Failure Analysis of Railway Forklifts and Their Current Reliability Levels

The number of forklifts ranks first among the material handling machinery in railway goods yards. It is important to analyze their failures and to assess their reliability. In this paper, the failure distribution, failure modes and laws of failure distribution of railway forklifts are analyzed based on the field data. The comprehensive assessment of the reliability level of the railway forklifts is also presented according to the reliability index system of forklifts.

Tailpipe emission characterizations of diesel-fueled forklifts under real-world operations using a portable emission measurement system

Non-road equipment is one of the key contributing sources to air pollution.Thus,an accurate development of emission inventory from non-road equipment is imperative for air quality management,especially for equipment with a large population such as diesel-fueled forklifts.The objective of this paper is to characterize duty-cycle based emissions from diesel-fueled forklifts using a portable emission measurement system(PEMS).Three dutycycles were defined in this study,including idling,moving,and working(active duty operation)and used to characterize in-use emissions for diesel-fueled forklifts.A total of twelve diesel-fueled forklifts were selected for real-world emission measurements.Results showed that fuel-based emission factors appear to have smaller variability compared to time-based ones.For example,the time-based emission factors for CO,HC,NO,and PM 2.5 for forklifts were estimated to be 16.6-43.9,5.3-15.1,26.2-49.9,5.5-11.1 g/hr with the fuel-based emission factors being 12.1-20.3,4.1-8.3,19.1-32.4,3.5-6.5 g/kg-fuel,respectively.NO emissions appear to be the biggest concern for emissions control.Furthermore,most of the emissions factors estimated from this study are significantly different from those in both National Guideline for Emission Inventory Development for Non-Road Equipment in China and welldeveloped emission factor models such as NONROAD by US EPA.This implies that localized,preferably fuel-based emission factors should be adjusted based on real-world emission measurements in order to develop a representative emission inventory for non-road equipment.

Accuracy comparison and improvement for state of health estimation of lithium-ion battery based on random partial recharges and feature engineering

State of health(SOH)estimation of e-mobilities operated in real and dynamic conditions is essential and challenging.Most of existing estimations are based on a fixed constant current charging and discharging aging profiles,which overlooked the fact that the charging and discharging profiles are random and not complete in real application.This work investigates the influence of feature engineering on the accuracy of different machine learning(ML)-based SOH estimations acting on different recharging sub-profiles where a realistic battery mission profile is considered.Fifteen features were extracted from the battery partial recharging profiles,considering different factors such as starting voltage values,charge amount,and charging sliding windows.Then,features were selected based on a feature selection pipeline consisting of filtering and supervised ML-based subset selection.Multiple linear regression(MLR),Gaussian process regression(GPR),and support vector regression(SVR)were applied to estimate SOH,and root mean square error(RMSE)was used to evaluate and compare the estimation performance.The results showed that the feature selection pipeline can improve SOH estimation accuracy by 55.05%,2.57%,and 2.82%for MLR,GPR and SVR respectively.It was demonstrated that the estimation based on partial charging profiles with lower starting voltage,large charge,and large sliding window size is more likely to achieve higher accuracy.This work hopes to give some insights into the supervised ML-based feature engineering acting on random partial recharges on SOH estimation performance and tries to fill the gap of effective SOH estimation between theoretical study and real dynamic application.

Design of an Electric Forklift

In order to achieve integration of electrmotion and manual operation for manual forklift, this paper designs an improved manual forklift. We select the appropriate motor according to parameters of original manual forklift, which was analyzed by SolidWorks. We make the detailed design of the speed reducer according to the selected motor＇s rated speed and other parameters, and design the reduction speed ratio, reduction speed form, size of gear drive, meshing parameters, force analysis of gear and strength check. In this paper, we electrify the traditional manual forklift eventually.

Control strategy for energy recovery system in hybrid forklift

After analyzing the working condition of the conventional diesel forklift,an energy recovery system in hybrid forklift is considered and its simulation model is built.Then,the control strategy for the proposed energy recovery system is discussed,which is validated and evaluated by simulation.The simulation results show that the proposed control strategy can achieve balance of the power and keep the state of charge(SOC) of ultra capacitor in a reasonable range,and the fuel consumption can be reduced by about 20.8% compared with the conventional diesel forklift.Finally,the feasibility of the simulation results is experimentally verified based on the lifting energy recovery system.

A Conceptual Framework for the Forklift-to-Grid (F2G) Implementation

This paper focuses on presenting the current research findings on the “Vehicle to Grid” concept and proposes theoretical model of F2G integration in energy management of a warehouse. The study is based on an analysis of collected data and on model calculations of the economic value. Analyses of data, calculations of economic value and profitability of the proposed business model give positive results, which fully confirm the thesis that the integration of renewable energy sources and new modern technologies in the logistic processes can improve energy management.