New-Energy Vehicle Transmission System Optimization and Design

This paper discusses the optimization and design of the new-energy vehicle transmission systems.Traditional automatic transmission systems suffer from fuel consumption and emissions issues,as well as limitations in the efficiency of mechanical transmission systems.In order to solve these problems,different types of transmission systems are used in new-energy vehicles,including battery electric vehicles(BEV),hybrid electric vehicles(HEV),and fuel cell electric vehicles(FCEV).The key factors for optimizing the transmission systems of new-energy vehicles include battery technology and energy management system,electric motor and electronic control system,energy recovery and reuse technology,and lightweight and aerodynamic design of the vehicle.This paper also introduces methods and tools for designing new-energy vehicle transmission systems,including simulation tools,optimization algorithms and data analysis,as well as experimental verification and testing methods.Besides,new-energy vehicle transmission system designs are proposed,and future challenges and development directions are discussed through case studies.

Research on the Development of Global New-Energy Vehicle Industry Under the Goal of Carbon Neutrality

As a pioneer in enengy conservation and environmental protection,new energy vehicles also play an important role in reducing carbon dioxide emissions.Carbon neutrality will become a long-term measure to reduce greenbouse gas emissions in the future,and the new-energy vehicle industry should develop a path that is more in line with the goal of carbon neutrality.

Analysis of Potential Causes of Safety Failure of New Energy Vehicle Power Batteries

The aim of this paper is to analyze the potential reasons for the safety failure of batteries for new-energy vehicles.Firstly,the importance and popularization of new energy batteries are introduced,and the importance of safety failure issues is drawn out.Then,the composition and working principle of the battery is explained in detail,which provides the basis for the subsequent analysis.Then,the potential impacts of factors such as overcharge and over-discharge,high and low temperature environments,internal faults,and external shocks and vibrations on the safety of the batteries are analyzed.Finally,some common safety measures and solutions are proposed to improve the safety of new energy batteries,in hopes of improving the safety of batteries for new-energy vehicle.

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Research on the big data of traditional taxi and online car-hailing:A systematic review

The purpose of this paper is to provide a summary of a quick overview of the latest developments and unprecedented opportunities for scholars who want to set foot in the field of traditional taxi and online car-hailing(TTOC).From the perspectives of peoples(e.g.,passenger,driver,and policymaker),vehicle,road,and environment,this paper describes the current research status of TTOC's big data in six hot topics,including the ridership factor,spatio-temporal distribution and travel behavior,cruising strategy and passenger service market partition,route planning,transportation emission and new-energy,and TTOC's data extensional application.These topics were included in five mainstreams as follows:(1)abundant studies often focus only on determinant analysis on given transportation(taxi,transit,online car-hailing);the exploration of ridership patterns for a multimodal transportation mode is rare;furthermore,multiple aspects of factors were not considered synchronously in a wide time span;(2)travel behavior research mainly concentrates on the commuting trips and distribution patterns of various travel indices(e.g.,distance,displacement,time);(3)the taxi driver-searching strategy can be divided into autopsychic cruising and system dispatching;(4)the spatio-temporal distribution character of TTOC's fuel consumption(FC)and greenhouse gas(GHG)emissions has become a hotspot recently,and there has been a recommendation for electric taxi(ET)in urban cities to decrease transportation congestion is proposed;and(5)based on TTOC and point of interest(POI)multi-source data,many machine learning algorithms were used to predict travel condition indices,land use,and travel behavior.Then,the main bottlenecks and research directions that can be explored in the future are discussed.We hope this result can provide an overview of current fundamental aspects of TTOC's utilization in the urban area.