An in-depth analysis of electric vehicle charging station infrastructure, policy implications, and future trends

Monitoring vibrations in rotating machinery allows effective diagnostics, as abnormal functioning states are related to specific patterns that can be extracted from vibration signals. Extensively studied issues concern the different methodologies used for carrying out the main phases (signal measurements, pre-processing and processing, feature selection, and fault diagnosis) of a malfunction automatic diagnosis. In addition, vibration-based condition monitoring has been applied to a number of different mechanical systems or components. In this review, a systematic study of the works related to the topic was carried out. A preliminary phase involved the analysis of the publication distribution, to understand what was the interest in studying the application of the method to the various rotating machineries, to identify the interest in the investigation of the main phases of the diagnostic process, and to identify the techniques mainly used for each single phase of the process. Subsequently, the different techniques of signal processing, feature selection, and diagnosis are analyzed in detail, highlighting their effectiveness as a function of the investigated aspects and of the results obtained in the various studies. The most significant research trends, as well as the main innovations related to the various phases of vibration-based condition monitoring, emerge from the review, and the conclusions provide hints for future ideas. 

/pdf/a-review-on-vibration-based-condition-monitoring-of-rotating-1rwkagxg.pdf

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

The rise of greenhouse gas levels in the atmosphere is a severe climate change concern. A significant part, such as CO2 emission, comes from internal combustion engine-driven vehicles, incited the automotive sector to focus more on the sustainable electric transportation system. However, electric vehicles face significant charging time, charging methods, and range anxiety challenges. To overcome these challenges, charging technologies for electric vehicle batteries play an essential role. Many types of electric vehicle charging topologies have been discussed in the literature and implemented in many practical applications. This paper presents a state of art criticism of advanced converter topologies and charging methodology for electric vehicle applications. Apart from the conventional topologies, this manuscript has covered the comparative criticism of the recently proposed EV charging technologies regarding charging methods, control strategies, and power levels. Further, this paper discussed the different onboard chargers with their power factor correction topologies, drawbacks, and required corrections. This manuscript provides the research directions for the academic and industrial communities. 

A comprehensive review on advanced charging topologies and methodologies for electric vehicle battery

This review provides an overview of new strategies to address the current challenges of automotive battery systems: Intelligent Battery Systems. They have the potential to make battery systems more performant and future-proof for coming generations of electric vehicles. The essential features of Intelligent Battery Systems are the accurate and robust determination of cell individual states and the ability to control the current of each cell by reconfiguration. They enable high-level functions like fault diagnostics, multi-objective balancing strategies, multilevel inverters, and hybrid energy storage systems. State of the art and recent advances in these topics are compiled and critically discussed in this article. A comprising, critical discussion of the implementation aspects of Intelligent Battery Systems complements the review. We touch on sensing, battery topologies and management, switching elements, communication architecture, and impact on the single-cell. This review contributes to transferring the best technologies from research to product development.

Critical Review of Intelligent Battery Systems: Challenges, Implementation, and Potential for Electric Vehicles

The selection of the lithium-ion battery chemistry is a crucial step when designing a certain application that includes an energy storage device, as it could limit the lifetime of the system. This paper presents two empirical cycling degradation models designed for NMC and LFP lithium-ion battery chemistries. The novel contribution of the models consists on representing the effect of the degradation stress factors as function of battery chemistries, rather than single cell references as typically approached in the literature. This know-how is claimed to be potentially valuable in order to compare characteristics of different lithium-ion battery chemistries and therefore help in the selection of the definite cell reference to be integrated in a certain application. A methodology is proposed for the development of such empirical degradation models, which are built upon large sets of degradation data collected from the literature and include information of diverse cell references and stress factors. A review of the state-of-art reveals that the data of NMC and LFP cathode chemistries is enough rich to develop degradation models that consider the main cycling stress factors accelerating battery ageing – number of full equivalent cycles, operation temperature, depth-of-discharge, charge and discharge current rate, and state-of-charge. Eventually, the cycling degradation models are constructed based on data from 232 degradation tests for NMC and 85 degradation tests for LFP. The obtained models are validated and mean absolute errors of 2.49% (NMC model) and 2.18% (LFP model) are achieved. An analysis of the models is conducted focusing on the effect that each stress factor has on the overall degradation of both lithium-ion battery chemistries. Finally, some case studies based on typical applications are proposed in order to calculate the lifetimes of each battery chemistry and evaluate the most appropriate option for each application.

Modelling the cycling degradation of Li-ion batteries: Chemistry influenced stress factors

While the constant current charge time (CCCT) and constant voltage charge time (CVCT) are increasingly used for the state of health (SOH) estimation of Li-ion batteries, their correlations with battery degradation are not investigated comprehensively. This paper develops an analytical model to quantify the chargeable capacity of a Li-ion battery under a CC-CV profile, in which CCCT and CVCT are identified as two uncoupled parameters. The model is verified using a battery dataset of cycling tests subjected to 19 different test conditions with different discharge currents, ambient temperatures, and rest times. The behaviors of CCCT and CVCT during battery degradation are studied in terms of chargeable capacity fade. A new health indicator, the CV-CC time ratio, is developed for degradation analysis. Two partial CC-CV cases are also considered in the studies: in one case, the partial CC-CV charge starts after different discharged depths, and in the other case, different fast charging policies are executed before the partial CC-CV charge.

An analytical model for the CC-CV charge of Li-ion batteries with application to degradation analysis

High data throughput during real-time vibration monitoring can easily lead to network congestion, insufficient data storage space, heavy computing burden, and high communication costs. As a new computing paradigm, edge computing is deemed to be a good solution to these problems. In this paper, perceptual hashing is proposed as an edge computing form, aiming not only to reduce the data dimensionality but also to extract and represent the machine condition information. A sub-band coding method based on wavelet packet transform, two-dimensional discrete cosine transform, and symbolic aggregate approximation is developed for perceptual vibration hashing. When the sub-band coding method is implemented on a monitoring terminal, the acquired kilobyte-long vibration signal can be transformed into a machine condition hash occupying only a few bytes. Therefore, the efficiency of condition monitoring can benefit from the compactness of the machine condition hash, while comparable diagnostic and prognostic results can still be achieved. The effectiveness of the developed method is verified with two benchmark bearing datasets. Considerations on practical condition monitoring applications are also presented.

/pdf/perceptual-vibration-hashing-by-sub-band-coding-an-edge-53y2yaxs8e.pdf

Perceptual Vibration Hashing by Sub-Band Coding: An Edge Computing Method for Condition Monitoring

A flexible micro direct methanol fuel cells array based on FPCB

This paper develops generalized sparse filtering (GSF) by applying general norm normalization to improve the feature learning ability. A rotating machinery fault diagnosis method is then developed by combining the GSF and softmax regression. A rolling bearing dataset is applied to validate the performance of the developed method. The influences of normalization parameters on the diagnostic performance are investigated in detail, and thus, the best parameter combinations are determined based on the diagnostic accuracy and computing time. A planetary gearbox dataset is also applied to further validate the diagnostic performance on rotating machinery. Finally, the mechanism of the GSF is explained using a simple example. The results show that the GSF has a more powerful feature learning capacity than standard sparse filtering, and the developed method can obtain excellent diagnostic performance. Two variants of the developed method are recommended for the rotating machinery fault diagnosis.

Generalized sparse filtering for rotating machinery fault diagnosis

Intelligent fault diagnosis using an unsupervised sparse feature learning method

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