Empowering Electric Vehicles Batteries:A Comprehensive Look at the Application and Challenges of Second-Life Batteries

The surge in electric vehicle adoption has resulted in a significant rise in end-of-life batteries, which are unsuitable for demanding EV applications. Repurposing these batteries for secondary applications presents a promising avenue to tackle environmental and economic challenges associated with their disposal. The second-life battery (SLB) approach emerges as a mechanism to manage this massive amount of retired EV batteries. However, this approach poses significant challenges in determining and monitoring battery degradation and performance. After evaluating different scenarios for reusing or recycling retired EV batteries, this paper examines the main challenges associated with SLBs, including techno-economic aspects, uncertainty from first life, safety, characterization and screening, battery-management systems, and secondary applications. A comprehensive review of current state-of-the-art SLB research and implementations is provided, particularly emphasizing battery characterization and the requisite evaluation processes for SLB eligibility. This paper explores diverse measurement techniques for assessing SLB performance, evaluating them based on accuracy, complexity, and time consumption, which are essential for achieving cost-effective SLB applications. The overarching objective is to thoroughly understand the principal challenges associated with repurposing EV batteries and delineate the research imperatives necessary for their successful implementation and prolonged lifespan.</p

ADVANCES IN SYSTEM RELIABILITY-BASED DESIGN AND PROGNOSTICS AND HEALTH MANAGEMENT (PHM) FOR SYSTEM RESILIENCE ANALYSIS AND DESIGN

Failures of engineered systems can lead to significant economic and societal losses. Despite tremendous efforts (e.g., $200 billion annually) denoted to reliability and maintenance, unexpected catastrophic failures still occurs. To minimize the losses, reliability of engineered systems must be ensured throughout their life-cycle amidst uncertain operational condition and manufacturing variability. In most engineered systems, the required system reliability level under adverse events is achieved by adding system redundancies and/or conducting system reliability-based design optimization (RBDO). However, a high level of system redundancy increases a system's life-cycle cost (LCC) and system RBDO cannot ensure the system reliability when unexpected loading/environmental conditions are applied and unexpected system failures are developed. In contrast, a new design paradigm, referred to as resilience-driven system design, can ensure highly reliable system designs under any loading/environmental conditions and system failures while considerably reducing systems' LCC. In order to facilitate the development of formal methodologies for this design paradigm, this research aims at advancing two essential and co-related research areas: Research Thrust 1 - system RBDO and Research Thrust 2 - system prognostics and health management (PHM). In Research Thrust 1, reliability analyses under uncertainty will be carried out in both component and system levels against critical failure mechanisms. In Research Thrust 2, highly accurate and robust PHM systems will be designed for engineered systems with a single or multiple time-scale(s). To demonstrate the effectiveness of the proposed system RBDO and PHM techniques, multiple engineering case studies will be presented and discussed. Following the development of Research Thrusts 1 and 2, Research Thrust 3 - resilience-driven system design will establish a theoretical basis and design framework of engineering resilience in a mathematical and statistical context, where engineering resilience will be formulated in terms of system reliability and restoration and the proposed design framework will be demonstrated with a simplified aircraft control actuator design problem

Prognostics of automotive electronics with data driven approach: A review

Prognostics and Health Management (PHM) is a comprehensive framework that can deal with solutions for predicting and maintaining electronic system's health. The emerging concept of PHM is increasingly considered for adoption in many engineering fields such as automotive, mechanical, electrical, industrial, aerospace and railway. PHM of electronic components and systems can offer competitive advantages by improving performance, reliability, safety, maintainability and availability. In this paper, a brief description of PHM concept, current PHM approaches, key prognostics components and corresponding monitored/sensed parameters in automotive PHM applications are presented. Software tools developed for PHM applications are also reviewed. Particular focus is given on data driven approaches for prognostics of performance and reliability of automotive electronic systems. Based on the undertaken review of state-of-art in this area, key requirements and attributes of prognostic frameworks for automotive electronics are formulated and future prognostics challenges for the sector are discussed

Data-driven reliability analysis of Boeing 787 Dreamliner

The Boeing 787 Dreamliner, launched in 2011, was presented as a game changer in air travel. With the aim of producing an efficient, mid-size, wide-body plane, Boeing initiated innovations in product and process design, supply chain operation, and risk management. Nevertheless, there were reliability issues from the start, and the plane was grounded by the U.S. Federal Aviation Administration (FAA) in 2013, due to safety problems associated with Li-ion battery fires. This paper chronicles events associated with the aircraft's initial reliability challenges. The manufacturing, supply chain, and organizational factors that contributed to these problems are assessed based on FAA data. Recommendations and lessons learned are provided for the benefit of engineers and managers who will be engaged in future complex systems development

Assessment of the State-of-the-Art of System-Wide Safety and Assurance Technologies

Since its initiation, the System-wide Safety Assurance Technologies (SSAT) Project has been focused on developing multidisciplinary tools and techniques that are verified and validated to ensure prevention of loss of property and life in NextGen and enable proactive risk management through predictive methods. To this end, four technical challenges have been listed to help realize the goals of SSAT, namely (i) assurance of flight critical systems, (ii) discovery of precursors to safety incidents, (iii) assuring safe human-systems integration, and (iv) prognostic algorithm design for safety assurance. The objective of this report is to provide an extensive survey of SSAT-related research accomplishments by researchers within and outside NASA to get an understanding of what the state-of-the-art is for technologies enabling each of the four technical challenges. We hope that this report will serve as a good resource for anyone interested in gaining an understanding of the SSAT technical challenges, and also be useful in the future for project planning and resource allocation for related research

