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The thermal stability of MAPbI3 poses a challenge for the industry. To overcome this limitation, a thorough investigation of MAPbI3 is necessary. In this work, thermal gravimetric analysis (TGA) and Fourier transform infrared (FTIR) spectroscopy were conducted to identify the thermal decomposition products of MAPbI3, which were found to be CH3I, NH3, and PbI2. In situ X-ray diffraction (XRD) measurements were then performed in the temperature range from 300 to 700 K, which revealed the significant decomposition of the (110), (220), and (310) surfaces of MAPbI3 between 550 and 600 K. Density functional theory (DFT) calculations demonstrated that the (220) surface exhibited the highest stability. Additionally, the transition states of thermal decomposition showed that the energy barrier for the decomposition of the (110) surface was 2.07 eV. Our combined experimental and theoretical results provide a better understanding of the thermal decomposition mechanism of MAPbI3, providing valuable theoretical support for the design of long-term stable devices. [ABSTRACT FROM AUTHOR]

Objective: To investigate the genetic stability of Lacticaseibacillus rhamnosus Probio-M9 (L. rhamnosus Pro- bio-M9) during continuous passage. Methods: The morphological changes, carbohydrate utilization capacity, activity and metabolic characteristics of L. rhamnosus Probio-M9 were compared in the 0, 25, 50, 75 and 100 passages of the pro- biotic strain. At the same time, comparative genomics was used to analyze the changes of genome level during the con¬tinuous culture of 100 generations. Results: There were no significant changes (P >0.05) in phenotypic characteristics and metabolic characteristics of L. rhamnosus Probio-M9 during 100 generations of continuous culture, and the phenotyp- ic characteristics were stable, and the strain activity was maintained well. At the same time, using the genome of L. rhamnosus Probio-M9 as the reference sequence, 15 strains of different generations were sequenced, and the genome size and GC content showed no significant difference between the 15 strains cultured continuously and L. rhamnosus Probio- M9. Phylogenetic relationships showed that the 15 strains were closely related to the original genome. From the identified SNP loci, no mutation locus with stable inheritance was found in every two generations, and the collinear strain was good. The mutation level of the coding gene at the genome level confirmed that L. rhamnosus Probio-M9 had stable phe- notypic characteristics and metabolic characteristics during the continuous culture of 100 generations. Conclusion: L. rhamnosus Probio-M9 has good genetic stability during continuous passage, which provides a theoretical basis for further development, research and industrialization of L. rhamnosus Probio-M9. [ABSTRACT FROM AUTHOR]

Accurately evaluating the technological improvement effects of wind turbines is crucial for wind farm operators. To this end, this paper proposes an innovative approach that employs a wind power regression model which leverages external environmental information to predict the output power of wind turbines. The effectiveness of technological improvements can be evaluated by comparing the predicted output power with the measured output power. In this paper, a model called stacked LSTM networks with attention mechanisms is designed. In the proposed model, the stacked LSTM networks are used to enhance the nonlinear fitting ability and capture deeper features of the input sequence. Furthermore, temporal attention mechanisms are employed to make the model focus on important time-series information of the data. In addition, a hierarchical attention mechanism is designed to explore the correlation among the outputs of the stacked LSTM networks and enrich the model's output information. The experiments on the data from a wind farm show that the proposed method outperforms various wind power prediction benchmarks, achieving lower RMSE, MAE, and MAPE values of 142.82, 104.2, and 4.85%, respectively. [ABSTRACT FROM AUTHOR]

In real industrial electronic applications that involve batteries, the inevitable health degradation of batteries would result in both the shorter battery service life and decreased performance. In this article, an attention-based model is proposed for Li-ion battery calendar health prognostics, i.e., the capacity forecaster based on knowledge-data-driven attention (CFKDA), which will be the first work that applies attention mechanism to benefit battery calendar health monitor and management. By taking the battery empirical knowledge as the foundation of its crucial part, i.e., the knowledge-driven attention module, the CFKDA has realized a satisfactory combination of the complementary domain knowledge and data, which has improved both its theoretic strength and prognostic performance significantly. Experimental studies on practical battery calendar ageing demonstrate the superiority of CFKDA in forecasting and generalizing to unwitnessed conditions over both state-of-the-art knowledge-driven and data-driven calendar health prognostic models, implying that the introduction of domain knowledge in CFKDA has brought a significant performance improvement. [ABSTRACT FROM AUTHOR]

