Digital twin-driven estimation of state of charge for Li-ion battery

Under the net-zero carbon transition, lithium-ion batteries (LIB) plays a critical role in supporting the connection of more renewable power generation, increasing grid resiliency and creating more flexible energy systems. However, poor useful life and relatively high cost of batteries result in barriers that hinder the wider adoption of battery technologies e.g., renewable resources storage. Furthermore, the useful life of a battery is significantly affected by the materials composition, system design and operating conditions, hence, made the control and management of battery systems more challenging. Digitalisation and artificial intelligence (AI) offer an opportunity to establish a battery digital twin that has great potentials to improve the situational awareness of battery management systems and enable the optimal operation of battery storage units. An accurate estimation of the state of charge (SOC) can indicate the battery's status, provide valuable information for maintenance and maximise its useful life. In this paper, a digital twin-driven framework based on a hybrid model that connects LSTM (long short-term memory) and EKF (extended Kalman filter) has been proposed to estimate the SOC of a li-ion battery. LSTM provides more accurate initial SOC estimations and impedance model data to EKF. According to experimental results, the developed battery digital twin is considered less dependent on the initial SOC conditions and is deemed more robust compared to traditional means with a lower RMSE (root mean squared error)

Algorithme d'estimation d'état de charge pour système de gestion de batterie avancée

La popularité des véhicules électriques a grandement progressé durant les dernières années. Cependant, ceux-ci font encore face à certaines problématiques qui nuisent à leur adoption rapide comme l’autonomie et le coût. L'utilisation de batterie lithium-ion dans les véhicules électriques nécessite un système électronique et un algorithme embarqué de gestion du bloc batterie. Le système de gestion de batterie a comme tâche d'estimer son état de charge ou en d’autres mots d'estimer l'énergie stockée dans la batterie en temps réel. L'état de charge est une donnée très importante, car elle permet d'estimer le nombre de kilomètres pouvant être parcouru par le véhicule. La précision de cet algorithme est primordiale pour donner à l'utilisateur du véhicule une meilleure estimation de son autonomie et ainsi il pourra mieux planifier le moment de la prochaine recharge et éviter de tomber en panne. Ce projet propose d'une part de développer un système de validation Hardware-in-the-loop (HIL) qui servira à valider les performances de l'algorithme développé et ensuite programmé dans un système de gestion de batterie. Ce système permettra de répliquer fidèlement le comportement d’une batterie en opération à partir d’un modèle électrique équivalent en émulant les signaux électriques requis par le système de gestion de batterie (BMS). Le système HIL permet de tester les algorithmes d'estimation d'état de charge en répliquant des situations réelles et assure la répétabilité des tests ce qui est très difficile et dispendieux à obtenir lors de test avec de vraies batteries. De plus, un algorithme issu du domaine du contrôle avancé a été développé et nouvellement utilisé pour estimer l'état de charge de la batterie. L'algorithme basé sur des observateurs de perturbations (Disturbance Observer (DOB)) a été couplé à un modèle mathématique de batterie afin d'estimer l'état de charge avec une grande précision. Les performances de cet algorithme ont été comparées à d'autres algorithmes présents dans la littérature telle que le filtre de Kalman étendue et un BMS commercial. Des résultats de simulation et de validation avec le système HIL sont présentés afin de démontrer le grand potentiel de ce nouvel algorithme d'estimation parfaitement adapté à être implémenté sur des microcontrôleurs utilisés dans l’industrie automobile.Abstract: The popularity of the electric vehicle has increased in the last years. However, they face some issues like range and cost, that affect the massive adoption by the population. Lithiumion battery in an electric vehicle needs an electronic circuit and an embedded algorithm for the battery management. The battery management system has to estimate in real-time the state-of-charge (SoC) or in other words, the remaining energy stored in the battery. The state-of-charge estimation is crucial information because it will be used to estimate the remaining mileage that can be traveled by the vehicle. The precision of the algorithm is upmost important to have a better estimation of electric vehicle range, also help to plan the best moment to recharge the vehicle and avoid to run out energy. This project proposes to develop an Hardware-in-the-loop system (HIL) for performance validation of algorithms programed into the battery management system. This system allows replicating the battery behavior accordingly to an equivalent circuit model by emulating the electrical signals required by the battery management system (BMS). The HIL facilitates the testing of SoC algorithms by replicating real scenarios and ensure the tests repeatability that is very difficult to obtain during tests with real batteries. In addition, an algorithm from the advanced control field has been developed and newly used to estimate the SoC of the battery. The Disturbance Observer algorithm has been merged to a mathematical model of the battery to estimate the SoC accurately. The performance of this algorithm has been compared to other algorithms present in the literature like extended Kalman filter and a commercial BMS. Simulation results and validation by HIL are presented to demonstrate the great potential of this novel estimation algorithm. This new algorithm is perfectly adapted to be implemented into automotive industry microcontrollers

