Real-time optimal EMS of adaptive charge sustenance for fuel cell hybrid buses based on driving characteristics recognition

Abstract

To improve the service lifetime and fuel economy of fuel cell hybrid buses (FCHB), it is extremely important to establish a reasonable energy management strategy (EMS). Therefore, an optimization-based EMS for dual-energy systems is proposed in this paper. Specifically, the proposed strategy is realized via pontryagin’s minimum principle (PMP), which can solve the problems that include inaccurate estimation of the motor output power, the difficulty of SOC adaptive sustenance and real-time application of the algorithm. First, we analyze the driving characteristics of bus routes based on real bus driving data, and solve the optimal co-state values of the historical driving segments (DSs) by dividing driving cycle (DC) and DS. Then, the motor output power and the density-based spatial clustering of applications with noise (DBSCAN) algorithm are employed to predict the co-state value of the next DS. Thereafter, the real-time application of the PMP algorithm is achieved by using predicted co-state and analyzing the SOC adaptive sustenance item during the FCHB runs. The comparisons between real bus experiments and the simulations of five EMSs are conducted to validate the effectiveness. Results demonstrate that our strategy can reduce hydrogen consumption by 6.6% on average versus the rule-based strategy, the fluctuation range of SOC is less than 2%, the mean absolute error (MAE) between the initial and final SOC is 0.10%, and the fuel cell has less lifetime decay. Moreover, our strategy has the same excellent performance compared with the strategy under known driving conditions. In addition, the online computation time per step of the proposed real-time strategy is averaged at 34.8 ms, less than the sampling time interval 1 s. Therefore, the real-time performance of our strategy will contribute to the promotion and application of FCHBs.