Estimation of direct current resistance online for new energy vehicles

The state of resistance growth (SOR) for batteries on hybrid electric vehicles (HEV) is difficult to be estimated precisely online, due to the current and especially the temperature are uncontrollable, which make the battery health state always is hard to be known. Accurate estimation of resistance online and gaining the resistance of battery at the begin of life (BOL) are the two keys to address the problem. As direct current resistance (R_dc) is a world-wide used evaluation parameter for battery through off-line test, algorithm for online R_dc calculating is proposed in this paper, adopting a fractional order model and recursive least square identification with the virtual current design. Comparing with the common resistance estimation, R_dc estimated here is comparable with the offline tested, with an error less than 5%. With the R_dc provided in the specification for batteries at BOL, SOR with small error (1.3%) and high universality is obtained. It can accurately diagnose batteries health for HEV, and give early alarm for cell with abnormal or large resistance growth. It addresses the urgent evaluation problem especially for the old vehicles operated for years since this algorithm doesn't rely on the historical estimated resistance data.