## 2 Materials and Methods
### EF Predictor  
The EF predictor uses a regression ANN to simultaneously identify the drive cycle type and estimate \(s\) based on future state trajectory predictions obtained using look-ahead data. The expected output of this ANN is the optimal EF for the upcoming driving segment, and Table 1 tabulates the features extracted from the data, which includes a selection used by the authors in [22] and additional features related to the SOC variation. These features are used as the input vector for the ANN. The rationale behind using an ANN to estimate \(s\) originates from the concept that a well-tuned EF results in near-optimal behavior [23; 24; 25]. In its traditional formulation, ECMS relies on full route information which is impossible to know in the real world. However, using the look-ahead data available at the beginning of the trip, the full route speed limits can be predicted. Since the proposed approach uses DP to obtain the optimal speed, this optimal speed could be used to compute the optimal EF. The idea is that if \(s\) can be determined for every \(N_{h}\) such that the torque split obtained from ECMS matches that determined by DP, the fuel consumption should closely match the optimal result, since each recalculation is influenced by the full route solution. By doing so, A-ECMS can now be adapted based on look-ahead information as opposed to reacting to an SOC difference or relying solely on past information. The ANN training data is generated using the optimal speed and SOC trajectories determined using DP over many trips. The speed limits and traffic light positions for the trips were generated using typical drive cycles that captured common driving types, such as highway, urban, and mixed driving. They are based off of five standard drive cycles with some modifications, namely the US06, HWFET, UDDS, NYCC, and LA92 cycles [26]. This ensures that the ANN training data is representative of realistic driving. Each drive cycle was first converted from a speed versus time plot to speed versus distance. The speed limit was approximated by matching the drive cycle with distance coordinates. Traffic lights were placed at locations where the drive cycle slows down to zero or closely approaches zero. Figure 4 shows an example of the speed limits constructed from drive cycles. \begin{table}
\begin{tabular}{c c} \hline \hline
**List of Features** & \\ \hline Trip distance & Maximum, minimum and mean SOC \\ Maximum, minimum and mean vehicle speed & Standard deviation SOC \\ Maximum, minimum and mean acceleration & Maximum, minimum and mean SOC change \\ Speed standard deviation & Fuel consumed \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A List of 16 selected features for EF estimation. The speed profile not only depends on the speed limits and traffic lights but also on surrounding traffic. For each of the drive cycles, five iterations are created where the speed limit is randomized within a certain range to model changes in average traffic flow speed. Traffic light positions remain fixed, but the SPaT is random for each iteration to simulate real-world traffic. The result is a large number of trips defined by speed limits as a function of distance and SPaT. These trips are further divided into road segments of length \(N_{h}\), which are representative of what the vehicle will encounter on the road. For each segment of each trip, DP provides optimal speed and SOC trajectories. The result is a large data set of optimal speed and SOC trajectories over a horizon of length \(N_{h}\). The actual training data set which is fed into the ANN consists of the calculated features shown in Table 1 for each speed and SOC trajectory found by DP. To obtain the optimal \(s\) for each training data entry, a traditional ECMS with a constant \(s\) is used. The optimal \(s\) is found using the shooting method, where \(s\) is varied until the SOC trajectory terminates at the desired final SOC, \(SOC_{f}\), starting from an initial SOC, \(SOC_{i}\).