A novel energy management strategy for the ternary lithium batteries based on the dynamic equivalent circuit modeling and differential Kalman filtering under time-varying conditions.

The dynamic model of the ternary lithium battery is a time-varying nonlinear system due to the polarization and diffusion effects inside the battery in its charge-discharge process. Based on the comprehensive analysis of the energy management methods, the state of charge is estimated by introducing the differential Kalman filtering method combined with the dynamic equivalent circuit model considering the nonlinear temperature coefficient. The model simulates the transient response with high precision which is suitable for its high current and complicated charging and discharging conditions. In order to better reflect the dynamic characteristics of the power ternary lithium battery in the step-type charging and discharging conditions, the polarization circuit of the model is differential and the improved iterate calculation model is obtained. As can be known from the experimental verifications, the maximize state of charge estimation error is only 0.022 under the time-varying complex working conditions and the output voltage is monitored simultaneously with the maximum error of 0.08 V and the average error of 0.04 V. The established model can describe the dynamic battery behavior effectively, which can estimate its state of charge value with considerably high precision, providing an effective energy management strategy for the ternary lithium batteries

Fast battery capacity estimation using convolutional neural networks

Lithium-ion batteries have been widely used in electric vehicles, smart grids and many other applications as energy storage devices, for which the aging assessment is crucial to guarantee their safe and reliable operation. The battery capacity is a popular indicator for assessing the battery aging, however, its accurate estimation is challenging due to a range of time-varying situation-dependent internal and external factors. Traditional simplified models and machine learning tools are difficult to capture these characteristics. As a class of deep neural networks, the convolutional neural network (CNN) is powerful to capture hidden information from a huge amount of input data, making it an ideal tool for battery capacity estimation. This paper proposes a CNN-based battery capacity estimation method, which can accurately estimate the battery capacity using limited available measurements, without resorting to other offline information. Further, the proposed method only requires partial charging segment of voltage, current and temperature curves, making it possible to achieve fast online health monitoring. The partial charging curves have a fixed length of 225 consecutive points and a flexible starting point, thereby short-term charging data of the battery charged from any initial state-of-charge can be used to produce accurate capacity estimation. To employ CNN for capacity estimation using partial charging curves is however not trivial, this paper presents a comprehensive approach covering time series-to-image transformation, data segmentation, and CNN configuration. The CNN-based method is applied to two battery degradation datasets and achieves root mean square errors (RMSEs) of less than 0.0279 Ah (2.54%) and 0.0217 Ah (2.93% ), respectively, outperforming existing machine learning methods

Development Schemes of Electric Vehicle Charging Protocols and Implementation of Algorithms for Fast Charging under Dynamic Environments Leading towards Grid-to-Vehicle Integration

