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Coal will continue to be the main energy source in China for the immediate future, although the environmental pollution and ecological impacts of each stage in the full life cycle of coal mining, transportation, and combustion generate large quantities of external costs. The Late Permian coals in southwestern (SW) China usually contain high amounts of fluorine (F), arsenic (As), and ash, which together with high-F clays cause abnormally high levels of endemic fluorosis, As poisoning, and lung cancer in areas where coal is mined and burned. In this paper, we estimate the external costs of the life cycle of coal. The results show that the externalities of coal in SW China are estimated at USD 73.5 billion or 284.3 USD/t, which would have accounted for 6.5 % of the provincial GDP in this area in 2018. The external cost of human health accounts for 87.2% of the total external costs, of which endemic skeletal fluorosis diseases and related lung cancers have the most important impact. Our study provides a more precise estimate of externalities compared with its counterparts in other provinces in China. Therefore, several policy recommendations would be proposed to internalize the external cost. [ABSTRACT FROM AUTHOR]

Extreme rainfall may induce flooding failures of electricity facilities, which poses power systems in a risk of power supply interruption. To quantitatively estimate the risk of power system operation under extreme rainfall, a multi-scenario stochastic risk assessment method was proposed. First, a scenario generation scheme considering waterlogged faults of power facilities was constructed based on the storm water management model (SWMM) and the extreme learning machine method. These scenarios were merged with several typical scenario sets for further processing. The outage of power facilities will induce power flow transfer which may consequently lead to transmission lines' thermal limit violation. Semi-invariant and Gram–Charlier level expansion methods were adopted to analytically depict the probability density function and cumulative probability function of each line's power flow. The optimal solution was performed by a particle swarm algorithm to obtain proper load curtailment at each bus, and consequently, the violation probability of line thermal violations can be controlled within an allowable range. The volume of load curtailment as well as their importance were considered to quantitatively assess the risk of power supply security under extreme precipitation scenarios. The effectiveness of the proposed method was verified in case studies based on the Southeast Australia Power System. Simulation results indicated that the risk of load shedding in extreme precipitation scenarios can be quantitatively estimated, and the overload probability of lines can be controlled within the allowable range through the proposed optimal load shedding scheme. [ABSTRACT FROM AUTHOR]

With the increasing application of lithium-ion batteries, accurate estimation of the state of health (SOH) of lithium-ion batteries is of great significance for the safe operation of lithium-ion battery systems and the reduction of operation and maintenance costs. The complex physical and chemical reactions inside the lithium battery and the complex external working conditions make it challenging to achieve an accurate health-state estimation and life prediction. Therefore, the accurate estimation of the SOH of lithium-ion batteries is an important issue. At present, electrochemical impedance spectroscopy (EIS) is widely used in the study of battery-power impedance characteristics and battery-state estimation due to its advantage of nondestructive measurement. For this reason, this paper summarizes the research progress of lithium-ion SOH estimation based on EIS in recent years and details it layer by layer, mainly from two aspects: first, the quantitative relationship model between the characteristic parameters and SOH is established by constructing a frequency domain-equivalent circuit model. Secondly, we construct a quantitative relationship model between EIS data and SOH using the data-driven method. Finally, the advantages and disadvantages of different methods and estimation accuracy are analyzed and compared, and the future estimation of SOH based on EIS is prospected. [ABSTRACT FROM AUTHOR]

As an important energy storage device, lithium-ion batteries (LIBs) have been widely used in various fields due to their remarkable advantages. The high level of precision in estimating the battery's state of health greatly enhances the safety and dependability of the application process. In contrast to traditional model-based prediction methods that are complex and have limited accuracy, data-driven prediction methods, which are considered mainstream, rely on direct data analysis and offer higher accuracy. Therefore, this paper reviews how to use the latest data-driven algorithms to predict the SOH of LIBs, and proposes a general prediction process, including the acquisition of datasets for the charging and discharging process of LIBs, the processing of data and features, and the selection of algorithms. The advantages and limitations of various processing methods and cutting-edge data-driven algorithms are summarized and compared, and methods with potential applications are proposed. Effort was also made to point out their application methods and application scenarios, providing guidance for researchers in this area. [ABSTRACT FROM AUTHOR]

