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The charging coordination of electric vehicle (EV) fleets in both temporal domain and spatial domain has attracted growing attention in recent years. Meanwhile, the uncertainties in EV arrival time and total available charging power from charging stations make the coordination problem highly dynamic and challenging. This paper develops a new temporal–spatial EV charging coordination scheme that jointly considers the above two major uncertainties. Firstly, EV charging scheduling (i.e., temporal coordination) is treated as a generalized Nash equilibrium game, in which each EV (including an upcoming EV) prefers to meet its own charging demand with minimized charging cost. The probability of EV charging availability is especially proposed to incorporate the charging demands of the upcoming EVs into the coordination scheme. In order to provide flexibility and private information protection, a distributed receding horizon optimization-based solution is developed, through which the Lagrange multipliers to reach the social equilibrium are determined via an iterative manner. The charging station selection is then recommended that minimizes the objective function over the entire optimization horizon. Finally, simulations under both small-scale and large-scale scenarios effectively demonstrate improved service quality of the EV charging, both in temporal and spatial domains, and avoidance of overload in charging stations. Results in a 150-EV scenario show that, averagely, the proposed method reduces battery SoC mismatch by 43% and increases degree of consistency by 5.9%. • Temporal uncertainty and spatial uncertainty are jointly addressed. • EV charging power fluctuations and shortage are considered. • Upcoming EVs are incorporated in the charging coordination scheme. • Battery SoC mismatch is reduced by 43% in a 150-EV simulation scenario. [ABSTRACT FROM AUTHOR]

Prognostics and health management (PHM) will ensure the safe and reliable operation of the battery systems. The remaining useful life (RUL) prediction as one of the major PHM strategies gives early warning of faults, which can be applied to recognize the necessary battery maintenance and replacement in advance. This study investigates a novel deep learning method for predicting lithium-ion battery RUL, which can learn the long-term dependency of degradation trend of batteries. This is the first time which a stacked bidirectional long short-term memory (SBLSTM) based on extreme gradient boosting (XGBoost) is applied to predict the battery capacity degradation trajectories. Using the XGBoost technique, important time-domain features are selected as multivariate inputs to feed the deep learning model for predicting. To improve the trained model, Bayesian optimization (BO) is also performed to tune the hyper-parameters. The findings show that the SBLSTM model achieves a low root mean square percentage error of 1.94%, which is lower than the state-of-the-art methods due to two-way learning. The suggested model will provide excellent support for the maintenance strategy development and health management of the battery systems. • Remaining useful life prediction using feature engineering through XGBoost. • SBLSTM performs well with multivariate time series input data. • Bayesian optimization performs to tune the hyper-parameters. • Precise and robust lifetime prediction for battery degradation with error low to 1.94%. [ABSTRACT FROM AUTHOR]

Contemporary industrial parks are challenged by the growing concerns about high cost and low efficiency of energy supply. Moreover, in the case of uncertain supply/demand, how to mobilize delay-tolerant elastic loads and compensate real-time inelastic loads to match multi-energy generation/storage and minimize energy cost is a key issue. Since energy management is hardly to be implemented offline without knowing statistical information of random variables, this paper presents a systematic online energy cost minimization framework to fulfill the complementary utilization of multi-energy with time-varying generation, demand and price. Specifically to achieve charging/discharging constraints due to storage and short-term energy balancing, a fast distributed algorithm based on stochastic gradient with two-timescale implementation is proposed to ensure online implementation. To reduce the peak loads, an incentive mechanism is implemented by estimating users' willingness to shift. Analytical results on parameter setting are also given to guarantee feasibility and optimality of the proposed design. Numerical results show that when the bid–ask spread of electricity is small enough, the proposed algorithm can achieve the close-to-optimal cost asymptotically. • A systematic online optimization framework ensuring provable performance for multi-energy system management is presented. • A method is proposed for estimating users' willingness to shift inelastic loads via public data. • The energy storage balance and real-time supply–demand balance can be achieved by two-timescale optimization. • Fast distributed method is proposed to deal with temporally-coupled constraints. [ABSTRACT FROM AUTHOR]

One of the main concerns of battery management systems is predicting the degradation of lithium-ion batteries, which remaining useful life prediction is an essential tool for prognostic and health management of batteries. In this study, we develop a novel prognostic architecture that is based on a least-squares generative adversarial network with the gated recurrent unit as the generator and multi-layer perceptron as the discriminator and use it to predict the Lithium-ion batteries' remaining useful life. The proposed method aims to learn the probability distribution of future values in an adversarial training fashion. This generative adversarial network gives more penalties to large errors and addresses the vanishing gradient problem during training. As a result, the predicted values will get closer to the actual data. Furthermore, to obtain high prediction accuracy, time-domain features are evaluated using statistical formulas. The most important features are then selected using the random forest algorithm and fed to the network as a multivariate input set. The performances of the proposed method are tested using a battery degradation dataset from the data repository of Prognostics Center of Excellence at NASA. Furthermore, experimental data from lithium-ion cells at different current rates are conducted for evaluation and verification. The obtained outcomes demonstrate that the designed model achieves the low prediction error of 2.63% and maximum absolute error of 0.02. • Remaining useful life prediction using feature engineering through Random Forest. • GRU-LSGAN performs well with multivariate time series input data. • Least-squares GAN performs to reduce the prediction error. • Precise and robust lifetime prediction for battery degradation with error low to 2.63%. [ABSTRACT FROM AUTHOR]

