## 5 Analysis of Different ECMS
However, it was basically possible to apply another optimization method, which enabled faster computation of the state of charge characteristic (e.g., the combination of DP and PMP [32]. In comparison with the approach based on the Hamilton-Jacobi-Bellmann equation, the control-based approach had the potential to be initialized faster. The other variants (blue and cyan marker) only required the distance of the intended trip in order to define the characteristic for the setpoints of the controller. Adapting this characteristic to the current state of charge when recuperating (blue marker)  
Figure 10: Simulation results for the ECMS, showing average fuel consumptions for all driving cycles and prediction methods. enabled slightly lower fuel consumptions. This, however, depended on how much energy could be recuperated. Since these variants did not require a driving cycle or optimization in advance, it was also possible to apply them to instantaneous trips. Table 1 shows an overview of the considered operating strategies and the corresponding information required for its applications. Furthermore, it contains the mean percentage increase of the fuel consumptions obtained by each operating strategy related to the theoretically minimal fuel consumption obtained with DP. The results show that only minor information about the driving cycle enabled the ECMS to achieve significantly lower fuel consumptions compared with the non-optimization-based CD-CS operation. In addition, the more information about the future driving cycle was available, the lower the fuel consumptions obtained by the ECMS were. Optimization-based predictions led to an improvement in the results, while the best results were obtained when the real driving behavior was used for optimization. The control-based method with a linear characteristic required the same input information as the control-based method with the adaption to the _SoC_ but caused higher fuel consumption. For this reason, it was more beneficial to apply the variant with the adaption, since the additional implementation effort was only slightly increased. The variant with the optimized characteristic further reduced the fuel consumption but required optimization in advance. The advantage in comparison with the method based on the Hamilton-Jacobi-Bellmann equation is that the optimization does not need to be carried out with DP. Another optimization method can be chosen which computes the required characteristic significantly faster (e.g., the method presented in [32]). Since the ECMS was applied for the same model used for the ECMS algorithm itself, some of the resulting fuel consumptions were very close to the theoretical minimum. In practice, however, the powertrain model used for the ECMS algorithm differed from the real powertrain (e.g., due to neglecting the powertrain dynamics) so that, in reality, higher fuel consumptions were to be expected. Nevertheless, it can be assumed that the previously identified tendencies of reducing the fuel consumptions by improving the prediction methods will still remain. The required input information can be obtained by using the intended route information of the navigation system, providing the driver's needs. Furthermore, automatized approaches are recommended for recognizing and logging frequently driven routes.