Model And Applied Study Of Malfunction Diagnosis Of Public Transportation Electric Automobiles Based On Study Algorithm

Public transportation electric automobiles are controlled by a complicated system that is composed of various kinds of electric equipments. Actually, malfunctions of this system are diversified, caused by many kinds of uncertainty, random, and combination. Piles are theimportant parts of electric automobiles; moreover, due to the reasons caused by techniques and roads, piles are also the main parts of malfunctions. Therefore, it is the key point of malfunction diagnosis. Based on researches and analysis on the present situation and developing trend of domestic or overseas public transportation electric automobiles, this thesis contrastively analyses characteristics of piles malfunction, contents of malfunctions, as well as the diagnostic measures. In order to satisfy the requirements of malfunction diagnosis of piles, the thesis classifies various kinds of malfunctions by a complicated model based on a Decision tree algorithm, and then creates the Decision tree as well as the regular sets.The chief task of malfunction diagnosis is getting correlative information by Internet server, and then diagnosing the piles of the main parts of electric automobiles. The analysis of malfunctions of piles is mostly a kind of statistic analysis of malfunction information in a fixed period. According to the statistic analysis, the situations of piles (SOC, electric current, voltage changed by time and charged or discharged situation), styles of malfunction and reasons of malfunction can be acquired.As for the researches found on a regular diagnosis model, a regular database of diagnosis information is required. Via various kinds of studying and reasoning measures, such as comparative nerve Internet, Petri web, Decision trees, the relevant statements, measures as well as ratiocination mechanism of malfunction diagnosis of electric automobiles can be found. This thesis raises an applied study based on Decision tree algorithm and a model of malfunction diagnosis. Based on the C4.5 algorithm, examples of malfunctions can be classified, and then a Decision tree can be outlined. Many regular sets are sorted out by the Decision tree, and a final set of diagnostic information can be acquired after the entire sets are predigested.The thesis makes researches and analysis on the complicated model of malfunction diagnosis and study algorithm of public transportation electric automobile based Decision tree algorithm. The model can analyses examples of battery malfunctions of electric automobiles and produces satisfactory results, making groundwork for the professional system of malfunction diagnosis.