A Design for a Lithium-Ion Battery Pack Monitoring System Based on NB-IoT-ZigBee

With environmental issues arising from the excessive use of fossil fuels, clean energy has gained widespread attention, particularly the application of lithium-ion batteries. Lithium-ion batteries are integrated into various industrial products, which necessitates higher safety requirements. Narrowband Internet of Things (NB-IoT) is an LPWA (Low Power Wide Area Network) technology that provides IoT devices with low-power, low-cost, long-endurance, and wide-coverage wireless connectivity. This study addresses the shortcomings of existing lithium-ion battery pack detection systems and proposes a lithium-ion battery monitoring system based on NB-IoT-ZigBee technology. The system operates in a master-slave mode, with the subordinate module collecting and fusing multi-source sensor data, while the master control module uploads the data to local monitoring centers and cloud platforms via TCP and NB-IoT. Experimental validation demonstrates that the design functions effectively, accomplishing the monitoring and protection of lithium-ion battery packs in energy storage power stations

A Driver Modeling Based on the Preview-Follower Theory and the Jerky Dynamics

Based on the preview optimal simple artificial neural network driver model (POSANN), a new driver model, considering jerky dynamics and the tracing error between the real track and the planned path, is established. In this paper, the modeling for the driver-vehicle system is firstly described, and the relationship between weighting coefficients of driver model and system parameters is examined through test data. Secondly, the corresponding road test results are presented in order to verify the vehicle model and obtain the information on drive model and vehicle parameters. Finally, the simulations are carried out via CarSim. Simulation results indicate that the jerky dynamics need to be considered and the proposed new driver model can achieve a better path-following performance compared with the POSANN driver model

A Modeling Study of the Responses of Mesosphere and Lower Thermosphere Winds to Geomagnetic Storms at Middle Latitudes

Thermosphere Ionosphere Mesosphere Electrodynamics General Circulation Model (TIMEGCM) simulations are diagnostically analyzed to investigate the causes of mesosphere and lower thermosphere (MLT) wind changes at middle latitudes during the 17 April 2002 storm. In the early phase of the storm, middle‐latitude upper thermospheric wind changes are greater and occur earlier than MLT wind changes. The horizontal wind changes cause downward vertical wind changes, which are transmitted to the MLT region. Adiabatic heating and heat advection associated with downward vertical winds cause MLT temperature increases. The pressure gradient produced by these temperature changes and the Coriolis force then drive strong equatorward meridional wind changes at night, which expand toward lower latitudes. Momentum advection is minor. As the storm evolves, the enhanced MLT temperatures produce upward vertical winds. These upward winds then lead to a decreased temperature, which alters the MLT horizontal wind pattern and causes poleward wind disturbances at higher latitudes

On the Responses of Mesosphere and Lower Thermosphere Temperatures to Geomagnetic Storms at Low and Middle Latitudes

Observations from lidars and satellites have shown that large neutral temperature increases and decreases occur in the middle and low latitudes of the mesosphere and lower thermosphere region during geomagnetic storms. Here we undertake first-principles simulations of mesosphere and lower thermosphere temperature responses to storms using the Thermosphere Ionosphere Mesosphere Electrodynamics General Circulation Model to elucidate the nature and causes of these changes. Temperature variations were not uniform; instead, nighttime temperatures changed earlier than daytime temperatures, and temperatures changed earlier at high latitudes than at low ones. Furthermore, temperatures increased in some places/times and decreased in others. As the simulation behaves similar to observations, it provides an opportunity to understand physical processes that drive the observed changes. Our analysis has shown that they were produced mainly by adiabatic heating/cooling that was associated with vertical winds resulting from general circulation changes, with additional contributions from vertical heat advection

Chronic Myeloid Leukemia Patients Sensitive and Resistant to Imatinib Treatment Show Different Metabolic Responses

The BCR-ABL tyrosine kinase inhibitor imatinib is highly effective for chronic myeloid leukemia (CML). However, some patients gradually develop resistance to imatinib, resulting in therapeutic failure. Metabonomic and genomic profiling of patients' responses to drug interventions can provide novel information about the in vivo metabolism of low-molecular-weight compounds and extend our insight into the mechanism of drug resistance. Based on a multi-platform of high-throughput metabonomics, SNP array analysis, karyotype and mutation, the metabolic phenotypes and genomic polymorphisms of CML patients and their diverse responses to imatinib were characterized. The untreated CML patients (UCML) showed different metabolic patterns from those of healthy controls, and the discriminatory metabolites suggested the perturbed metabolism of the urea cycle, tricarboxylic acid cycle, lipid metabolism, and amino acid turnover in UCML. After imatinib treatment, patients sensitive to imatinib (SCML) and patients resistant to imatinib (RCML) had similar metabolic phenotypes to those of healthy controls and UCML, respectively. SCML showed a significant metabolic response to imatinib, with marked restoration of the perturbed metabolism. Most of the metabolites characterizing CML were adjusted to normal levels, including the intermediates of the urea cycle and tricarboxylic acid cycle (TCA). In contrast, neither cytogenetic nor metabonomic analysis indicated any positive response to imatinib in RCML. We report for the first time the associated genetic and metabonomic responses of CML patients to imatinib and show that the perturbed in vivo metabolism of UCML is independent of imatinib treatment in resistant patients. Thus, metabonomics can potentially characterize patients' sensitivity or resistance to drug intervention