Adaptive equivalent consumption minimization strategy based on road grade estimation for a plug-in hybrid electric truck

Road grade plays an important role in deciding power repartition and improving energy management performances. In this paper, instead of predictive energy management strategies where terrain information is obtained from GIS maps, an adaptive equivalent consumption minimization strategy (A-ECMS) considering current road grade information is proposed, aiming to design an instantaneous feedback supervisory controller based on the estimation of current road grade without using external devices. To achieve real-time control for a plug-in hybrid electric truck (PHET), the bounds of the optimal equivalent factor (EF) are analyzed considering comprehensive performances, then the sensitivity of EF is evaluated towards road grade. According to the effect of different road grade scenarios on control performances, the real-time EF adaptation is divided into two conditions, i.e. SOCbased adaptation and Estimation-based adaption. The proposed A-ECMS can achieve good performances on both fuel economy improvement and emission reduction, which approximates the results obtained from the DP algorithm, and the high computing load can be avoided for real-time implementation.

Automated summary

In this paper, an adaptive equivalent consumption minimization strategy (A-ECMS) is proposed to achieve real-time control for a plug-in hybrid electric truck (PHET). The strategy considers the current road grade information and designs an instantaneous feedback supervisory controller without using external devices. The performance of the A-ECMS is analyzed and evaluated based on the optimal equivalent factor and its sensitivity towards road grade. The proposed strategy shows good performances in fuel economy improvement and emission reduction, approaching the results of the DP algorithm while avoiding high computing load for real-time implementation.