A New Method of Insulation Detection on Electric Vehicles Based on a Variable Forgetting Factor Recursive Least Squares Algorithm

• Published in: IEEE access Vol. 9; pp. 73590 - 73607
• Main Authors: Chen, Zhenbin, Cui, Weiya, Cui, Xiangyu, Qiao, Huimin, Lu, Hao, Qiu, Na
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Capacitance; Circuits; Electric vehicle (EV); Electric vehicles; Electrical resistance measurement; embedded micro-control unit; High voltages; HVDC transmission; Insulation; insulation detection; Least squares; Lithium-ion batteries; Mathematical models; Parameter identification; Rechargeable batteries; Reflected waves; Resistance; Resistors; Shutdowns; Signal injection; Taylor series; variable forgetting factor recursive least squares (VFFRLS); Voltage measurement
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3079332

The lithium-ion batteries of an electric vehicle belong to a high-voltage direct-current system. The high-voltage insulation performance of electric vehicles is very important for their safe operation. To solve the problems of slow response and the poor estimation accuracy of the insulation resistance under complex vehicle working conditions, a real-time insulation resistance detection method based on the variable forgetting factor least squares algorithm is proposed in this paper. Based on the low-frequency signal injection method and considering the influence of the Y capacitor, the corresponding circuit model and the mathematical model of the reflected wave voltage are established, and the mathematical model is linearized by a first-order Taylor expansion. By analyzing the influence of the forgetting factor on model parameter identification and setting appropriate shutdown criteria, the least squares algorithm with a variable forgetting factor is designed to quickly and accurately estimate the insulation resistance and Y capacitance. The experimental test results show that the proposed method can quickly track the changes in the insulation resistance and Y capacitance under the condition of noise interference and that the root mean square error of the estimation resistor is within 0.012.