A Systematic Framework for Battery Performance Estimation Considering Model and Parameter Uncertainties

Up to date, model and parameter uncertainties are generally overlooked by majority of researchers in the field of battery diagnostics and prognostics. As a consequence, accuracy of the battery performance estimation is dominated by the model fidelity and may vary from cell-to-cell. This paper proposes a systematic framework to quantify battery model and parameter uncertainties for more effective battery performance estimation. Such a framework is generally applicable for estimating various battery performances of interest (e.g. state of charge (SOC), capacity, and power capability). Case studies for battery SOC estimation are conducted to demonstrate the effectiveness of the proposed framework

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)

Battery Systems and Energy Storage beyond 2020

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

Optimal Control of Hybrid Systems and Renewable Energies

This book is a collection of papers covering various aspects of the optimal control of power and energy production from renewable resources (wind, PV, biomass, hydrogen, etc.). In particular, attention is focused both on the optimal control of new technologies and on their integration in buildings, microgrids, and energy markets. The examples presented in this book are among the most promising technologies for satisfying an increasing share of thermal and electrical demands with renewable sources: from solar cooling plants to offshore wind generation; hybrid plants, combining traditional and renewable sources, are also considered, as well as traditional and innovative storage systems. Innovative solutions for transportation systems are also explored for both railway infrastructures and advanced light rail vehicles. The optimization and control of new solutions for the power network are addressed in detail: specifically, special attention is paid to microgrids as new paradigms for distribution networks, but also in other applications (e.g., shipboards). Finally, optimization and simulation models within SCADA and energy management systems are considered. This book is intended for engineers, researchers, and practitioners that work in the field of energy, smart grid, renewable resources, and their optimization and control

Development of automated and connected testing processes for electric vehicles

Electric vehicles provide a practical transportation solution to overcome emission and energy deficiencies posed by combustion vehicles. However, high product costs driven by the price of components and immaturity of the processes to create them reduce the product’s financial competitiveness. Manufacturers need to adapt their processes to develop cars more economically while adhering to emission requirements by legislative bodies. This EngD determined the estimated R&D cost saving made through innovating automated and connected technologies into the development process to reduce the development costs of vehicles holistically. The research targeted physical testing costs due to the potential increase in demand for testing to improve the characterisation of virtual models while the automotive industry transitions to vehicle electrification. The research established objectives to target human, capital and facility costs as significant cost drivers for physical testing. Three applications of automation and connected systems were ideated and investigated to evaluate the saving potential of each cost driver. Firstly, an automated dynamometer was designed and experimentally tested to demonstrate its capability in reducing man-hours for powertrain component testing. Secondly, a distributed test network was virtually modelled to understand the opportunities to supplement physical prototype vehicles by utilising connected component test facilities. Finally, an automated test management system with test case generation capability was proposed and evaluated to determine its capability to improve testing productivity. Using the results from each technology innovation and Jaguar Land Rover’s historical strategy, a numerical model identified an estimated saving of £225m across 12 vehicle models representing a net change of 1.71%. Changes in human resources demand were the most significant contributor toward total development cost savings. DTS and automated dynamometer innovations provided 90% and 9% of human resource cost-saving, respectively. The results suggested that these technological innovations would make only a marginal impact on saving for customers. Ultimately, a combination of further developing of these technologies to maximise application and saving made on other portions of the vehicle development process is necessary to bridge the gap between combustion and electric vehicle. However, the savings proposed would benefit manufacturers financially and allow them to also gain additional revenue by providing opportunities to release vehicle models marginally earlier

