Architecture and Information Requirements to Assess and Predict Flight Safety Risks During Highly Autonomous Urban Flight Operations

As aviation adopts new and increasingly complex operational paradigms, vehicle types, and technologies to broaden airspace capability and efficiency, maintaining a safe system will require recognition and timely mitigation of new safety issues as they emerge and before significant consequences occur. A shift toward a more predictive risk mitigation capability becomes critical to meet this challenge. In-time safety assurance comprises monitoring, assessment, and mitigation functions that proactively reduce risk in complex operational environments where the interplay of hazards may not be known (and therefore not accounted for) during design. These functions can also help to understand and predict emergent effects caused by the increased use of automation or autonomous functions that may exhibit unexpected non-deterministic behaviors. The envisioned monitoring and assessment functions can look for precursors, anomalies, and trends (PATs) by applying model-based and data-driven methods. Outputs would then drive downstream mitigation(s) if needed to reduce risk. These mitigations may be accomplished using traditional design revision processes or via operational (and sometimes automated) mechanisms. The latter refers to the in-time aspect of the system concept. This report comprises architecture and information requirements and considerations toward enabling such a capability within the domain of low altitude highly autonomous urban flight operations. This domain may span, for example, public-use surveillance missions flown by small unmanned aircraft (e.g., infrastructure inspection, facility management, emergency response, law enforcement, and/or security) to transportation missions flown by larger aircraft that may carry passengers or deliver products. Caveat: Any stated requirements in this report should be considered initial requirements that are intended to drive research and development (R&D). These initial requirements are likely to evolve based on R&D findings, refinement of operational concepts, industry advances, and new industry or regulatory policies or standards related to safety assurance

Predicting the state of charge and health of batteries using data-driven machine learning

Machine learning is a specific application of artificial intelligence that allows computers to learn and improve from data and experience via sets of algorithms, without the need for reprogramming. In the field of energy storage, machine learning has recently emerged as a promising modelling approach to determine the state of charge, state of health and remaining useful life of batteries. First, we review the two most studied types of battery models in the literature for battery state prediction: the equivalent circuit and physics-based models. Based on the current limitations of these models, we showcase the promise of various machine learning techniques for fast and accurate battery state prediction. Finally, we highlight the major challenges involved, especially in accurate modelling over length and time, performing in situ calculations and high-throughput data generation. Overall, this work provides insights into real-time, explainable machine learning for battery production, management and optimization in the future

Lithium-sulfur cell equivalent circuit network model parameterization and sensitivity analysis

Compared to lithium-ion batteries, lithium-sulfur (Li-S) batteries potentially offer greater specific energy density, a wider temperature range of operation, and safety benefits, making them a promising technology for energy storage systems especially in automotive and aerospace applications. Unlike lithium-ion batteries, there is not a mature discipline of equivalent circuit network (ECN) modelling for Li-S. In this study, ECN modelling is addressed using formal ‘system identification’ techniques. A Li-S cell’s performance is studied in the presence of different charge/discharge rates and temperature levels using precise experimental test equipment. Various ECN model structures are explored, considering the trade-offs between accuracy and speed. It was concluded that a ‘2RC’ model is generally a good compromise, giving good accuracy and speed. Model parameterization is repeated at various state-of-charge (SOC) and temperature levels, and the effects of these variables on Li-S cell’s ohmic resistance and total capacity are demonstrated. The results demonstrate that Li-S cell’s ohmic resistance has a highly nonlinear relationship with SOC with a break-point around 75% SOC that distinguishes it from other types of battery. Finally, an ECN model is proposed which uses SOC and temperature as inputs. A sensitivity analysis is performed to investigate the effect of SOC estimation error on the model’s accuracy. In this analysis, the battery model’s accuracy is evaluated at various SOC and temperature levels. The results demonstrate that the Li-S cell model has the most sensitivity to SOC estimation error around the break-point (around 75% SOC) whereas in the middle SOC range, from 20% to 70%, it has the least sensitivity

Safety Performance and Failure Criteria of Lithium-Ion Batteries under Mechanical Abuse

Data Availability Statement: Not applicable.Copyright © 2023 by the authors. With the increasing global focus on environmental issues, controlling carbon dioxide emissions has become an important global agenda. In this context, the development of new energy vehicles, such as electric vehicles, is flourishing. However, as a crucial power source for electric vehicles, the safety performance of lithium-ion batteries under mechanical abuse has drawn widespread attention. Evaluating the safety performance of lithium-ion batteries requires in-depth research. This paper provides a review of recent experimental and numerical simulation studies on the mechanical abuse of lithium-ion batteries. It showcases the main methods and conclusions of experimental research, compares different response forms under quasi-static and dynamic loading, discusses the causes of strain-rate dependence in lithium-ion batteries, and briefly describes the impact of the state of charge (SOC) on safety performance under mechanical abuse, as well as the influence of mechanical abuse on battery capacity and impedance characteristics. Furthermore, this paper summarizes the methods of numerical simulation research, analyzes the advantages and disadvantages of detailed modeling and homogenized modeling methods, summarizes the strain-based internal short circuit failure criteria, and reviews numerical predictive models based on multiphysics coupling. Finally, it presents the latest progress in studying the safety performance of battery packs through numerical simulations.National Natural Science Foundation of China (Grant Number 11872265); Central Guidance on Local Science and Technology Development Fund of Shanxi Province (Grant Number YDZJSX2021A021); Natural Science Foundation of Shanxi Province (Grant Number 201901D111087); Research Project Supported by Shanxi Scholarship Council of China (Grant Number 2023-046)

