## 5 Fuel-Consumption Assessment Results
### Fuel Consumption and Emissions Reduction  
The same two drivers (D1 and D11) shown in Figure 11 were selected in order to indicate the potential reduction on emissions that this advice system can achieve. In this 300 s highway driving stretch, speeds for both drivers are kept above 79 km/h (79.1 km/h and 85.4 km/h, respectively). In these conditions, the DS identification system classifies D1 mostly into the Medium consumption class, while D11 is classified into the Very low consumption class. Additionally, the GT-Suite simulation data show that the mean fuel consumption measurements in that stretch for D1 and D11 are 4.46 L/100 km and 2.61 L/100 km, respectively. Assuming that the average composition for diesel fuel corresponds to the formula C\({}_{12}\)H\({}_{23}\), with a density of 0.835 g/L [59], the stoichiometric combustion of this fuel type follows the equation  
\[4\mathrm{C}_{12}\mathrm{H}_{23}+7\mathrm{I}\mathrm{O}_{2}\mathrm{\to 48} \mathrm{CO}_{2}+46\mathrm{H}_{2}\mathrm{O}. \tag{7}\]  
With the chemical reaction of Equation (7), the average CO\({}_{2}\) generation rate can be calculated, with the CO\({}_{2}\) emissions for D1 and D11 being 128.4 g/km and 75.2 g/km, respectively. That is to say, D11's CO\({}_{2}\) emissions are 41.4% lower than those of D1 for similar road stretches at similar speeds. With these results, we can assert that, if the recommendations of Tables 5 and 8 were provided to D1, with the aim of being classified into the Very low cluster, then a noticeable reduction in fuel consumption and emission rates could happen.  
In sum, by using SOM algorithms, the clusters of Figures 9 and 10 were discovered and fuel-consumption-associated DS features were extracted. Those clusters and features, jointly with the analysis of the cluster distribution ratio for each driver of Tables 3 and 7, allowed for the identification of complex behaviors. Finally, several actions were described to modify individual DSs with the aim of improving fuel economy while taking the clustering as well as the distributions and the features into account, consequently encouraging eco-driving.  
task is implemented in must be capable of performing high-speed data computing, while providing high throughput data outputs. For those reasons, a hybrid HW/SW architecture was developed and implemented on the Xilinx XC7Z045-2 FFG900 Programmable PSoC [60] using the Xilinx ZC706 development board [61].  
Figure 13 depicts a block diagram of the proposed solution. PSoCs are devices that combine the high performance of FPGAs with the flexibility of embedded microprocessor-based systems that are both connected with each other by means of an internal AXI4 interface. Thus, while the FPGA (HW partition) performs highly parallelizable binary operations, the microprocessor (SW partition) is specially dedicated to executing sequential tasks, being enabled to interchange information through the AXI4 bus.  
The entire HW partition of the system, consisting of an SOM accelerator, was deployed in the FPGA of the PSoC using VHDL language and the Xilinx Vivado 2018.1 design suite [50]. On the other hand, the remainder of the proposed system functionalities were programmed at the microprocessor (SW partition depicted in Figure 13) by developing a bare-metal C application that can acquire data from the buses of the vehicle, compute the windows of the ERPM, GP, PGP, and pos XACC features, share them with the FPGA, retrieve the SOM accelerator results, and provide advice to drivers.