State of Charge Estimation of Parallel Connected Battery Cells via Descriptor System Theory

This manuscript presents an algorithm for individual Lithium-ion (Li-ion) battery cell state of charge (SOC) estimation when multiple cells are connected in parallel, using only terminal voltage and total current measurements. For battery packs consisting of thousands of cells, it is desirable to estimate individual SOCs by only monitoring the total current in order to reduce sensing cost. Mathematically, series connected cells yield dynamics given by ordinary differential equations under classical full voltage sensing. In contrast, parallel connected cells are evidently more challenging because the dynamics are governed by a nonlinear descriptor system, including differential equations and algebraic equations arising from voltage and current balance across cells. An observer with linear output error injection is formulated, where the individual cell SOCs and local currents are locally observable from the total current and voltage measurements. The asymptotic convergence of differential and algebraic states is established by considering local Lipschitz continuity property of system nonlinearities. Simulation results on LiNiMnCoO$_2$/Graphite (NMC) cells illustrate convergence for SOCs, local currents, and terminal voltage.Comment: 7 pages, 4 figures, 1 table, accepted by 2020 American Control Conferenc

Trends in electric vehicles research

Electrification of vehicles has been recognised as a key part of meeting global climate change targets and a key aspect of sustainable transport. Here, an integrative and bird\u27s-eye view of scholarly research on Electric Vehicles (EV) is provided with a focus on an objective and quantitative determination of research trends. The analyses suggest that areas of EV research linked to (i) charging infrastructure, (ii) EV adoption, (iii) thermal management systems and (iv) routing problem have been the distinct trending topics in recent years. While hybrid EV proves to have been a dominant keyword, its frequency of use has either flattened out in recent years or is notably on the decline across major subfields of EV research. The findings provide objective indications about the directions to which EV research is currently headed. A secondary outcome is the determination of references that have been most instrumental in developing each major stream of EV research

Resolving Kirchhoff’s laws for parallel Li-ion battery pack state-estimators

A state-space model for Li-ion battery packs with parallel-connected cells is introduced. The key feature of the model is an explicit solution to Kirchhoff’s laws for parallel-connected packs, which expresses the branch currents directly in terms of the model’s states, applied current, and cell resistances. This avoids the need to solve these equations numerically. To illustrate the potential of the proposed model for pack-level control and estimation, a method to bound the error of a state-estimator is introduced and the modeling framework is generalized to a class of electrochemical models. It is hoped that the insight brought by this model formulation will allow the wealth of results developed for series-connected packs to be applied to those with parallel connections

Large-Scale Statistical Learning for Mass Transport Prediction in Porous Materials Using 90,000 Artificially Generated Microstructures

Effective properties of functional materials crucially depend on their 3D microstructure. In this paper, we investigate quantitative relationships between descriptors of two-phase microstructures, consisting of solid and pores and their mass transport properties. To that end, we generate a vast database comprising 90,000 microstructures drawn from nine different stochastic models, and compute their effective diffusivity and permeability as well as various microstructural descriptors. To the best of our knowledge, this is the largest and most diverse dataset created for studying the influence of 3D microstructure on mass transport. In particular, we establish microstructure-property relationships using analytical prediction formulas, artificial (fully-connected) neural networks, and convolutional neural networks. Again, to the best of our knowledge, this is the first time that these three statistical learning approaches are quantitatively compared on the same dataset. The diversity of the dataset increases the generality of the determined relationships, and its size is vital for robust training of convolutional neural networks. We make the 3D microstructures, their structural descriptors and effective properties, as well as the code used to study the relationships between them available open access

Design and Simulation of a DC Electric Vehicle Charging Station Interconnected with a MVDC Network

