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Summary: With the rapid development of global electric vehicles, artificial intelligence, and aerospace, lithium‐ion batteries (LIBs) have become more and more widely used due to their high property. More and more disasters are caused by battery combustion. Among them, the temperature prediction of LIBs is the key to prevent the occurrence of fire. At present, using surface temperature sensor to measure the temperature of LIBs is the main method. High‐capacity LIB packs used in electric vehicles and grid‐tied stationary energy storage system essentially consist of thousands of individual LIB cells. Therefore, installing a physical sensor at each cell, especially at the cell core, is not practically feasible from the solution cost, space, and weight point of view. So developing a new method for battery temperature prediction has become an urgent problem to be solved. Electrochemical impedance spectroscopy (EIS) is a widely applied non‐destructive method of characterization of LIBs. In recent years, methods of predicting LIBs temperature by EIS have been developed. The prediction of LIBs temperature based on EIS has the advantages of high real‐time performance and prediction accuracy, and the device is simple and practical. The proposed method has a good development prospect in electric vehicles and other fields and can effectively solve the current problems of LIBs temperature prediction. Therefore, it is urgent to summarize these works to promote the next development. This review summarizes the main methods of using EIS to predict the temperature of LIBs in recent years, including the methods based on the impedance, phase shift, and intercept frequency. The principle and application of various methods are reviewed. The advantages and disadvantages of different methods and the future development direction are discussed. Highlights: Use EIS to quickly and effectively predict the internal temperature changes of LIBs.No hardware temperature sensors and thermal model are required.The methods to predict battery temperature based on impedance, phase shift, and intercept frequency are reviewed. [ABSTRACT FROM AUTHOR]

Summary: Implementing carbon neutrality and emission peak policies requires a high‐level electric vehicle field. Lithium‐ion batteries have been considered an essential component of electric vehicle power batteries. Effective state of charge (SOC) estimation for lithium‐ion batteries is a critical problem that needs to be addressed at present. With the feature extraction and fitting capability, the neural network can achieve accurate SOC estimation without considering the internal electrochemical state of the battery. This article overviews the definition of SOC and the relationship with battery aging state. Then, by examining recent literature on estimating the SOC of Lithium‐ion batteries using neural network methods, the methods are classified into three categories: feed‐forward neural network method, deep learning method, and hybrid method. The progress of neural network methods in SOC estimation applications is systematically reviewed, including principles, advantages, disadvantages, current status, and estimation errors. Possible recommendations for next‐generation intelligent battery management systems and SOC estimation are also presented. This review's highlighted insights will inspire researchers in the battery field and point the way to developing electric vehicles. [ABSTRACT FROM AUTHOR]

As a kind of unwelcome, unavoidable, and malicious behavior, distributed denial of service (DDoS) is an ongoing issue in today’s Internet as well as in some newly conceived future Internet architectures. Recently, a first step was made towards assessing DDoS attacks in Named Data Networking (NDN)—one of the promising Internet architectures in the upcoming big data era. Among them, interest flooding attack (IFA) becomes one of the main serious problems. Enlightened by the extensive study on the possibility of mitigating DDoS in today’s Internet by employing micropayments, in this paper we address the possibility of introducing economic levers, say, dynamic pricing mechanism, and so forth, for regulating IFA in NDN. [ABSTRACT FROM AUTHOR]

In this paper, the stability of two weakly coupled elastic beams connected vertically by a spring is investigated via the frequency domain method and the multiplier technique. When the two beams have partially local damping, the operator A is obtained via variable conversion, and it generating a semigroup is proved, then we obtain that the semigroup is exponentially stable by reduction to absurdity. [ABSTRACT FROM AUTHOR]

The concept of public key encryption with equality test was introduced at CT-RSA 2010. It has been used in many fields, especially in cloud storage. However, the previous schemes do not provide an effective authorization mechanism. To fill this gap, Ma et al. presented a public key encryption with equality test supporting flexible authorization based on the bilinear pairings. Recently, Lin et al. presented a pairing-free scheme that employs quadratic curve to perform the equality tests, which can achieve a trade-off between computational cost and storage space. In this article, we show that the equality test can be better performed by using a straight line, rather than a quadratic curve. Moreover, we simplify the encryption algorithm, as well as reduce the ciphertext storage space. [ABSTRACT FROM AUTHOR]

With the increasing carbon emissions worldwide, lithium-ion batteries have become the main component of energy storage systems for clean energy due to their unique advantages. Accurate and reliable state-of-charge (SOC) estimation is a central factor in the widespread use of lithium-ion batteries. This review, therefore, examines the recent literature on estimating the SOC of lithium-ion batteries using the hybrid methods of neural networks combined with Kalman filtering (NN-KF), classifying the methods into Kalman filter-first and neural network-first methods. Then the hybrid methods are studied and discussed in terms of battery model, parameter identification, algorithm structure, implementation process, appropriate environment, advantages, disadvantages, and estimation errors. In addition, this review also gives corresponding recommendations for researchers in the battery field considering the existing problems. [ABSTRACT FROM AUTHOR]

As a new type of energy-storage device, supercapacitors are widely used in various energy storage fields because of their advantages such as fast charging and discharging, high power density, wide operating temperature range, and long cycle life. However, the degradation and failure of supercapacitors in large-scale applications will adversely affect the operation of the whole system. To maximize the efficiency of supercapacitors without damaging the equipment and to ensure timely replacement before reaching the end of their useful life, it is critical to accurately predict the remaining useful life of supercapacitors. This paper presents a comprehensive review of model-based and data-driven approaches to predict the remaining useful life of supercapacitors, introduces the characteristics of the various methods, and foresees future trends, with the expectation of providing a reference for further research in this field. [ABSTRACT FROM AUTHOR]

Lithium battery state of health (SOH) is a key parameter to characterize the actual battery life. SOH cannot be directly measured. In order to further improve the accuracy of SOH estimation of lithium batteries, a model combining incremental capacity analysis (ICA) and bidirectional long- and short-term memory (Bi-LSTM) neural networks based on health characteristic parameters is proposed to predict the SOH of lithium-ion batteries. First, the health characteristic parameters are initially selected from the lithium battery charging curve, and the health characteristics are extracted by the Pearson correlation coefficient, including the charging time of constant current, charging time of constant voltage, voltage change rate from 300 s to 1000 s, 200s of voltage per cycle at a time. Second, ICA was used to deeply mine the deep associations related to SOH and the peaks of IC curves and their corresponding voltages were extracted as additional inputs to the model. Then, Bi-LSTM is used to form a combined SOH estimation model through adaptive weighting factors. Finally, the verification is based on the 5th battery parameters of the NASA lithium battery data set. The experimental results show that the proposed combined model reduces the mean square error by 55.17%, 49.28%, and 41.47%, respectively, compared with single models such as BP neural network (BPNN), LSTM, and gated recurrent neural network (GRU). [ABSTRACT FROM AUTHOR]

Secret sharing is an important aspect of key management in wireless ad hoc and sensor networks. In this paper, we define a new secure model of secret sharing, use the Lagrange interpolation and the bilinear cyclic groups to construct an efficient publicly verifiable secret sharing scheme on the basis of this model, and show that this scheme is provably secure against adaptively chosen secret attacks (CSAs) based on the decisional bilinear Diffie-Hellman (DBDH) problem. We find that this scheme has the following properties: (a) point-to-point secure channels are not required in both the secret distribution phase and the secret reconstruction phase; (b) it is a noninteractive secret sharing system in that the participants need not communicate with each other during subshadow verification; and (c) each participant is able to share many secrets with other participants despite holding only one shadow. [ABSTRACT FROM AUTHOR]