Techno-economic evaluation of battery storage systems in industry

In the context of a changing energy system towards one dominated by renewable energy sources, the demand for flexible energy generation and consumption will increase. Battery storage systems can provide a significant share of this energy flexibility, especially when combined with an industrial manufacturing plant to shift the industrial electricity demand over time. This paper contributes to a better understanding of the business decision when investing in a battery storage system and when marketing energy flexibility. For this purpose, the work considers the techno-economic and regulatory framework for flexibility measures and examines the optimal investment and dispatch planning for a battery storage system in an industrial company. The studies in this thesis focus on three central aspects. As a first aspect, the various revenue streams for the stored electricity are analysed and how these influence the profitability of a battery storage system. In particular, the provision of frequency containment reserve power, peak load shifting or peak shaving, arbitrage trading on the energy markets and the increase in self-consumption through photovoltaic self-generation are addressed. For this purpose, an optimisation model is formulated as a discrete, linear programme that maps the economic framework of the flexibility markets and integrates the technological constraints of the battery storage system. As a second aspect, uncertainties about market prices, load and generation behaviour are integrated into the optimisation model and the influence on the investment decision is investigated. This is done on the one hand by a two-stage robust optimisation model, which represents the uncertainty about the market success on the intraday market. On the other hand, the significance of the sequence of uncertain market decisions is illuminated through a multi-stage stochastic optimisation model. As a third aspect, the trade-off between the economic and ecological use of a battery storage system is analysed. For this purpose, an ecological, CO₂-minimal dispatch is calculated by deriving national CO₂-emission factors and compared with an economically optimal dispatch. The case studies are analysed based on real industrial load data from small, medium and large enterprises. The thesis discusses the technical and economic framework conditions, with the main focus on Germany. However, a comparison between the countries Germany, Denmark, and Croatia is also presented. The results show that peak shaving and the provision of frequency containment reserve are complementary and make the investment in a battery storage system economically viable. Self-generation through a photovoltaic system can reduce the risk arising from uncertain energy market prices. However, the sequence of uncertain decisions has a significant impact on the design of the battery storage system. Economically feasible operation through arbitrage trading, on the other hand, is not possible due to the small price differences in the markets and limitations due to battery ageing and efficiency. These battery characteristics also influence the use of a battery storage system for CO₂-reduction. Due to the limited number of cycles and relatively high charging losses, battery technology is currently unsuitable for CO₂-minimal storage use. Nevertheless, the economic and ecological potential of battery storage systems strongly depends on individual factors such as local grid charges, the selected battery technology and the individual industrial load profile. Advances in battery technology, such as increased lifetime, and possible new flexibility markets, such as dynamic grid charges, offer new application and marketing opportunities that could increase the economic viability of a battery storage system

Projeção e Análise comparativa das emissões de veículos elétricos até 2050: um modelo de dinâmica de sistemas

TCC(graduação) - Universidade Federal de Santa Catarina. Centro Tecnológico. Engenharia de Produção ElétricaDadas as preocupações de agentes governamentais internacionais com a redução das emissões de gases de efeito estufa, nas atividades industriais e particularmente no setor de transportes, faz-se recorrente o incentivo à difusão de tecnologias ditas limpas, isto é, menos poluentes. Esse é o caso dos veículos elétricos e híbridos, os quais, respectivamente, eliminam ou reduzem as emissões locais de escapamento. No caso dos veículos elétricos a bateria (BEV), o potencial de redução de emissões é ainda mais alto, sobretudo em países com a matriz elétrica predominantemente renovável, tal qual a matriz brasileira. Contudo, ainda existem incertezas quanto ao tipo de veículo elétrico mais adequado à realidade do país, considerando particularmente as emissões equivalentes de gases de efeito estufa (GEE). O objetivo deste trabalho é, portanto, avaliar a influência de distintos cenários e políticas na difusão de veículos elétricos e híbridos no Brasil, tendo em vista a redução das emissões equivalentes totais. Para isso é desenvolvido um modelo de dinâmica de sistemas que simula o crescimento da frota de veículos elétricos até 2050 e calcula as emissões equivalentes de cada tecnologia. A partir dos cenários de dominância de determinada tecnologia de veículo elétrico, parcial ou total, ainda é possível verificar-se a intersecção com possíveis políticas para o incentivo da compra desses produtos, como a redução dos impostos (IPI, II, etc) para esses veículos, a perspectiva de redução do custo em R$/kWh das baterias, isenções ou reduções de IPVA, entre outros, reduzindo o custo total de propriedade/operação do produto. O trabalho conclui que o tipo de veículo elétrico que menos emissões produzirá é o veículo elétrico a bateria, considerando o horizonte de 2050