Electric Vehicles Online Monitoring System Design Based on the Hybrid Reasoning

Abstract:

This paper designs a set of electric vehicles online monitoring system with SOA framework, which includes acquisition layer, network layer, support platform, knowledge base layer and presentation layer, and knowledge layer includes a knowledge base management system, reasoning system and man-machine interface system. Also, this paper constructs a knowledge expert system with hybrid rule-based reasoning method of CBR and RBR, finally, electric automobile remote intelligent fault diagnosis is illustrated with the expert systems.

• Periodical: Applied Mechanics and Materials (Volumes 278-280)
• Pages: 878-883
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.278-280.878
• Online since: January 2013
• Authors: Wei Guo Zhang, Yi Chu, Ming Yu Zhao, Fu Bin Hong
• Keywords: Electric Vehicle (EV), Expert System, Hybrid Reasoning, Online Monitoring, SOA Framework

References

[5] as is shown in fig. 4, read the database for each battery status data in turn, determined whether there is a man-made fault, and subject to exclusion. Then, based on the rule reasoning, credibility of battery symptoms was calculated, state of battery symptoms was assessed, the DOF and DOH of battery failure were caculated, and the type of fault were shown. Meanwhile, the type of fault was re-learned, its credibility were determined, and it was added to the rule base, in the end, judgment rule base standard were sounded. Fig. 4 Battery online monitoring Conclusion Online monitoring system based on SOA electric vehicles comprehensively settled the system change difficulties which is due to the large amount of information as well as changes in demand for information, so systems was easy to maintain. Knowledge layer of the system introduced a hybrid reasoning mechanism, which combined rules of reasoning and case-based reasoning. On this basis, this paper designed expert system with self-learning capability which had both the expert knowledge base and case base. The results show that the hybrid reasoning mechanism designed in this paper is more effective, and can provide a strong service operator systems for electric vehicles. Acknowledgements This work was financially supported by the charging and battery swaping technology research and demonstration for Electric passenger car (science and technology projects of State Grid Corporation 2012), electric vehicle intelligent charge, discharge, and storage integrated power system and engineering demonstration (863 program) (2011AA05A108)， and two-way interactive terminals of Electric vehicles with the grid and intelligent charge and discharge control system (National Energy Board, intelligent charge and discharge key equipment and application demonstration of electric vehicles, ) (NY20110703-1). Referrences.

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