## 3 Results
### Analysis and Synthesis
#### 3.1.4 OB EMSs  
The objective of optimization-based (OB) EMSs is to find the optimal control sequence (i.e., reference power demand) that minimizes a cost function while meeting the dynamic state constraints, such as the global state constraints (e.g., battery SOC) and local state constraints (e.g., power limit, speed limit and torque limit) [10]. The cost functions can be different representations, such as the fuel consumption, the hybridization costs, the payload weight of the vehicle, the exhaust gas emissions, etc. This work is focused on online strategies, i.e., those that can be implemented in real-time applications. Some of the strategies in this category, apart from ECMS and MPC, are robust control, extremum seeking, decoupling control, pseudospectral optimal control and sliding mode control, among others. For example, an adaptive EMS with velocity forecast formulated into a mixed-integer optimization problem was presented in [43]. An approximate dynamic programming was proposed for optimizing the power split and gear shift and a multi-stage neural network (MSNN) for velocity forecast. The power split control and the discrete gear shift control were jointly optimized without prior knowledge of the driving cycle and system model. The proposed EMS was compared with a common RB EMS and with a DP that was regarded as the benchmark. Simulation results indicated close fuel economy with respect to DP and effective online application. The same authors, [PERSON] and [PERSON] [44], presented a different approach based on an adaptive cruise control method based on action-dependent heuristic dynamic programming (ADHDP) that generated the gear shift command and the power split between the engine and the electric motor. This control was realized without prior knowledge of the driving cycle, facilitating online implementation. The near-optimality and robustness of the proposed EMS was validated by a comparison with DP results. Machine learning logic employed as online controller for multimode HEVs was proposed in [38]. The machine learning logic was employed to select the current operating mode for the HEV powertrain and thus the specific set of clutches that needs to be engaged. The advantage of the presented methodology was the flexibility to be applied to different HEV powertrain configurations. Compared to the optimal DP benchmark,fuel economy and ease of online optimal controllability were simultaneously achieved. In [40], it was highlighted the importance of dynamic transmission efficiency (DTE) due to its influence on the overall powertrain efficiency. Therefore, when considering the EMSs of PHEVs, the best way to optimize the comprehensive efficiency was to take the DTE of the transmission system into consideration. The proposed EMS method considered the effect of the ICE, electric motor and planetary coupling transmission system (PCTS) for a comprehensive dynamic efficiency. An improved dynamic efficiency optimization strategy (Improved-DEMS) was proposed as EMS that considered the trade-off between energy consumption and the fatigue life of the planetary coupling transmission system. The EMS was verified compared to an RB strategy. The results demonstrated that the work efficiency of the ICE, electric motor and PCTS were the highest for the Improved-DEMS and experimental results using the HIL simulation test supported real-time validation. In [37], a methodology that involved two online iteration loops to simultaneously update the predictive model of velocity trajectories and optimize the control sequence was proposed. It was defined as dual-loop online intelligent programming (DOIP). Its performance was compared with DP. Moreover, a driver-in-the-loop (DiL) experiment was carried out to demonstrate its real-time implementation. The effect of considering stochastic vehicle mass for optimization of EMSs of PHEVs was studied in [41]. This factor has a strong relationship with the required power and the required power will in turn affect the optimal SOC prediction.