This thesis focuses on the development of electric vehicle (EV) charging protocols under a dynamic environment using artificial intelligence (AI), to achieve Vehicle-to-Grid (V2G) integration and promote automobile electrification. The proposed framework comprises three major complementary steps. Firstly, the DC fast charging scheme is developed under different ambient conditions such as temperature and relative humidity. Subsequently, the transient performance of the controller is improved while implementing the proposed DC fast charging scheme. Finally, various novel techno-economic scenarios and case studies are proposed to integrate EVs with the utility grid. The proposed novel scheme is composed of hierarchical stages; In the first stage, an investigation of the temperature or/and relative humidity impact on the charging process is implemented using the constant current-constant voltage (CC-CV) protocol. Where the relative humidity impact on the charging process was not investigated or mentioned in the literature survey. This was followed by the feedforward backpropagation neural network (FFBP-NN) classification algorithm supported by the statistical analysis of an instant charging current sample of only 10 seconds at any ambient condition. Then the FFBP-NN perfectly estimated the EV’s battery terminal voltage, charging current, and charging interval time with an error of 1% at the corresponding temperature and relative humidity. Then, a nonlinear identification model of the lithium-polymer ion battery dynamic behaviour is introduced based on the Hammerstein-Wiener (HW) model with an experimental error of 1.1876%. Compared with the CC-CV fast charging protocol, intelligent novel techniques based on the multistage charging current protocol (MSCC) are proposed using the Cuckoo optimization algorithm (COA). COA is applied to the Hierarchical technique (HT) and the Conditional random technique (CRT). Compared with the CC-CV charging protocol, an improvement in the charging efficiency of 8% and 14.1% was obtained by the HT and the CRT, respectively, in addition to a reduction in energy losses of 7.783% and 10.408% and a reduction in charging interval time of 18.1% and 22.45%, respectively. The stated charging protocols have been implemented throughout a smart charger. The charger comprises a DC-DC buck converter controlled by an artificial neural network predictive controller (NNPC), trained and supported by the long short-term memory neural network (LSTM). The LSTM network model was utilized in the offline forecasting of the PV output power, which was fed to the NNPC as the training data. The NNPC–LSTM controller was compared with the fuzzy logic (FL) and the conventional PID controllers and perfectly ensured that the optimum transient performance with a minimum battery terminal voltage ripple reached 1 mV with a very high-speed response of 1 ms in reaching the predetermined charging current stages. Finally, to alleviate the power demand pressure of the proposed EV charging framework on the utility grid, a novel smart techno-economic operation of an electric vehicle charging station (EVCS) in Egypt controlled by the aggregator is suggested based on a hierarchical model of multiple scenarios. The deterministic charging scheduling of the EVs is the upper stage of the model to balance the generated and consumed power of the station. Mixed-integer linear programming (MILP) is used to solve the first stage, where the EV charging peak demand value is reduced by 3.31% (4.5 kW). The second challenging stage is to maximize the EVCS profit whilst minimizing the EV charging tariff. In this stage, MILP and Markov Decision Process Reinforcement Learning (MDP-RL) resulted in an increase in EVCS revenue by 28.88% and 20.10%, respectively. Furthermore, the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) technologies are applied to the stochastic EV parking across the day, controlled by the aggregator to alleviate the utility grid load demand. The aggregator determined the number of EVs that would participate in the electric power trade and sets the charging/discharging capacity level for each EV. The proposed model minimized the battery degradation cost while maximizing the revenue of the EV owner and minimizing the utility grid load demand based on the genetic algorithm (GA). The implemented procedure reduced the degradation cost by an average of 40.9256%, increased the EV SOC by 27%, and ensured an effective grid stabilization service by shaving the load demand to reach a predetermined grid average power across the day where the grid load demand decreased by 26.5% (371 kW)

Roadmap for a sustainable circular economy in lithium-ion and future battery technologies

The market dynamics, and their impact on a future circular economy for lithium-ion batteries (LIB), are presented in this roadmap, with safety as an integral consideration throughout the life cycle. At the point of end-of-life (EOL), there is a range of potential options—remanufacturing, reuse and recycling. Diagnostics play a significant role in evaluating the state-of-health and condition of batteries, and improvements to diagnostic techniques are evaluated. At present, manual disassembly dominates EOL disposal, however, given the volumes of future batteries that are to be anticipated, automated approaches to the dismantling of EOL battery packs will be key. The first stage in recycling after the removal of the cells is the initial cell-breaking or opening step. Approaches to this are reviewed, contrasting shredding and cell disassembly as two alternative approaches. Design for recycling is one approach that could assist in easier disassembly of cells, and new approaches to cell design that could enable the circular economy of LIBs are reviewed. After disassembly, subsequent separation of the black mass is performed before further concentration of components. There are a plethora of alternative approaches for recovering materials; this roadmap sets out the future directions for a range of approaches including pyrometallurgy, hydrometallurgy, short-loop, direct, and the biological recovery of LIB materials. Furthermore, anode, lithium, electrolyte, binder and plastics recovery are considered in order to maximise the proportion of materials recovered, minimise waste and point the way towards zero-waste recycling. The life-cycle implications of a circular economy are discussed considering the overall system of LIB recycling, and also directly investigating the different recycling methods. The legal and regulatory perspectives are also considered. Finally, with a view to the future, approaches for next-generation battery chemistries and recycling are evaluated, identifying gaps for research. This review takes the form of a series of short reviews, with each section written independently by a diverse international authorship of experts on the topic. Collectively, these reviews form a comprehensive picture of the current state of the art in LIB recycling, and how these technologies are expected to develop in the future

Classical and reactive molecular dynamics: Principles and applications in combustion and energy systems