Highlights: What are the main findings? Rapid acquisition technology of electrochemical impedance spectroscopy. EIS was used to quickly and effectively estimate the SOH of LIBs. Machine learning method and equivalent circuit method to estimate battery health state. What is the implication of the main finding? EIS were found to be better SOH estimation method. The technology based on EIS has the characteristics of high precision advantages. The method based on EIS of LIBs SOH estimation has been applied to the BMS. Lithium-ion batteries stand out from other clean energy sources because of their high energy density and small size. With the increasing application scope and scale of lithium-ion batteries, real-time and accurate monitoring of its state of health plays an important role in ensuring the healthy and stable operation of an energy storage system. Due to the interaction of various aging reactions in the aging process of lithium-ion batteries, the capacity attenuation shows no regularity. However, the traditional monitoring method is mainly based on voltage and current, which cannot reflect the internal mechanism, so the accuracy is greatly reduced. Recently, with the development of electrochemical impedance spectroscopy, it has been possible to estimate the state of health quickly and accurately online. Electrochemical impedance spectroscopy can measure battery impedance in a wide frequency range, so it can reflect the internal aging state of lithium-ion batteries. In this paper, the latest impedance spectroscopy measurement technology and electrochemical impedance spectroscopy based on lithium-ion battery health state estimation technology are summarized, along with the advantages and disadvantages of the summary and prospects. This fills the gap in this aspect and is conducive to the further development of this technology. [ABSTRACT FROM AUTHOR]

To adapt to frequent charge and discharge and improve the accuracy in the DC microgrid with independent photovoltaics and distributed energy storage systems, an energy-coordinated control strategy based on increased droop control is proposed in this paper. The overall power supply quality of the DC microgrid is improved by optimizing the output priority of the multi-energy storage system. When photovoltaic and energy storage work simultaneously, the proposed method can dynamically adjust their working state and the energy storage unit's droop coefficient to meet the system's requirements. In DC microgrids with energy storage units of different capacities, the proposed strategy can be used to maintain the stability of bus voltage, improve the equalization speed and accuracy of the energy storage state of charge, and avoid the shutdown of energy storage units due to overcharge or discharge. Verification of the proposed strategy is implemented with MATLAB/Simulink. The simulation results show the proposed control strategy's effectiveness in balancing energy supply and demand and reducing the time of charging and discharging energy storage units. [ABSTRACT FROM AUTHOR]

Highlights: EIS was used to estimate the SOH of LIBs found to be fast and effective. It is more convenient to use CNN to extract features of EIS data automatically. The improved ECM method and IPSO-CNN-BiLSTM method are proposed in this paper. IPSO algorithm was first proposed to applied to the problem of optimizing the initial parameters of the neural networks. The state of health (SOH) is critical to the efficient and reliable use of lithium-ion batteries (LIBs). Recently, the SOH estimation method based on electrochemical impedance spectroscopy (EIS) has been proven effective. In response to different practical applications, two models for SOH estimation are proposed in this paper. Aiming at based on the equivalent circuit model (ECM) method, a variety of ECMs are proposed. Used EIS to predict the ECM, the results show that the improved method ensures the correctness of the ECM and improves the estimation results of SOH. Aiming at a data-driven algorithm, proposes a convolution neural network (CNN) to process EIS data which can not only extract the key points but also simplifies the complexity of manual feature extraction. The bidirectional long short-term memory (BiLSTM) model was used for serial regression prediction. Moreover, the improved Particle Swarm Optimization (IPSO) algorithm is proposed to optimize the model. Comparing the improved model (IPSO-CNN-BiLSTM) with the traditional PSO-CNN-BiLSTM, CNN-BiLSTM and LSTM models, the prediction results are improved by 13.6%, 93.75% and 94.8%, respectively. Besides that, the two proposed methods are 27% and 35% better than the existing gaussion process regression (GPR) model, which indicates that the proposed improved methods are more flexible for SOH estimation with higher precision. [ABSTRACT FROM AUTHOR]

In this paper, we propose an optimized power distribution method for hybrid electric energy storage systems for electric vehicles (EVs). The hybrid energy storage system (HESS) uses two isolated soft-switching symmetrical half-bridge bidirectional converters connected to the battery and supercapacitor (SC) as a composite structure of the protection structure. The bidirectional converter can precisely control the charge and discharge of the SC and battery. Spiral wound SCs with mesoporous carbon electrodes are used as the energy storage units of EVs. Under the 1050 operating conditions of the EV driving cycle, the SC acts as a "peak load transfer" with a charge and discharge current of 2isc~3ibat. An improved energy allocation strategy under state of charge (SOC) control is proposed, that enables SC to charge and discharge with a peak current of approximately 4ibat. Compared with the pure battery mode, the acceleration performance of the EV is improved by approximately 50%, and the energy loss is reduced by approximately 69%. This strategy accommodates different types of load curves, and helps improve the energy utilization rate and reduce the battery aging effect. [ABSTRACT FROM AUTHOR]