## Materials and Methods
### Vehicle Systems and Drive-Cycle Simulations  
The performance of the different concepts was assessed using the commercial software GT-Power from Gamma Technologies(r) (v2016, Gamma Technologies, LLC., Westmont, IL, USA, 2016). The GT-Drive package allows the simulation of the whole vehicle with its subsystem such as gearbox, tires, cooling circuits, etc. In addition, the software also possesses the required devices to perform the hybridization process, resulting in different batteries (NiMh, Li-ion, etc. ), motors, and controller devices. Additionally, the vehicle can be submitted to load and speed profiles, allowing assessment of its performance on different driving cycle conditions. In this way, the mapping results from bench experiments were used as inputs for the simulation. Different models were developed for each of the powertrains, and are presented in Figure 5. The main differences between the models are the increased number of controllers, the absence of a gearbox, and the addition of batteries and motors in the hybrid powertrain system (Figure 5b). The maps obtained from experimental bench tests are used as inputs for the model. These data are specified in the engine object in Figure 5. First, geometric parameters of the engine such as total displacement, idle, engine type, etc., are specified. Following this, the maps that characterize the workings of the engine are defined. The required maps for this step are the break mean effective pressure (BMEP) values as a function of load percentage and engine speed, and the friction mean effective pressure (FMEP) maps related to the BMEP values and the engine speed, see Figure 6. With these data, the IMEP values can be obtained, and the emissions and fuel consumption maps can be converted from indicated to brake-related values. Finally, the data for emissions and fuel consumption are inserted into the model. The advantage of this calculation method is to know, a priori, the values that the parameter will possess based on a determined operating condition (BMEP and engine speed). For intermediary conditions, the experimental maps are interpolated to predict a value. This operating condition is obtained from the solution of the differential equations of vehicular motion. The total required torque to move the vehicle is defined by Equation (1) [44]. \[\begin{array}{rl}\tau_{\text{vehicle}}=&\left[\text{I}_{\text{trans1}}\ +\ \frac{\text{I}_{\text{trans2}}\text{2}}{\text{R}_{\text{t}}^{3  
M\({}_{\rm veh}\), F\({}_{\rm aer}\), F\({}_{\rm rcl}\), and F\({}_{\rm grd}\), are the vehicle mass, aerodynamic forces, rolling resistance forces, and gravity forces respectively. For both powertrains, the same set of motion equations are solved. However, the hybrid system uses additional equations to determine the battery charging, instantaneous state of charge, etc. It is also important to mention that an additional loss term was added to the system for the hybrid powertrain. As the engine remains turned-off for long periods of time, every time it is started, an additional fuel consumption will be observed as a result of the increased heat losses. Therefore, a fuel penalty was attributed to each engine start and was considered during the simulations. Table 5 specifies the vehicle characteristics used in the drive-cycle simulations. The vehicle used to perform the simulations is an Opel Vectra, which is equipped with a four-cylinder engine with the same characteristics of the single-cylinder engine tested on the test bench. The aerodynamic and mechanical characterization of the vehicle allows modeling of the dragging and inertial forces that were described above. The driving cycles selected for study in this work are the worldwide harmonized light vehicles test cycle (WLTC) and the real driving emissions cycle (RDE); chosen because they have a major relevance with regard to the current regulatory stages of the European emissions standards.