Wavelet transform based energy management strategies for plug-in hybrid electric vehicles considering temperature uncertainty

In order to avoid the sharps and transients of power demand and extend the battery lifetime, three energy management strategies via wavelet transform (WT) considering temperature uncertainty for hybrid energy storage system (HESS) in the plug-in hybrid electric vehicle are proposed in this paper. The HESS consisting of batteries, ultracapacitors, along with two associated DC/DC converters is discussed and modeled in details. In addition, to further investigate the influence of temperature uncertainty, a random temperature variation and three-dimensional response surfaces are employed for modeling. To systematically compare the performances of WT-based (WTB) strategy, WT-and-rule-based (WTRB) strategy and WT-and fuzzy-logic-control-based (WTFLCB) strategy, an optimization scheme is presented directly. The simulation results demonstrate that the WTFLCB strategy shows better performance under temperature uncertainty. Moreover, a hardware in the loop experiment platform is set up to further verify the feasibility of the WTRB strategy for actual application. It is found that the battery SoC and ultracapacitor SoC estimation errors are less than 0.77% and 3.87%, respectively.