Control rules extraction and parameters optimization of energy management for bus series-parallel AMT hybrid powertrain

A rule-based energy management strategy, that the control rules are extracted from acknowledged optimal algorithms and its control parameters are optimized offline and corrected online, for a series-parallel hybrid powertrain with an automatic mechanical transmission (AMT) is proposed in this paper to achieve near optimal fuel economy and battery state-of-charge (SOC) balance. Firstly, the dynamic programming (DP) global optimization method is applied to extract driving-mode transition rules and gear shifting rules. Furthermore, an instantaneous equivalent fuel consumption minimizing optimization method (ECMS) is utilized to determinate the engine torque distribution rules during its parallel driving mode. Then selected control parameters of driving-mode switching rules and torque split distribution are optimized based on genetic algorithm (GA) for further fuel consumption improvement. And the adaptive correction of optimized control parameters based on online driving cycle recognition method is discussed also. The simulation results show that this real-time rule-based energy management control strategy associated with the series of optimization approaches comprehensively can achieve a relatively close fuel consumption results to global optimal results and sustain the battery SOC balance after the end of driving cycle without much cycle-depending care.