Data Science-Based Full-Lifespan Management of Lithium-Ion Battery

This open access book comprehensively consolidates studies in the rapidly emerging field of battery management. The primary focus is to overview the new and emerging data science technologies for full-lifespan management of Li-ion batteries, which are categorized into three groups, namely (i) battery manufacturing management, (ii) battery operation management, and (iii) battery reutilization management. The key challenges, future trends as well as promising data-science technologies to further improve this research field are discussed. As battery full-lifespan (manufacturing, operation, and reutilization) management is a hot research topic in both energy and AI fields and none specific book has focused on systematically describing this particular from a data science perspective before, this book can attract the attention of academics, scientists, engineers, and practitioners. It is useful as a reference book for students and graduates working in related fields. Specifically, the audience could not only get the basics of battery manufacturing, operation, and reutilization but also the information of related data-science technologies. The step-by-step guidance, comprehensive introduction, and case studies to the topic make it accessible to audiences of different levels, from graduates to experienced engineers

A novel remaining useful life prediction framework for lithium-ion battery using grey model and particle filtering

An accurate remaining useful life (RUL) prediction method is significant to optimize the lithium-ion batteries' performances in an intelligent battery management system. Since the construction of battery models and the initialization of algorithms require a large amount of data, it is difficult for conventional methods to guarantee the RUL prediction accuracy when the available data are insufficient. To solve this problem, a synergy of sliding-window grey model (SGM) and particle filter (PF) is exploited to build an innovative framework for battery RUL prediction. The SGM is adopted to explore the modelling of battery capacity degradation, and it characterizes the capacity changes during the battery's life-time with a few data (eg, 8 data points). To promote the accuracy and traceability of prediction, the development coefficient of the SGM, which can dynamically reflect the capacity degradation, is extracted to update the state variables of state transition function in PF. Accordingly, the fusion of SGM and PF (SGM-PF) can extrapolate the changes of the capacity and realize RUL prediction using fewer data. Furthermore, the performances of SGM-PF are comprehensively validated using two types of batteries aged under different conditions. The RUL prediction results reveal that the SGM-PF framework can achieve precise and reliable predictions in different prediction horizons with as few as 8 data points, and it has prominent performance in accuracy and stability over contrastive methods, especially in long-term prognosis

A uniform estimation framework for state of health of lithium-ion batteries considering feature extraction and parameters optimization

State of health is one of the most critical parameters to characterize inner status of lithium-ion batteries in electric vehicles. In this study, a uniform estimation framework is proposed to simultaneously achieve the estimation of state of health and optimize the healthy features therein, which are excavated based on the charging voltage curves within a fixed range. The fixed size least squares-support vector machine is employed to estimate the state of health with less computation intensity, and the genetic algorithm is applied to search the optimal charging voltage range and parameters of fixed size least squares-support vector machine. By this manner, the measured raw data during the charging process can be directly fed into the estimation model without any pretreatment. The estimation performance of proposed algorithm is validated in terms of different voltage ranges and sampling time, and also compared with other three traditional machine learning algorithms. The experimental results highlight that the presented estimation framework cannot only restrict the prediction error of state of health within 2%, but also feature high robustness and universality

Data Science-Based Full-Lifespan Management of Lithium-Ion Battery

This open access book comprehensively consolidates studies in the rapidly emerging field of battery management. The primary focus is to overview the new and emerging data science technologies for full-lifespan management of Li-ion batteries, which are categorized into three groups, namely (i) battery manufacturing management, (ii) battery operation management, and (iii) battery reutilization management. The key challenges, future trends as well as promising data-science technologies to further improve this research field are discussed. As battery full-lifespan (manufacturing, operation, and reutilization) management is a hot research topic in both energy and AI fields and none specific book has focused on systematically describing this particular from a data science perspective before, this book can attract the attention of academics, scientists, engineers, and practitioners. It is useful as a reference book for students and graduates working in related fields. Specifically, the audience could not only get the basics of battery manufacturing, operation, and reutilization but also the information of related data-science technologies. The step-by-step guidance, comprehensive introduction, and case studies to the topic make it accessible to audiences of different levels, from graduates to experienced engineers

Investigation of risk-aware MDP and POMDP contingency management autonomy for UAS

Unmanned aircraft systems (UAS) are being increasingly adopted for various applications. The risk UAS poses to people and property must be kept to acceptable levels. This paper proposes risk-aware contingency management autonomy to prevent an accident in the event of component malfunction, specifically propulsion unit failure and/or battery degradation. The proposed autonomy is modeled as a Markov Decision Process (MDP) whose solution is a contingency management policy that appropriately executes emergency landing, flight termination or continuation of planned flight actions. Motivated by the potential for errors in fault/failure indicators, partial observability of the MDP state space is investigated. The performance of optimal policies is analyzed over varying observability conditions in a high-fidelity simulator. Results indicate that both partially observable MDP (POMDP) and maximum a posteriori MDP policies performed similarly over different state observability criteria, given the nearly deterministic state transition model

Failure Diagnosis and Prognosis of Safety Critical Systems: Applications in Aerospace Industries