The popularization of electric vehicles (EVs) and charging stations has been threatening the distribution network’s reliability and efficiency. The prediction of EV charging demand can benefit the optimization of the operation of energy-transportation nexus and improve social welfare toward a low carbon future. In this article, a short-term probabilistic charging demand forecast model is proposed to estimate the quantiles of future charging demand of a charging station 15 min ahead, i.e., the self-attention-based machine theory of mind (SAMToM). The SAMToM has considered both the users’ historical charging habits (schedules) and the current trend of charging demand variation using the framework of machine theory of mind (MToM), and real-world-data-based case studies have verified its superiority in EV charging demand forecast over state-of-the-arts. Moreover, analyses show that the advantage of SAMToM lies in the following aspects. 1) The self-attention layers have mitigated the long-range forgetting in SAMToM. 2) The MToM architecture enables SAMToM to balance historical charging habits and current charging demand variation trends well. 3) Using a quantile forecast evaluation metric as the loss function, i.e., the continuous ranked probability score (CRPS), enables SAMToM to aim directly at the highest quality of forecasted quantiles. [ABSTRACT FROM AUTHOR]

Battery-based energy storage system is a key component to achieve low carbon industrial and social economy, where battery health status plays a vital role in determining the safety and reliability of energy-transportation nexus. This article proposes a transferred recurrent neural network (RNN)-based framework to achieve efficient calendar capacity prognostics under both witnessed and unwitnessed storage conditions. Specifically, this transferred RNN framework contains a base model part and a transfer model part. The base model is first trained by using the easily collected and time-saving accelerated ageing dataset from high temperature and state-of-charge (SOC) cases. Then the transfer part is tuned by using only a small portion of starting capacity data from unwitnessed condition of interest. The developed framework is evaluated under a well-rounded ageing dataset with three different storage SOCs (20%, 50%, and 90%) and temperatures (10 °C, 25 °C, and 45 °C). Experimental results demonstrate that the derived transferred RNN framework is capable of providing satisfactory calendar capacity health prognostics under different storage cases. A model structure with the impact factor terms of SOC and temperature outperforms other counterparts especially for the unwitnessed conditions. The proposed framework could assist engineers to significantly reduce battery ageing experiment burden and is also promising to capture future capacity information for battery health and life-cycle cost analysis of energy-transportation applications. [ABSTRACT FROM AUTHOR]

The inevitable battery ageing is a bottleneck that hinders the advancement of battery-based energy storage systems. Developing a feasible health assessment strategy for battery pack is important but challenging due to the joint requirements of the computational burden, modeling cost, estimation accuracy, and battery equalization. This article proposes a balancing current ratio (BCR) based solution to achieve reliable state-of-health (SoH) estimations of all series-connected cells within a pack while significantly reduce the overall reliance on cell-level battery models. Specifically, after employing BCR to describe the properties of the balancing process, the voltage-based active balancing is combined into the SoH estimator design for the first time, leading to a weighted fusion strategy to effectively estimate SoHs of all cells within a pack. Hardware-in-the-loop experiments show that even if a parameter-fixed open-circuit-voltage-resistance model is used for modeling, the typical estimation error of our proposed solution can still be bounded by only 1.5%, which is 70% lower than that of the benchmarking algorithms. Due to the model-free nature of the integrated voltage-based balancing, the robustness and flexibility of the proposed pack SoH estimation solution are also significantly improved. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries have been widely applied in energy conversion sectors, where effective future ageing prediction is crucial to guarantee their safety and performance. Due to the highly nonlinear ageing behaviours, developing a reliable method that could not only consider the knee point effect but also predict the future ageing trajectory with uncertainty quantification poses a formidable task. This paper derives a machine learning solution, based on the migrated Gaussian process regression (GPR), for predicting future battery two-stage ageing trajectory. Specifically, a base model is first offline identified from the easier collected accelerated-speed ageing data, through which the long life ageing information can be effectively learned. With this base model, a migrated mean function is then designed and coupled within the GPR framework for battery ageing predictions. Experimental data from three different batteries are applied for model validation and performance evaluation. Results indicate that the proposed solution leads to effective improvements in prediction accuracy and uncertainty quantification for both cases of training before and after the knee point. This is the first time to couple migration concept within GPR, paving the way to reduce experimental cost and predict battery future two-stage ageing trajectory with only a few (first 30%) data available. [ABSTRACT FROM AUTHOR]