Due to a rapidly aging electric transmission and distribution infrastructure, an increased demand for energy, an increased awareness of climate change and greenhouse gas pollution, and an increased cost of fuel there is a need to produce and deliver energy more efficiently. This thesis attempts to provide a solution to these constraints through advancements in DC power architectures. Medium Voltage Direct Current (MVDC) infrastructure serves as a platform for the interconnection of renewable electric power generation, including wind and solar. Abundant loads such as industrial facilities, data centers, commercial office buildings, industrial parks, and electric vehicle charging stations (EVCS) can also be powered using MVDC technology. MVDC networks are expected to improve efficiency, through reductions in power electronic conversion steps and by serving as an additional layer between the transmission and distribution level voltage for which generation sources and loads could directly interface with smaller rated power conversion equipment. This thesis provides an introduction to battery energy storage system technology, and primarily investigates an EVCS powered via a MVDC bus. A bidirectional DC-DC converter with appropriate controls serves as the interface between the EVCS and the MVDC bus. Two scenarios are investigated for testing and comparing EVCS operation: 1) EVCS power supplied by the interconnected MVDC model and 2) EVCS power supplied by an equivalent voltage source. The ability of the battery charger (synchronous buck converter) to isolate faults in next generation DC power systems is explored. Each of the investigated components is modeled and simulated utilizing the PSCAD simulation environment then analytically validated

Reconfigurable Antenna Systems: Platform implementation and low-power matters

Antennas are a necessary and often critical component of all wireless systems, of which they share the ever-increasing complexity and the challenges of present and emerging trends. 5G, massive low-orbit satellite architectures (e.g. OneWeb), industry 4.0, Internet of Things (IoT), satcom on-the-move, Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles, all call for highly flexible systems, and antenna reconfigurability is an enabling part of these advances. The terminal segment is particularly crucial in this sense, encompassing both very compact antennas or low-profile antennas, all with various adaptability/reconfigurability requirements. This thesis work has dealt with hardware implementation issues of Radio Frequency (RF) antenna reconfigurability, and in particular with low-power General Purpose Platforms (GPP); the work has encompassed Software Defined Radio (SDR) implementation, as well as embedded low-power platforms (in particular on STM32 Nucleo family of micro-controller). The hardware-software platform work has been complemented with design and fabrication of reconfigurable antennas in standard technology, and the resulting systems tested. The selected antenna technology was antenna array with continuously steerable beam, controlled by voltage-driven phase shifting circuits. Applications included notably Wireless Sensor Network (WSN) deployed in the Italian scientific mission in Antarctica, in a traffic-monitoring case study (EU H2020 project), and into an innovative Global Navigation Satellite Systems (GNSS) antenna concept (patent application submitted). The SDR implementation focused on a low-cost and low-power Software-defined radio open-source platform with IEEE 802.11 a/g/p wireless communication capability. In a second embodiment, the flexibility of the SDR paradigm has been traded off to avoid the power consumption associated to the relevant operating system. Application field of reconfigurable antenna is, however, not limited to a better management of the energy consumption. The analysis has also been extended to satellites positioning application. A novel beamforming method has presented demonstrating improvements in the quality of signals received from satellites. Regarding those who deal with positioning algorithms, this advancement help improving precision on the estimated position

Predicting electronic structures at any length scale with machine learning

The properties of electrons in matter are of fundamental importance. They give rise to virtually all molecular and material properties and determine the physics at play in objects ranging from semiconductor devices to the interior of giant gas planets. Modeling and simulation of such diverse applications rely primarily on density functional theory (DFT), which has become the principal method for predicting the electronic structure of matter. While DFT calculations have proven to be very useful to the point of being recognized with a Nobel prize in 1998, their computational scaling limits them to small systems. We have developed a machine learning framework for predicting the electronic structure on any length scale. It shows up to three orders of magnitude speedup on systems where DFT is tractable and, more importantly, enables predictions on scales where DFT calculations are infeasible. Our work demonstrates how machine learning circumvents a long-standing computational bottleneck and advances science to frontiers intractable with any current solutions. This unprecedented modeling capability opens up an inexhaustible range of applications in astrophysics, novel materials discovery, and energy solutions for a sustainable future