This paper proposes an adaptive real-time energy management strategy for a parallel
plug-in hybrid electric vehicle (PHEV). Three efforts have been made. First, a novel
driving pattern recognition method based on statistical analysis has been proposed.
This method classified driving cycles into three driving patterns: low speed cycle,
middle speed cycle, and high speed cycle, and then carried statistical analysis on
these three driving patterns to obtain rules; the types of real-time driving cycles
can be identified according to these rules. Second, particle swarm optimization (PSO)
algorithm is applied to optimize equivalent factor (EF) and then the EF MAPs, indexed
vertically by battery’s State of Charge (SOC) and horizontally by driving distance,
under the above three driving cycles, are obtained. Finally, an adaptive real-time
energy management strategy based on Simplified-ECMS and the novel driving pattern
recognition method has been proposed. Simulation on a test driving cycle is performed.
The simulation results show that the adaptive energy management strategy can decrease
fuel consumption of PHEV by 17.63% under the testing driving cycle, compared to CD-CS-based
strategy. The calculation time of the proposed adaptive strategy is obviously shorter
than the time of ECMS-based strategy and close to the time of CD-CS-based strategy,
which is a real-time control strategy.