Thymosin Beta 4 Is Involved in the Development of Electroacupuncture Tolerance

Background: Electroacupuncture (EA) tolerance, a negative therapeutic effect, is a gradual decline in antinociception because of its repeated or prolonged use. This study aims to explore the role of thymosin beta 4 (Tβ4), having neuro-protection properties, in EA tolerance (EAT).Methods: Rats were treated with EA once daily for eight consecutive days to establish EAT, effect of Tβ4 on the development of EAT was determined through microinjection of Tβ4 antibody and siRNA into the cerebroventricle. The mRNA and protein expression profiles of Tβ4, opioid peptides (enkephalin, dynorphin and endorphin), and anti-opioid peptides (cholecystokinin octapeptide, CCK-8 and orphanin FQ, OFQ), and mu opioid receptor (MOR) and CCK B receptor (CCKBR) in the brain areas (hypothalamus, thalamus, cortex, midbrain and medulla) were characterized after Tβ4 siRNA was administered.Results: Tβ4 levels were increased at day 1, 4, and 8 and negatively correlated with the changes of tail flick latency in all areas. Tβ4 antibody and siRNA postponed EAT. Tβ4 siRNA caused decreased Tβ4 levels in all areas, which resulted in increased enkephalin, dynorphin, endorphin and MOR levels in most measured areas during repeated EA, but unchanged OFQ, CCK-8, and CCKBR levels in most measured areas. Tβ4 levels were negatively correlated with enkephalin, dynorphin, endorphin, or MOR levels in all areas except medulla, while positively correlated with OFQ and CCK-8 levels in some areas.Conclusion: These results confirmed Tβ4 facilitates EAT probably through negatively changing endogenous opioid peptides and their receptors and positively influencing anti-opioid peptides in the central nervous system

Nonlinear Model Predictive Control with Terminal Cost for Autonomous Vehicles Trajectory Follow

This paper presents a nonlinear model predictive control with terminal cost (NMPC–WTC) algorithm and its open/closed-loop system analysis and simulation validation for accurate and stable path tracking of autonomous vehicles. The path tracking issue is formulated as an optimal control problem. In order to improve the squeezing phenomenon of traditional NMPC, a discrete-time nonlinear model predictive controller with terminal cost is then designed, in which the state error of last step is augmented. The cost function of NMPC–WTC consists of two parts: (1) the traditional NMPC cost function responding to tracking errors and controller output, and (2) the augmented terminal cost. The algorithm was implemented on CasADi numerical optimization framework, which is free, open-source and developed for nonlinear optimization. The open-loop and closed-loop simulation results are then presented to demonstrate the improved performance in tracking accuracy and stability compared to traditional model predictive controller

Studies on Equalization Strategy of Battery Management System for Electric Vehicle

Battery management system is one of the key technologies strengthening practical utilization and industrialization of electric vehicles. As an integral part of the battery management system, equalization system played an important role in development of electric vehicles. Based on the analysis of the key technologies of electric vehicle and the development trend of battery management system, a systematic method for bi-directional equalization of lithium ion battery pack is presented in this paper. The basic principle utilizes a Flyback Converter with a multiwinding transformer. Equalization with voltage is employed to balance the cell voltage of battery pack. In order to ensure the accuracy requirements of the cell voltage, a voltage measurement scheme based on analog multiplexers using photoelectric relay was adopted in this unit to detect the voltage of battery one by one. Experimental results show that the proposed battery equalization scheme can not only enhance the uniformity of power battery pack, but also improve the life of the battery as a whole

Nonlinear Model Predictive Control with Terminal Cost for Autonomous Vehicles Trajectory Follow

This paper presents a nonlinear model predictive control with terminal cost (NMPC–WTC) algorithm and its open/closed-loop system analysis and simulation validation for accurate and stable path tracking of autonomous vehicles. The path tracking issue is formulated as an optimal control problem. In order to improve the squeezing phenomenon of traditional NMPC, a discrete-time nonlinear model predictive controller with terminal cost is then designed, in which the state error of last step is augmented. The cost function of NMPC–WTC consists of two parts: (1) the traditional NMPC cost function responding to tracking errors and controller output, and (2) the augmented terminal cost. The algorithm was implemented on CasADi numerical optimization framework, which is free, open-source and developed for nonlinear optimization. The open-loop and closed-loop simulation results are then presented to demonstrate the improved performance in tracking accuracy and stability compared to traditional model predictive controller

Prediction for the Remaining Useful Life of Lithium–Ion Battery Based on RVM-GM with Dynamic Size of Moving Window

Accurate prediction of the remaining useful life of a lithium–ion battery (LiB) is of paramount importance for ensuring its durable operation. To achieve more accurate prediction with limited data, this paper proposes an RVM-GM algorithm based on dynamic window size. The method combines the advantages of the relevance vector machine (RVM) algorithm and grey predictive model (GM). The RVM is applied to provide the relevance vectors of fitting function and output probability prediction, and the GM is used to obtain the trend prediction with limited data information. The algorithm is further verified by the NASA PCoE lithium–ion battery data repository. The experimental prediction results of different batteries data show that the proposed algorithm has less error while applying a dynamic window size compared with a fixed window size, while it has higher prediction accuracy than particle filter algorithm (PF) and convolutional neural network (CNN), which has verified the effectiveness of the proposed algorithm

Prediction for the Remaining Useful Life of Lithium–Ion Battery Based on RVM-GM with Dynamic Size of Moving Window

Accurate prediction of the remaining useful life of a lithium–ion battery (LiB) is of paramount importance for ensuring its durable operation. To achieve more accurate prediction with limited data, this paper proposes an RVM-GM algorithm based on dynamic window size. The method combines the advantages of the relevance vector machine (RVM) algorithm and grey predictive model (GM). The RVM is applied to provide the relevance vectors of fitting function and output probability prediction, and the GM is used to obtain the trend prediction with limited data information. The algorithm is further verified by the NASA PCoE lithium–ion battery data repository. The experimental prediction results of different batteries data show that the proposed algorithm has less error while applying a dynamic window size compared with a fixed window size, while it has higher prediction accuracy than particle filter algorithm (PF) and convolutional neural network (CNN), which has verified the effectiveness of the proposed algorithm

Height Adjustment of Vehicles Based on a Static Equilibrium Position State Observation Algorithm

In this paper, a static state observer algorithm based on the static equilibrium position is proposed, which can realize accurate control of electric vehicle height adjustment with existing road excitation. The existence of road excitation can lead to deflection variation of the electronically controlled air suspension (ECAS). The use of only dynamic deflection as the reference for the electric vehicle height adjustment will produce great errors. Therefore, this paper provides an observation algorithm, which can realize the accurate control of vehicle height. Firstly, the static equilibrium position equation of suspension is derived according to the theory of hydrodynamics and characteristics of pneumatic chamber. Secondly, a vehicle dynamics model with seven degrees of freedom (7-DOF) is established and the kinetic equations are discretized. Then, the unscented Kalman filter (UKF) algorithm is used to obtain the static equilibrium position of vehicle. According to the vehicle static equilibrium position obtained by UKF, the height of the vehicle is adjusted by using a fuzzy controller. The simulation and experimental results show that this proposed algorithm can realize the control of vehicle height with an accuracy of over 96%, which ensures the excellent driving performance of vehicles under different road conditions