Molecular dynamics (MD) has evolved into a ubiquitous, versatile and powerful computational method for fundamental research in science branches such as biology, chemistry, biomedicine and physics over the past 60 years. Powered by rapidly advanced supercomputing technologies in recent decades, MD has entered the engineering domain as a first-principle predictive method for material properties, physicochemical processes, and even as a design tool. Such developments have far-reaching consequences, and are covered for the first time in the present paper, with a focus on MD for combustion and energy systems encompassing topics like gas/liquid/solid fuel oxidation, pyrolysis, catalytic combustion, heterogeneous combustion, electrochemistry, nanoparticle synthesis, heat transfer, phase change, and fluid mechanics. First, the theoretical framework of the MD methodology is described systemically, covering both classical and reactive MD. The emphasis is on the development of the reactive force field (ReaxFF) MD, which enables chemical reactions to be simulated within the MD framework, utilizing quantum chemistry calculations and/or experimental data for the force field training. Second, details of the numerical methods, boundary conditions, post-processing and computational costs of MD simulations are provided. This is followed by a critical review of selected applications of classical and reactive MD methods in combustion and energy systems. It is demonstrated that the ReaxFF MD has been successfully deployed to gain fundamental insights into pyrolysis and/or oxidation of gas/liquid/solid fuels, revealing detailed energy changes and chemical pathways. Moreover, the complex physico-chemical dynamic processes in catalytic reactions, soot formation, and flame synthesis of nanoparticles are made plainly visible from an atomistic perspective. Flow, heat transfer and phase change phenomena are also scrutinized by MD simulations. Unprecedented details of nanoscale processes such as droplet collision, fuel droplet evaporation, and CO2 capture and storage under subcritical and supercritical conditions are examined at the atomic level. Finally, the outlook for atomistic simulations of combustion and energy systems is discussed in the context of emerging computing platforms, machine learning and multiscale modelling

Implications of the BATTERY 2030+ AI-Assisted Toolkit on Future Low-TRL Battery Discoveries and Chemistries

Funder: Swedish national Strategic e‐Science programmeFunder: Deutsche Forschungsgemeinschaft; Id: http://dx.doi.org/10.13039/501100001659BATTERY 2030+ targets the development of a chemistry neutral platform for accelerating the development of new sustainable high-performance batteries. Here, a description is given of how the AI-assisted toolkits and methodologies developed in BATTERY 2030+ can be transferred and applied to representative examples of future battery chemistries, materials, and concepts. This perspective highlights some of the main scientific and technological challenges facing emerging low-technology readiness level (TRL) battery chemistries and concepts, and specifically how the AI-assisted toolkit developed within BIG-MAP and other BATTERY 2030+ projects can be applied to resolve these. The methodological perspectives and challenges in areas like predictive long time- and length-scale simulations of multi-species systems, dynamic processes at battery interfaces, deep learned multi-scaling and explainable AI, as well as AI-assisted materials characterization, self-driving labs, closed-loop optimization, and AI for advanced sensing and self-healing are introduced. A description is given of tools and modules can be transferred to be applied to a select set of emerging low-TRL battery chemistries and concepts covering multivalent anodes, metal-sulfur/oxygen systems, non-crystalline, nano-structured and disordered systems, organic battery materials, and bulk vs. interface-limited batteries

Datacenter management for on-site intermittent and uncertain renewable energy sources

