## 3 Results
### Analysis and Synthesis
#### 3.1.2 ECMS EMSs  
The equivalent consumption minimization concept is based on equating the consumption of electrical energy to the corresponding fuel consumption with an equivalence factor. The control parameters of ECMS are sensitive to the driving cycles. With the prior knowledge of a certain driving cycle, the control parameters of ECMS can be tuned to obtain the near-global optimal solutions. However, for other driving cycles, the control effects are deteriorated. Therefore, the current research into this kind of EMS is focused on correcting the control parameters online according to the vehicle states, and the resulting EMSs are usually known as adaptive ECMS (A-ECMS). In [20], the ECMS was modified based on the following ideas: it is common that the SOC set for the power-maintaining hybrid vehicle ranges between 0.5 and 0.6, but there is still room for the continued use of electric energy; from statistical data, it is observed that the time that the vehicle spends at high accelerationis relatively short; hence, it will not cause the battery SOC to significantly decrease. Based on these premises, the ECMS was modified using an optimization algorithm to allow cautious use of the battery when its SOC is lower than the target. To verify its performance, the improved ECMS, the conventional ECMS and a commonly used RB EMS (specifically the CDCS strategy) were compared. The results confirmed the effectiveness of the proposed control strategy but it was commented that, under other working conditions, the fuel-saving results would change. [PERSON] et al. [24] proposed an adaptive law that updated the equivalence factor of the ECMS according to the vehicle state. The authors focused on the ease of implementing the adaptive law in real time and conducted hardware in the loop simulations in dSPACE to validate their EMS. Their A-ECMS was compared with a common strategy used in mass production vehicles. The results showed that, although the proposed EMS showed good performance, its results compared to the RB strategy were very similar for fuel consumption, although improvements were observed in the battery's ability to sustain the SOC. The feasibility of adopting a trip planning assisted EMS was studied in [26]. Their authors stated that despite recent advances, the automotive industry had not been able to adopt it since there were still many open questions about its feasibility that needed to be answered prior to production. They proposed an ECMS-based EMS to verify the real-time implementability and to analyze its sensitivity to real-world driving conditions. Hardware in the loop (HIL) experiments were carried out to perform the sensitivity analysis. The results were compared to the fuel consumption of a baseline EMS (a common RB strategy). Although traffic mispredictions deteriorated the fuel economy of the proposed EMS, its results were remarkably better than the baseline EMS even in the worst-case scenarios. Another approach for ECMS is proposed in [25] that considers not only energy consumption and power performance but emission reduction. A weighting coefficient of emission performance was proposed. The higher was this coefficient, the more attention was paid to the optimization of emission indicators. The comparison of the proposed EMS with different weighting coefficients against DP validated the accuracy of the proposed control algorithm. In [22], it was proposed a time-windowed ECMS (which compared cost functions integrated during a fixed time interval) that relied on [PERSON]'s minimum principle (PMP), with some modifications. The proposed EMS was divided into three parts: the driving status determination, the powertrain status management and the time-windowed ECMS. The first determined the vehicle state flags and the driver demand torque; the second determined appropriate driving modes based on the vehicle state flags and driver demand torque; and finally, the ECMS determined the optimal power-split ratio and the optimal mode flags solving the real-time optimal problem of minimizing fuel consumption.