda de ion Litio utilizando la t茅cnica de optimizaci贸n por enjambre de part铆culas modificada.

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    Context: Battery modeling is an activity that can be complex if techniques based on chemical behavior are employed. Nowadays, there are other strategies to build the modeling as inverse modeling based on experimental curves and adjustments of circuit models. There are different techniques to parameterize the battery modeling based on their complexity, accuracy, and convergence time. Method: In this paper is used a particle swarm optimization algorithm to parameterize a dual-polarization model for a 18650-lithium cell. The proposed methodology divides the problem into different optimization cases and proposes a localized search strategy based on the experience of the previous particle. Results: The PSO algorithm allows adjusting the model parameters for each case analyzed. Problem division by stages allows improving the global precision while reducing the convergence times of the algorithm. From possible cases, it is possible to find the dynamics of each of the parameters as a function of the state of charge. Conclusions: The proposed methodology allows reducing the parameterization times of the dual-polarization model. Due to the approximation generated by the previous experiences, it is possible to decrease the number of the swarm population and further decrease the convergence time of the process. Additionally, the methodology can be used with different optimization algorithms.Contexto: El modelado de bater铆as es una actividad que puede ser compleja si se utilizan t茅cnicas basadas en el comportamiento qu铆mico, para facilitar esto se han utilizado estrategias de modelo inverso que se basan en curvas experimentales y ajustes de modelos circuitales. Para la parametrizaci贸n se utilizan diferentes t茅cnicas que radican en su complejidad, exactitud y tiempo de convergencia. M茅todo: En este trabajo se utiliza un algoritmo de optimizaci贸n por enjambre de part铆culas para la parametrizaci贸n de un modelo de polarizaci贸n dual para una celda de ion litio de tipo 18650. La metodolog铆a propuesta divide el problema en diferentes casos de optimizaci贸n y propone una estrategia de b煤squeda localizada basada en la experiencia del caso anterior. Resultados: El algoritmo PSO permite ajustar los par谩metros del modelo para cada uno de los casos analizados. La divisi贸n del problema por casos permite mejorar la precisi贸n global del problema y a su vez disminuir los tiempos de convergencia del algoritmo. A partir de los posibles casos se puede encontrar la din谩mica de cada uno de los par谩metros en funci贸n del estado de carga. Conclusiones: La metodolog铆a propuesta permite reducir los tiempos de parametrizaci贸n del modelo de polarizaci贸n dual. Debido a la aproximaci贸n generada por las experiencias anteriores, es posible disminuir el n煤mero de la poblaci贸n del enjambre y disminuir a煤n m谩s el tiempo de convergencia del proceso. Adicionalmente, la metodolog铆a puede ser utilizada con diferentes algoritmos de optimizaci贸n

    Power Electronics and Energy Management for Battery Storage Systems

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    The deployment of distributed renewable generation and e-mobility systems is creating a demand for improved dynamic performance, flexibility, and resilience in electrical grids. Various energy storages, such as stationary and electric vehicle batteries, together with power electronic interfaces, will play a key role in addressing these requests thanks to their enhanced functionality, fast response times, and configuration flexibility. For the large-scale implementation of this technology, the associated enabling developments are becoming of paramount importance. These include energy management algorithms; optimal sizing and coordinated control strategies of different storage technologies, including e-mobility storage; power electronic converters for interfacing renewables and battery systems, which allow for advanced interactions with the grid; and increase in round-trip efficiencies by means of advanced materials, components, and algorithms. This Special Issue contains the developments that have been published b researchers in the areas of power electronics, energy management and battery storage. A range of potential solutions to the existing barriers is presented, aiming to make the most out of these emerging technologies

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

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    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鈥檚 states including SOC, as well as the battery鈥檚 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)

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

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    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

    Advanced state of charge estimation for lithium-sulfur batteries.

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    Lithium-sulfur (Li-S) batteries have a high theoretical energy density, which could outperform classic Li-ion technology in weight, manufacturing costs, safety and environmental impact. The aim of this study is to extend the research around Li-S through practical applications, specifically to develop a Li-S battery state of charge (SoC) estimation in the environment of electrical vehicles. This thesis is written in paper based form and is organised into three main areas. Part I introduces general topic of vehicle electrification, the framework of the research project REVB, mechanisms of Li-S cells and techniques for SoC estimation. The major scientific contribution is given in Part II within three studies in paper-based form. In Paper 1, a simple and fast running equivalent circuit network discharge model for Li-S cells over different temperature levels is presented. Paper 2 uses the model as an observer for Kalman filter (KF) based SoC estimation, employing and comparing the extended Kalman filter, the unscented Kalman filter and the Particle filter. Generally, a robust Li-S cell SoC estimator could be realized for realistic scenarios. To improve the robustness of the SoC estimation with different current densities, in Paper 3 a fast running online parameter identification method is applied, which could be used to improve the battery model as well as the SoC estimation precision. In Part III, the results are discussed and future directions are given to improve the SoC estimation accuracy for a wider range of applications and conditions. The final conclusion of this work is that a robust Li-S cell SoC estimation can be achieved with Kalman filter types of algorithms. Amongst the approaches of this study, the online parameter identification approach could deliver the best results and also contains most potential for further improvement

    Battery Systems and Energy Storage beyond 2020

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    Currently, the transition from using the combustion engine to electrified vehicles is a matter of time and drives the demand for compact, high-energy-density rechargeable lithium ion batteries as well as for large stationary batteries to buffer solar and wind energy. The future challenges, e.g., the decarbonization of the CO2-intensive transportation sector, will push the need for such batteries even more. The cost of lithium ion batteries has become competitive in the last few years, and lithium ion batteries are expected to dominate the battery market in the next decade. However, despite remarkable progress, there is still a strong need for improvements in the performance of lithium ion batteries. Further improvements are not only expected in the field of electrochemistry but can also be readily achieved by improved manufacturing methods, diagnostic algorithms, lifetime prediction methods, the implementation of artificial intelligence, and digital twins. Therefore, this Special Issue addresses the progress in battery and energy storage development by covering areas that have been less focused on, such as digitalization, advanced cell production, modeling, and prediction aspects in concordance with progress in new materials and pack design solutions
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