An Integrated Framework and Software Prototype for Multi-scale Energy Systems Engineering

In this work, the developments of ENERGIA, a multi-scale energy systems transition modeling, optimization and scenario analysis framework and software prototype are presented. ENERGIA integrates (i) energy supply chain and transportation considerations, (ii) detailed energy production aspects, and (iii) scheduling decisions for operation and inventory management of energy and resources storage. It is based on a methodology that involves (i) detailed data and models for the description of process alternatives and units and the corresponding supply chains, (ii) a library of surrogate modeling techniques, for both the nonlinear process models, as well as scheduling decisions, and (iii) a detailed design planning time-varying scheduling model (iv) a mixed-integer programming optimization strategy. ENERGIA’s python-based environment allows users to visualize resource availability and demands at various temporal and geographic scales and resolutions, and compare competing objectives and renewable-based energy strategies. A hydrogen-economy energy transition problem is presented to highlight the key capabilities of the proposed framework

Understanding and modelling the thermal behaviour of incumbent and future lithium ion batteries

The thesis begins with a literature review on the thermal behaviours for an incumbent and a future lithium ion battery, which are Lithium iron phosphate (LFP) prismatic batteries and Lithium sulfur (Li-S) pouch batteries, respectively. Research gaps were identified for both types of batteries, requiring the development of novel experimental techniques and/or modelling approaches for each type. Lithium sulfur batteries are an important next generation high energy density battery technology. However, the phenomenon known as the polysulfide shuttle was identified as one of the most important challenges needing to be overcome. It causes accelerated degradation, reduced Coulombic efficiency and increased heat generation, particularly towards the end of charge. Research was conducted on how to track, quantify and therefore prevent the shuttle effect, in order to improve the safety and increase cycle life of Li-S batteries in real applications. This required the real-time detection of the onset of shuttle during charge. The diagnostic technique Differential Thermal Voltammetry (DTV) was used to track the shuttle effect during charging for the first time, and quantitative interpretations of the experimental DTV curves were performed by thermally-coupling a zero-dimensional Li-S model. The DTV technique, together with the model, is a promising tool for real-time detection of shuttle in applications, to inform control algorithms for deciding the end of charging, thus preventing excessive degradation and charge inefficiency. Lithium iron phosphate prismatic batteries are widely used in both sustainable transportation and stationary energy storage. However, system level thermal management for large format prismatic cells is rarely considered in the literature. Equivalent circuit models (ECM) were shortlisted, due to their ease of implementation and low complexity. The accuracy of an ECM is critical to the functionality and usefulness of the battery management system (BMS). However, their accuracy is limited by how easy they are parameterised, and therefore different experimental techniques and model parameter identification methods (PIM) have been widely studied. Yet, how to account for significant changes in time constants between operation under load and during relaxation has not been resolved. In this work a novel PIM and a modified ECM is presented that increases accuracy by 77.4% during drive cycle validation and 87.6% during constant current load validation for a large format LFP prismatic cell. The modified ECM uses switching RC network values for each phase, which is significant for this cell and particularly at low state-of-charge for all lithium ion batteries. Different characterisation tests and the corresponding experimental data have been trained together across a complete State-of-Charge (SoC) and temperature range, which enables a smooth transition between identified parameters. Ultimately, the model created using parameters captured by the proposed PIM shows an improved model accuracy in comparison with conventional PIM techniques. Large format prismatic cell’s thermal management is challenging due to the large internal heat generation rate, longer distance for internal battery core away from the heat exchange cooling interface and therefore larger thermal gradient across the cell. The standardised surface Cell Cooling Coefficient (CCC) can be used to quantify the degree of difficulty of a target cell to be thermally managed. Here, in this thesis, the novel metric surface CCC is introduced and implemented onto a large format LFP prismatic cell, with aluminium alloy prismatic casing. Further, based on developed PIM, a parameterised and discretised 3-dimentional Electro-Thermal Equivalent Circuit Model is developed. The developed model is validated using the experimental data through embedding corresponding boundary conditions, including drive cycle noisy load and constant current CCC square wave load, electrically and thermally at the same time. The study offers a quantitative guide of the trade-off between cell energy density and surface CCC, and also a casing selection analysis is conducted. The CCC metric together with proposed model enable the cell manufacturer and Original Equipment Makers (OEMs) to customise the cell design based on the casing material, single cell energy density, cell thickness and CCC/capability to be thermally managed. In the future cell design process, this study offers a cost-effective, time-efficient, convenient and quantitative way, in order to achieve a better and safe battery design (high capacity, power and longer lifetime) for wider application needs. Finally, it is concluded that, for both incumbent and future lithium ion batteries, understanding the thermal behaviour is the key for a safer, lighter, longer lifetime, longer range application. By using engineering customised experimental techniques together with empirical and/or physical simulations, enhanced understanding with quantitative battery optimisation and thermal management are achieved in this thesis. The findings in thesis are beneficial for wide range of communities including research community, industry OEMs, application engineers, battery management system developers, control engineers and electric vehicle end users.Open Acces

NREL Energy Storage Projects -- FY2012 Annual Report

This report describes the activities of the Energy Storage group over FY2012