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This is the second paper in a series of two that describe our research in intelligent energy management in a hybrid electric vehicle (HEV). In the first paper we presented a machine learning framework, ML_EMO_HEV, developed for learning the knowledge about energy optimization in an HEV. The framework consists of machine learning algorithms for predicting driving environments and for generating optimal power split of the HEV system for a given driving environment.In this second paper, we present three online intelligent energy controllers, IEC_HEV_SISE, IEC_HEV_MISE, and IEC_HEV_MIME. All three online intelligent energy controllers were trained within the machine learning framework, ML_EMO_HEV were trained to generate the best combination of engine power and battery power in real-time such that the total fuel consumption over whole driving cycle is minimized while still meeting the driver's demand and the system constraints including engine, motor, battery, and generator operation limits. The three online controllers were integrated into the Ford Escape Hybrid vehicle model for online performance evaluation. Based on their performances on 10 test drive cycles provided by the PSAT (Powertrain Systems Analysis Toolkit) library, we can conclude that the roadway type and traffic congestion level specific machine learning of optimal energy management is effective for in-vehicle energy control. The best controller, IEC_HEV_MISE, trained with the optimal power split generated by the DP optimization algorithm with multiple initial SOC points and single ending point can provide fuel saving range from 5% through 19%.In conclusion, together these two papers cover the innovative technologies for modeling power flow, mathematical background of optimization in energy management, and machine learning algorithms for generating intelligent energy controllers for quasioptimal energy flow in a power split HEV.Index Terms-Energy optimization, fuel economy, hybrid electric vehicle (HEV) power management, machine learning.