Co-estimation of state-of-charge and state-of-health for high-capacity lithium-ion batteries.

To address the challenges of efficient state monitoring of lithium-ion batteries in electric vehicles, a co-estimation algorithm of state-of-charge (SOC) and state-of-health (SOH) is developed. The algorithm integrates techniques of adaptive recursive least squares and dual adaptive extended Kalman filtering to enhance robustness, mitigate data saturation, and reduce the impact of colored noise. At 25C, the algorithm is tested and verified under dynamic stress test (DST) and Beijing bus DST conditions. Under the Beijing bus DST condition, the algorithm achieves a mean absolute error (MAE) of 0.17% and a root mean square error (RMSE) of 0.19% for SOC estimation, with a convergence time of 4 s. Under the DST condition, the corresponding values are 0.05% for MAE, 0.07% for RMSE, and 5 s for convergence time. Moreover, in this research, the SOH is described as having internal resistance. Under the Beijing bus DST condition, the MAE and the RMSE of the estimated internal resistance of the proposed approach are 0.018% and 0.075%, with the corresponding values of 0.014% and 0.043% under the DST condition. The results of the experiments provide empirical evidence for the challenges associated with the efficacious estimation of SOC and SOH

Improving Factual Consistency of Text Summarization by Adversarially Decoupling Comprehension and Embellishment Abilities of LLMs

Despite the recent progress in text summarization made by large language models (LLMs), they often generate summaries that are factually inconsistent with original articles, known as "hallucinations" in text generation. Unlike previous small models (e.g., BART, T5), current LLMs make fewer silly mistakes but more sophisticated ones, such as imposing cause and effect, adding false details, overgeneralizing, etc. These hallucinations are challenging to detect through traditional methods, which poses great challenges for improving the factual consistency of text summarization. In this paper, we propose an adversarially DEcoupling method to disentangle the Comprehension and EmbellishmeNT abilities of LLMs (DECENT). Furthermore, we adopt a probing-based efficient training to cover the shortage of sensitivity for true and false in the training process of LLMs. In this way, LLMs are less confused about embellishing and understanding; thus, they can execute the instructions more accurately and have enhanced abilities to distinguish hallucinations. Experimental results show that DECENT significantly improves the reliability of text summarization based on LLMs

Recent progress in noble-metal-free electrocatalysts for alkaline oxygen evolution reaction

The practical application of splitting water to generate hydrogen is to a large extent hindered by an oxygen evolution reaction (OER) process. Electrocatalysts with low-cost, high activity, and durability are essential for the low kinetic threshold of the OER. Despite the high active performances of noble metal compound electrocatalysts like IrO2 and RuO2, they are heavily restricted by the high cost and scarcity of noble metal elements. In this context, noble-metal-free electrocatalysts have acquired increasing significance in recent years. So far, a broad spectrum of noble-metal-free electrocatalysts has been developed for improved OER performance. In this review, three types of electrolysis and some evaluation criteria are introduced, followed by recent progress in designing and synthesizing noble-metal-free alkaline OER electrocatalysts, with the classification of metal oxides/(oxy)hydroxides, carbon-based materials, and metal/carbon hybrids. Finally, perspectives are also provided on the future development of the alkaline OER on active sites and stability of electrocatalysts

Hepcidin and iron metabolism in preterm infants

Background: Iron deficiency (ID) and ID anemia are widespread in low-income countries, particularly among preterm infants. Hepcidin is a key regulator of iron metabolism, which offers the possibility of new solutions to diagnose ID in premature infants. Objective: To explore the relationship between iron metabolism and hepcidin in premature infants. Materials and methods: The study involved 81 preterm infants between 28+1 and 36+6 who underwent iron status indicators and hepcidin testing at 6 months of corrected gestational age. The preterm infants were divided into two groups based on iron status indicators: ID and no ID. Results: Serum hepcidin was lower for premature infants with ID compared to those without ID (log10hepcidin, 1.18 ± 0.44 vs 1.49 ± 0.37, p = 0.002). A single-variate linear regression model was used to explore the correlation between hepcidin and other indicators of iron metabolism. A strongly positive relationship was observed between hepcidin levels and ferritin levels (p < 0.001) in the correlation analysis. Conclusions: Hepcidin can be used as an efficient indicator of iron storage and a promising indicator for the early diagnosis of ID in premature infants

A novel strategy for power sources management in connected plug-in hybrid electric vehicles based on mobile edge computation framework

This paper proposes a novel control framework and the corresponding strategy for power sources management in connected plug-in hybrid electric vehicles (cPHEVs). A mobile edge computation (MEC) based control framework is developed first, evolving the conventional on-board vehicle control unit (VCU) into the hierarchically asynchronous controller that is partly located in cloud. Elaborately contrastive analysis on the performance of processing capacity, communication frequency and communication delay manifests dramatic potential of the proposed framework in sustaining development of the cooperative control strategy for cPHEVs. On the basis of MEC based control framework, a specific cooperative strategy is constructed. The novel strategy accomplishes energy flow management between different power sources with incorporation of the active energy consumption plan and adaptive energy consumption management. The method to generate the reference battery state-of-charge (SOC) trajectories in energy consumption plan stage is emphatically investigated, fast outputting reference trajectories that are tightly close to results by global optimization methods. The estimation of distribution algorithm (EDA) is employed to output reference control policies under the specific terminal conditions assigned via the machine learning based method. Finally, simulation results highlight that the novel strategy attains superior performance in real-time application that is close to the offline global optimization solutions