34 research outputs found
Toward Enhanced State of Charge Estimation of Lithium-ion Batteries Using Optimized Machine Learning Techniques.
State of charge (SOC) is a crucial index used in the assessment of electric vehicle (EV) battery storage systems. Thus, SOC estimation of lithium-ion batteries has been widely investigated because of their fast charging, long-life cycle, and high energy density characteristics. However, precise SOC assessment of lithium-ion batteries remains challenging because of their varying characteristics under different working environments. Machine learning techniques have been widely used to design an advanced SOC estimation method without the information of battery chemical reactions, battery models, internal properties, and additional filters. Here, the capacity of optimized machine learning techniques are presented toward enhanced SOC estimation in terms of learning capability, accuracy, generalization performance, and convergence speed. We validate the proposed method through lithium-ion battery experiments, EV drive cycles, temperature, noise, and aging effects. We show that the proposed method outperforms several state-of-the-art approaches in terms of accuracy, adaptability, and robustness under diverse operating conditions
On block diagonal-Schur complements of the block strictly doubly diagonally dominant matrices
The impacts of market reform on the market penetration of natural gas-fired electricity and renewable energy in China
Prognostic Significance of E-cadherin Expression and Peripheral Blood Micrometastasis in Gastric Carcinoma Patients
An evaluation of reordering algorithms to reduce the computational cost of the incomplete Cholesky-conjugate gradient method
Parametric control of collision rates and capture rates in geometrically enhanced differential immunocapture (GEDI) microfluidic devices for rare cell capture
<it>Arctium lappa</it> ameliorates endothelial dysfunction in rats fed with high fat/cholesterol diets
<p>Abstract</p> <p>Background</p> <p><it>Arctium lappa</it> L. (Asteraceae), burdock, is a medicinal plant that is popularly used for treating hypertension, gout, hepatitis, and other inflammatory disorders. This study was performed to test the effect of ethanol extract of <it>Arctium lappa</it> L. (EAL) seeds on vascular reactivity and inflammatory factors in rats fed a high fat/cholesterol diet (HFCD).</p> <p>Method</p> <p>EAL-I (100 mg·kg<sup>−1</sup>/day), EAL-II (200 mg·kg<sup>−1</sup>/day), and fluvastatin (3 mg·kg<sup>−1</sup>/day) groups initially received HFCD alone for 8 weeks, with EAL supplementation provided during the final 6 weeks.</p> <p>Results</p> <p>Treatment with low or high doses of EAL markedly attenuated plasma levels of triglycerides and augmented plasma levels of high-density lipoprotein (HDL) in HFCD-fed rats. Chronic treatment with EAL markedly reduced impairments of acetylcholine (ACh)-induced relaxation of aortic rings. Furthermore, chronic treatment with EAL significantly lowered systolic blood pressure (SBP) and maintained smooth and flexible intimal endothelial layers in HFCD-fed rats. Chronic treatment with EAL suppressed upregulation of intercellular adhesion molecule (ICAM)-1, vascular cell adhesion molecule (VCAM)-1, and E-selectin in the aorta. Chronic treatment with EAL also suppressed increases in matrix metalloproteinase (MMP)-2 expression. These results suggested that EAL can inhibit HFCD-induced vascular inflammation in the rat model.</p> <p>Conclusion</p> <p>The present study provides evidence that EAL ameliorates HFCD-induced vascular dysfunction through protection of vascular relaxation and suppression of vascular inflammation.</p