## 3 Results
### Analysis and Synthesis
#### 3.1.2 ECMS EMSs
The proposed control algorithm was evaluated by comparing its results with the results of previous research (two previous algorithms based on the same approach of ECMS and from which the proposed was derived), showing better fuel economy. However, it lacked a comparison against a DP or a common RB EMS to support the presented results. [PERSON] et al. [23] commented on the problems of the ECMS-based EMS related to determining the equivalence factor (e.g., using averaged efficiencies, using prior trip information, etc.) in the introduction of their work. The proposed EMS fitted for real-time control without using any prior trip information. The equivalence factor of the optimization model was defined as the efficiency of gasoline energy transferring to battery energy. Another two coefficients depending on the battery SOC were introduced to control the battery SOC to change in the predefined interval. The simulation for standard driving cycles demonstrated that the proposed EMS could maintain the battery SOC in the expected interval. At the same time, it showed a better fuel economy performance than a commonly used RB EMS and previous EMS based on A-ECMS. The work presented in [21] also proposed an A-ECMS based on the idea that the propulsion and recuperation efficiencies cannot be considered constant. In their EMS, the controller was composed by a higher-level controller and a lower-level controller. The higher-level controller utilized model predictive control (MPC) for the prediction of the optimal velocity profile over a finite time horizon, with periodically updated efficiencies from the lower-level controller. The adaptive ECMS (A-ECMS)-based lower-level controller then made use of the optimal velocity profile for the energy management control of the HEV. To evaluate the validity of the proposed EMS, its fuel economy was compared with a baseline method based on a common RBEMS. In [28], it was proposed a strategy that included offline and online parts: the offline part used historical traffic data and an optimization algorithm to optimize the thresholds of mode switching and the equivalence factor of the ECMS; in the online part, a supervisory control generated mode switching signals and an adaptive equivalence factor for instantaneous optimization for the ECMS. The effectiveness of the proposed strategy was demonstrated by simulation results compared with a practical strategy (a common RB control) and with the DP, regarded as the benchmark. The results indicated that the proposed strategy was much nearer to that of the DP than that of the RB. Other work that considered an A-ECMS for multi-objective purposes (minimizing gasoline consumption, minimizing electricity consumption and prolonging battery life) was presented in [27]. In this case, the two objectives of energy consumption and battery loss were balanced in the cost function by a weighting factor that changed in real time. The real-time performance of the proposed EMS was guaranteed by the tuning of this weighting factor by a recurrent neural network (RNN). This RNN was trained offline by DP utilizing historical traffic data. To verify the performance of the proposed EMS, it was compared against the DP results and the conventional CDCS strategy. This showed that the proposed EMS possessed the desired effectiveness and adaptability to various driving cycles and met the multiple control objectives.