Assurance of Machine Learning-Based Aerospace Systems: Towards an Overarching Properties-Driven Approach

692M15-22-T-00012Traditional process-based approaches of certifying aerospace digital systems are not sufficient to address the challenges associated with using Artificial Intelligence (AI) or Machine Learning (ML) techniques. To address this, agencies are evaluating an alternative Means of Compliance (MoC) called the Overarching Properties (OP). The goals for this research are to develop recommendations and assurance criteria and to explore safety risk mitigation approaches for such AI/ML-based software systems. This document outlines a novel foundation for the application of OPs to support the assurance and certification of complex aerospace digital systems consisting of AI/ML-based components. To this end, we first select the use case of a Recorder Independent Power Supply (RIPS) system. We then perform a Functional Hazard Assessment (FHA) to identify a set of hazards associated with the RIPS and design a set of appropriate requirements to mitigate those hazards

Cyber-Enabled Product Lifecycle Management: A Multi-Agent Framework

Trouble free use of a product and its associated services for a specified minimum period of time is a major factor to win the customer\u27s trust in the product. Rapid and easy serviceability to maintain its functionalities plays a key role in achieving this goal. However, the sustainability of such a model cannot be promised unless the current health status of the product is monitored and condition-based maintenance is exercised. Internet of Things (IoT), an important connectivity paradigm of recent times, which connects physical objects to the internet for real-time information exchange and execution of physical actions via wired/wireless protocols. While the literature is full of various feasibility and viability studies focusing on architecture, design, and model development aspects, there is limited work addressing an IoT-based health monitoring of systems having high collateral damage. This motivated the research to develop a multi-agent framework for monitoring the performance and predicting impending failure to prevent unscheduled maintenance and downtime over internet, referred to as for cyber-enabled product lifecycle management (C-PLM). The framework incorporates a number of autonomous agents, such as hard agent, soft agent, and wave agent, to establish network connectivity to collect and exchange real-time health information for prognostics and health management (PHM). The proposed framework will help manufacturers not only to resolve the warranty failure issues more efficiently and economically but also improve their corporate image. The framework further leads to efficient handling of warranty failure issues and reduces the chances of future failure, i.e., offering durable products. From the sustainability point of view, this framework also addresses the reusability of the parts that still have a significant value using the prognostics and health data. Finally, multi-agent implementation of the proposed approach using a power substations for IoT-based C-PLM is included to show is efficacy

An accurate time constant parameter determination method for the varying condition equivalent circuit model of lithium batteries.

An accurate estimation of the state of charge for lithium battery depends on an accurate identification of the battery model parameters. In order to identify the polarization resistance and polarization capacitance in a Thevenin equivalent circuit model of lithium battery, the discharge and shelved states of a Thevenin circuit model were analyzed in this paper, together with the basic reasons for the difference in the resistance capacitance time constant and the accurate characterization of the resistance capacitance time constant in detail. The exact mathematical expression of the working characteristics of the circuit in two states were deduced thereafter. Moreover, based on the data of various working conditions, the parameters of the Thevenin circuit model through hybrid pulse power characterization experiment was identified, the simulation model was built, and a performance analysis was carried out. The experiments showed that the accuracy of the Thevenin circuit model can become 99.14% higher under dynamic test conditions and the new identification method that is based on the resistance capacitance time constant. This verifies that this method is highly accurate in the parameter identification of a lithium battery model

Developing computational intelligence for smart qualification testing of electronic products

In electronics manufacturing, the necessary quality of electronic components and parts is ensured through qualification testing using standards and user requirements. The challenge is that product qualification testing is time-consuming and comes at a substantial cost. The work contributes to develop a novel prognostics framework for predicting qualification test outcomes of electronic components enabling the reduction of qualification test time and cost. The research focuses on the development of a new, prognostics-based approach to qualification of electronics parts that can enable “smart testing” using data-driven modelling techniques in order to ensure product robustness and reliability in operation. This work is both novel and original because at present such approach to qualification testing and the associated capability for test time reduction (respectively cost reduction) it offers are non-existent in the electronics industry. An effective way of using three different methods for development of prognostics models are identified and applied. Predictive models are constructed from historical qualification test data in the form of electrical parameter measurements using Machine Learning (ML) techniques. ML models can be imbedded within the sequential electrical tests qualification procedure and enable the forecasting of the pass/fail qualification outcome using only partial information from already completed electrical tests. Data-driven prognostics models are developed using the following machine learning techniques: (1) Support Vector Machine (SVM), (2) Neural Network (NN) and (3) K-Nearest Neighbor (KNN). The results show that with just over half of the individual tests completed, the models are capable of forecasting the final qualification outcome, pass or fail, with accuracy as high as 92.5%. The predictive power and overall performance of the researched models in predicting qualification test binary outcomes with varying ratios of Pass and Fail data in the processed datasets are analysed