Lithium-ion battery state of health estimation based on improved deep extreme learning machine

Accurate estimation of the state of health (SOH) is an important guarantee for safe and reliable battery operation. In this paper, an online method based on indirect health features (IHF) and sparrow search algorithm fused with deep extreme learning machine (SSA-DELM) of lithium-ion batteries is proposed to estimate SOH. Firstly, the temperature and voltage curves in the battery discharge data are acquired, and the optimal intervals are obtained by ergodic method. Discharge temperature difference at equal time intervals (DTD-ETI) and discharge time interval with equal voltage difference (DTI-EVD) are extracted as IHF. Then, the input weights and hidden layer thresholds of the DELM algorithm are optimized using SSA, and the SSA-DELM model is applied to the estimation of battery's SOH. Finally, the established model is experimentally validated using the battery data, and the results show that the method has high prediction accuracy, strong algorithmic stability and good adaptability.

Optimal harvesting strategies of a stochastic competitive model with S-type distributed time delays and Lévy jumps

The aim of this paper is to investigate the optimal harvesting strategies of a stochastic competitive Lotka–Volterra model with S-type distributed time delays and Lévy jumps by using ergodic method. Firstly, the sufficient conditions for extinction and stable in the time average of each species are established under some suitable assumptions. Secondly, under a technical assumption, the stability in distribution of this model is proved. Then the sufficient and necessary criteria for the existence of optimal harvesting policy are established under the condition that all species are persistent. Moreover, the explicit expression of the optimal harvesting effort and the maximum of sustainable yield are given.

Bifurcation Analysis of Five-Level Cascaded H-Bridge Inverter Using Proportional-Resonant Plus Time-Delayed Feedback

In this paper, a traditional five-level cascaded H-bridge inverter is studied and regulated by a proportional-resonant (PR) controller. In order to extend the range of the gain of PR controller, for the purpose of achieving a fast response, a time-delayed feedback controller (TDFC) is used. Similar to the pulse width modulation (PWM) current-mode single phase H-bridge inverter that exhibits bifurcation and chaos when parameters vary, we demonstrate for the first time that the cascaded H-bridge inverter also shows similar features. From the perspective of a discontinuous map, the cascaded H-bridge inverter generally displays extraordinary complexity. Moreover, a new virtual ergodic method (VEM) is proposed to establish the mathematical model of the whole system, which helps to understand the observed bifurcation phenomena. Simulation results are given to verify the analysis.

The Computerized Simulation Software Development of the Pneumatic DTH

Designing the pneumatic DTH by the method of anology, experience and actual test, the investment is large, the cycle is long, and it is difficult to achieve the optimization. In order to research profoundly the DTH work mechanism and describe piston movement laws in detail, physical model is established according to the actual conditions, finite difference method is adopted, thermodynamics and kinematics are applied. Using object-oriented programming, the pneumatic DTH computerized simulation software is developed whose integrated development environment is VB. The software can calculate acceleration, velocity, displacement, pressure, temperature of the front and back air chamber at any time and the operating performance parameters of the pneumatic DTH. The law of many factors influencing on the pneumatic DTH performance can be found and the optimization of the pneumatic DTH structure parameter can be achieved by the ergodic method. Not only the pneumatic DTH but also the no-dig impactor is designed by computerized simulation.