A driving pattern recognition-based energy management for plug-in hybrid electric bus to counter the noise of stochastic vehicle mass

Abstract Because the strong coupling relationship between energy management and required power, the Pontryagin’s Minimum Principle (PMP)-based energy management should consider the noise of stochastic vehicle mass for plug-in hybrid electric bus (PHEB). However, if the vehicle mass is evaluated on-line, the control complexity will be greatly increased. This paper proposes a driving pattern recognition method to address the problem. The method is constituted by a look-up table and the K-nearest neighbor algorithm (KNN). The look-up table is used to recognize the robust design value (the inverse value of the robust co-state), where the average velocity at every bus station is taken as input, and the robust design value is taken as output. More importantly, the robust design value is found off-line by Design For Six Sigma (DFSS) method, and can counter the noise of stochastic vehicle mass. Because of this, the noise of the stochastic vehicle mass can be neglected in adaptive energy management control. The Monte Carlo Simulation (MCS) and simulation test results show that the proposed method is reasonable, robust and applicable; the fuel economy can be averagely improved by 34.36%, compared to a rule-based energy management.