Electrochemical energy storage systems are fundamental to renewable energy integration and electrified vehicle penetration. Hybrid electrochemical energy storage systems (HEESSs) are an attractive option because they often exhibit superior performance over the independent use of each constituent energy storage. This article provides an HEESS overview focusing on battery-supercapacitor hybrids, covering different aspects in smart grid and electrified vehicle applications. The primary goal of this paper is to summarize recent research progress and stimulate innovative thoughts for HEESS development. To this end, system configuration, DC/DC converter design and energy management strategy development are covered in great details. The state-of-the-art methods to approach these issues are surveyed; the relationship and technological details in between are also expounded. A case study is presented to demonstrate a framework of integrated sizing formulation and energy management strategy synthesis. The results show that an HEESS with appropriate sizing and enabling energy management can markedly reduce the battery degradation rate by about 40% only at an extra expense of 1/8 of the system cost compared with battery-only energy storage. • Hybrid energy storage systems for electrified vehicle and smart grid are surveyed. • The operation principles and energy storage system requirements are provided. • System configuration, converter design and energy management are covered. • A case study is presented for integrated sizing and control strategy development. [ABSTRACT FROM AUTHOR]

• A multi-energy management framework including fuel cell vehicles and energy storage is proposed. • The synergies between hydrogen and electricity further improve the absorption of the renewable energy. • A joint algorithm is designed to optimize the long-term energy cost. The emerging paradigm of interconnected microgrids advocates energy trading or sharing among multiple microgrids. It helps make full use of the temporal availability of energy and diversity in operational costs when meeting various energy loads. However, energy trading might not completely absorb excess renewable energy. A multi-energy management framework including fuel cell vehicles, energy storage, combined heat and power system, and renewable energy is proposed, and the characteristics and scheduling arrangements of fuel cell vehicles are considered to further improve the local absorption of the renewable energy and enhance the economic benefits of microgrids. While intensive research has been conducted on energy scheduling and trading problem, a fundamental question still remains unanswered on microgrid economics. Namely, due to multi-energy coupling, stochastic renewable energy generation and demands, when and how a microgrid should schedule and trade energy with others, which maximizes its long-term benefit. This paper designs a joint energy scheduling and trading algorithm based on Lyapunov optimization and a double-auction mechanism. Its purpose is to determine the valuations of energy in the auction, optimally schedule energy distribution, and strategically purchase and sell energy with the current electricity prices. Simulations based on real data show that each individual microgrid, under the management of the proposed algorithm, can achieve a time-averaged profit that is arbitrarily close to an optimum value, while avoiding compromising its own comfort. [ABSTRACT FROM AUTHOR]

Signal transducer and activator of transcription 3 (STAT3) exerts a profound role in regulating mitochondrial function and cellular metabolism. Mitochondrial STAT3 supports RAS -dependent malignant transformation and tumor growth. However, whether pharmacological blockade of STAT3 leads to metabolic lethality in KRAS -mutant lung cancer remains unclear. Pyrvinium pamoate, a clinical antihelminthic drug, preferentially inhibited the growth of KRAS -mutant lung cancer cells in vitro and in vivo. Mechanistic study revealed that pyrvinium dose-dependently suppressed STAT3 phosphorylation at tyrosine 705 and serine 727. Overexpression mitochondrial STAT3 prominently weakened the therapeutic efficacy of pyrvinium. As a result of targeting STAT3, pyrvinium selectively triggered reactive oxygen species release, depolarized mitochondrial membrane potential and suppressed aerobic glycolysis in KRAS -mutant lung cancer cells. Importantly, the cytotoxic effects of pyrvinium could be significantly augmented by glucose deprivation both in vitro and in a patient-derived lung cancer xenograft mouse model in vivo. The combined efficacy significantly correlated with intratumoural STAT3 suppression. Our findings reveal that KRAS -mutant lung cancer cells are vulnerable to STAT3 inhibition exerted by pyrvinium, providing a promising direction for developing therapies targeting STAT3 and metabolic synthetic lethality for the treatment of KRAS -mutant lung cancer. [ABSTRACT FROM AUTHOR]