Adaptive Real-Time Energy Management Strategy for Plug-In Hybrid Electric Vehicle Based on Simplified-ECMS and a Novel Driving Pattern Recognition Method

Table 2

Detailed procedure of the proposed energy management strategy.

Offline	(1) Three typical driving cycles are constructed after driving data gathering, driving features selection, and driving segments clustering.
	(2) Two driving features for driving pattern recognition are determined, and the probability density functions of these two driving features under the above typical driving cycles are obtained by statistical analysis.
	(3) Rules of driving cycle recognition are extracted according to the above two probability density functions.
	(4) Particle swarm optimization (PSO) algorithm is applied to optimize equivalent factor (EF), and the MAPs of this factor under different typical driving cycles, driving distance, and SOC are obtained.
Online	(5) The real-time driving pattern is identified according to the driving cycle recognition rules, and output the type of drive cycle.
	(6) The driving distance is got by navigation system and vehicle’s velocity.
	(7) Based on the aforementioned work, the EF can be obtained by looking up EF Maps through the type of driving cycle, driving distance, and SOC.
	(8) Simplified-ECMS-based strategy is employed to solve the energy distribution optimization problem, and the optimal torque distribution between engine and motor can be obtained.