Les technologies de l'information et de la communication sont devenues, au cours des dernières années, un pôle majeur de consommation énergétique avec les conséquences environnementales associées. Dans le même temps, l'émergence du Cloud computing et des grandes plateformes en ligne a causé une augmentation en taille et en nombre des centres de données. Pour réduire leur impact écologique, alimenter ces centres avec des sources d'énergies renouvelables (EnR) apparaît comme une piste de solution. Cependant, certaines EnR telles que les énergies solaires et éoliennes sont liées aux conditions météorologiques, et sont par conséquent intermittentes et incertaines. L'utilisation de batteries ou d'autres dispositifs de stockage est souvent envisagée pour compenser ces variabilités de production. De par leur coût important, économique comme écologique, ainsi que les pertes énergétiques engendrées, l'utilisation de ces dispositifs sans intégration supplémentaire est insuffisante. La consommation électrique d'un centre de données dépend principalement de l'utilisation des ressources de calcul et de communication, qui est déterminée par la charge de travail et les algorithmes d'ordonnancement utilisés. Pour utiliser les EnR efficacement tout en préservant la qualité de service du centre, une gestion coordonnée des ressources informatiques, des sources électriques et du stockage est nécessaire. Il existe une grande diversité de centres de données, ayant différents types de matériel, de charge de travail et d'utilisation. De la même manière, suivant les EnR, les technologies de stockage et les objectifs en termes économiques ou environnementaux, chaque infrastructure électrique est modélisée et gérée différemment des autres. Des travaux existants proposent des méthodes de gestion d'EnR pour des couples bien spécifiques de modèles électriques et informatiques. Cependant, les multiples combinaisons de ces deux parties rendent difficile l'extrapolation de ces approches et de leurs résultats à des infrastructures différentes. Cette thèse explore de nouvelles méthodes pour résoudre ce problème de coordination. Une première contribution reprend un problème d'ordonnancement de tâches en introduisant une abstraction des sources électriques. Un algorithme d'ordonnancement est proposé, prenant les préférences des sources en compte, tout en étant conçu pour être indépendant de leur nature et des objectifs de l'infrastructure électrique. Une seconde contribution étudie le problème de planification de l'énergie d'une manière totalement agnostique des infrastructures considérées. Les ressources informatiques et la gestion de la charge de travail sont encapsulées dans une boîte noire implémentant un ordonnancement sous contrainte de puissance. La même chose s'applique pour le système de gestion des EnR et du stockage, qui agit comme un algorithme d'optimisation d'engagement de sources pour répondre à une demande. Une optimisation coopérative et multiobjectif, basée sur un algorithme évolutionnaire, utilise ces deux boîtes noires afin de trouver les meilleurs compromis entre les objectifs électriques et informatiques. Enfin, une troisième contribution vise les incertitudes de production des EnR pour une infrastructure plus spécifique. En utilisant une formulation en processus de décision markovien (MDP), la structure du problème de décision sous-jacent est étudiée. Pour plusieurs variantes du problème, des méthodes sont proposées afin de trouver les politiques optimales ou des approximations de celles-ci avec une complexité raisonnable.In recent years, information and communication technologies (ICT) became a major energy consumer, with the associated harmful ecological consequences. Indeed, the emergence of Cloud computing and massive Internet companies increased the importance and number of datacenters around the world. In order to mitigate economical and ecological cost, powering datacenters with renewable energy sources (RES) began to appear as a sustainable solution. Some of the commonly used RES, such as solar and wind energies, directly depends on weather conditions. Hence they are both intermittent and partly uncertain. Batteries or other energy storage devices (ESD) are often considered to relieve these issues, but they result in additional energy losses and are too costly to be used alone without more integration. The power consumption of a datacenter is closely tied to the computing resource usage, which in turn depends on its workload and on the algorithms that schedule it. To use RES as efficiently as possible while preserving the quality of service of a datacenter, a coordinated management of computing resources, electrical sources and storage is required. A wide variety of datacenters exists, each with different hardware, workload and purpose. Similarly, each electrical infrastructure is modeled and managed uniquely, depending on the kind of RES used, ESD technologies and operating objectives (cost or environmental impact). Some existing works successfully address this problem by considering a specific couple of electrical and computing models. However, because of this combined diversity, the existing approaches cannot be extrapolated to other infrastructures. This thesis explores novel ways to deal with this coordination problem. A first contribution revisits batch tasks scheduling problem by introducing an abstraction of the power sources. A scheduling algorithm is proposed, taking preferences of electrical sources into account, though designed to be independent from the type of sources and from the goal of the electrical infrastructure (cost, environmental impact, or a mix of both). A second contribution addresses the joint power planning coordination problem in a totally infrastructure-agnostic way. The datacenter computing resources and workload management is considered as a black-box implementing a scheduling under variable power constraint algorithm. The same goes for the electrical sources and storage management system, which acts as a source commitment optimization algorithm. A cooperative multiobjective power planning optimization, based on a multi-objective evolutionary algorithm (MOEA), dialogues with the two black-boxes to find the best trade-offs between electrical and computing internal objectives. Finally, a third contribution focuses on RES production uncertainties in a more specific infrastructure. Based on a Markov Decision Process (MDP) formulation, the structure of the underlying decision problem is studied. For several variants of the problem, tractable methods are proposed to find optimal policies or a bounded approximation