Measurement and Evaluation of the Business Environment in Large and Medium-Sized Cities in China

The comprehensive development level of the business environment reflects the quality of a region’s economic development,and a good business environment will give a city a strong vitality.This paper uses the entropy method to measure and evaluate the business environment of 106 large and medium-sized cities in China from 2017 to 2021.The results show that:From the spatial point of view,the business environment index of China’s cities shows a gradually decreasing pattern from east to west and from south to north.The balance of the business environment of large and medium-sized cities in China is divided into four levels according to the standard deviation of the ranking of each subindex.The greater the standard deviation,the more unbalanced the overall development of the business environment in the region.Finally,this paper put forward countermeasures and suggestions to further optimize the business environment.

Multi-time scale identification of key kinetic processes for lithium-ion batteries considering variable characteristic frequency

The electrification of vehicles puts forward higher requirements for the power management efficiency of integrated battery management systems as the primary or sole energy supply.In this paper,an efficient adaptive multi-time scale identification strategy is proposed to achieve high-fidelity modeling of complex kinetic processes inside the battery.More specifically,a second-order equivalent circuit model network considering variable characteristic frequency is constructed based on the high-frequency,medium-high-frequency,and low-frequency characteristics of the key kinetic processes.Then,two coupled sub-filters are developed based on forgetting factor recursive least squares and extended Kalman filtering methods and decoupled by the corresponding time-scale information.The coupled iterative calculation of the two sub-filter modules at different time scales is realized by the voltage response of the kinetic diffusion process.In addition,the driver of the low-frequency subalgorithm with the state of charge variation amount as the kernel is designed to realize the adaptive identification of the kinetic diffusion process parameters.Finally,the concept of dynamical parameter entropy is introduced and advocated to verify the physical meaning of the kinetic parameters.The experimental results under three operating conditions show that the mean absolute error and root-mean-square error metrics of the proposed strategy for voltage tracking can be limited to 13 and 16 mV,respectively.Additionally,from the entropy calculation results,the proposed method can reduce the dispersion of parameter identification results by a maximum of 40.72%and 70.05%,respectively,compared with the traditional fixed characteristic frequency algorithms.The proposed method paves the way for the subsequent development of adaptive state estimators and efficient embedded applications.