Many safety-critical systems such as aircraft, space crafts, and large power plants are required to operate in a reliable and efficient working condition without any performance degradation. As a result, fault diagnosis and prognosis (FDP) is a research topic of great interest in these systems. FDP systems attempt to use historical and current data of a system, which are collected from various measurements to detect faults, diagnose the types of possible failures, predict and manage failures in advance. This thesis deals with FDP of safety-critical systems. For this purpose, two critical systems including a multifunctional spoiler (MFS) and hydro-control value system are considered, and some challenging issues from the FDP are investigated. This research work consists of three general directions, i.e., monitoring, failure diagnosis, and prognosis. The proposed FDP methods are based on data-driven and model-based approaches. The main aim of the data-driven methods is to utilize measurement data from the system and forecast the remaining useful life (RUL) of the faulty components accurately and efficiently. In this regard, two dierent methods are developed. A modular FDP method based on a divide and conquer strategy is presented for the MFS system. The modular structure contains three components:1) fault diagnosis unit, 2) failure parameter estimation unit and 3) RUL unit. The fault diagnosis unit identifies types of faults based on an integration of neural network (NN) method and discrete wavelet transform (DWT) technique. Failure parameter estimation unit observes the failure parameter via a distributed neural network. Afterward, the RUL of the system is predicted by an adaptive Bayesian method. In another work, an innovative data-driven FDP method is developed for hydro-control valve systems. The idea is to use redundancy in multi-sensor data information and enhance the performance of the FDP system. Therefore, a combination of a feature selection method and support vector machine (SVM) method is applied to select proper sensors for monitoring of the hydro-valve system and isolate types of fault. Then, adaptive neuro-fuzzy inference systems (ANFIS) method is used to estimate the failure path. Similarly, an online Bayesian algorithm is implemented for forecasting RUL. Model-based methods employ high-delity physics-based model of a system for prognosis task. In this thesis, a novel model-based approach based on an integrated extended Kalman lter (EKF) and Bayesian method is introduced for the MFS system. To monitor the MFS system, a residual estimation method using EKF is performed to capture the progress of the failure. Later, a transformation is utilized to obtain a new measure to estimate the degradation path (DP). Moreover, the recursive Bayesian algorithm is invoked to predict the RUL. Finally, relative accuracy (RA) measure is utilized to assess the performance of the proposed methods

Mining electric vehicle adoption of users

Rodrigues, R., Albuquerque, V., Ferreira, J. C., Dias, M. S., & Martins, A. L. (2021). Mining electric vehicle adoption of users. World Electric Vehicle Journal, 12(4), 1-31. [233]. https://doi.org/10.3390/wevj12040233 ------------------------------------------------------------------------------------- Funding Information: Funding: This research was funded by the Foundation for Science and Technology (FCT) through ISTAR-IUL’s project UIDB/04466/2020 and UIDP/04466/2020. Funding Information: Acknowledgments: J.C.F. received support from the Portuguese National Funds through FITEC— Programa Interface, with reference CIT INOV—INESC INOVAÇÃO—Financiamento Base. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.The increase of greenhouse gas emissions into the atmosphere, and their adverse effects on the environment, have prompted the search for alternative energy sources to fossil fuels. One of the solutions gaining ground is the electrification of various human activities, such as the transport sector. This trend has fueled a growing need for electrical energy storage in lithium batteries. Precisely knowing the degree of degradation that this type of battery accumulates over its useful life is necessary to bring economic benefits, both for companies and citizens. This paper aims to answer the current need by proposing two research questions about electric motor vehicles. The first focuses on habits EV owners practice, which may harm the battery life, and the second on factors that may keep consumers from purchasing this type of vehicle. This research work sought to answer these two questions, using a methodology from data science and statistical analysis applied to three surveys carried out on electric vehicle owners. The results allowed us to conclude that, except for the Year variable, all other factors had a marginal effect on the vehicles’ absolute autonomy degradation. Regarding obstacles of the adoption of electric vehicles, the biggest encountered was the insufficient coverage of the network of charging stations.publishersversionpublishe

Mining electric vehicle adoption of users

The increase of greenhouse gas emissions into the atmosphere, and their adverse effects on the environment, have prompted the search for alternative energy sources to fossil fuels. One of the solutions gaining ground is the electrification of various human activities, such as the transport sector. This trend has fueled a growing need for electrical energy storage in lithium batteries. Precisely knowing the degree of degradation that this type of battery accumulates over its useful life is necessary to bring economic benefits, both for companies and citizens. This paper aims to answer the current need by proposing two research questions about electric motor vehicles. The first focuses on habits EV owners practice, which may harm the battery life, and the second on factors that may keep consumers from purchasing this type of vehicle. This research work sought to answer these two questions, using a methodology from data science and statistical analysis applied to three surveys carried out on electric vehicle owners. The results allowed us to conclude that, except for the Year variable, all other factors had a marginal effect on the vehicles’ absolute autonomy degradation. Regarding obstacles of the adoption of electric vehicles, the biggest encountered was the insufficient coverage of the network of charging stations.info:eu-repo/semantics/publishedVersio