## 6 Conclusions  
This study presents a methodology for improving the accuracy of a Lo-Fi model of the electric bus powertrain using measurement data from 12 m and 18 m electric bus demonstrations in cities. First, a qualitative comparison is made of the bus's energy requirements between the baseline UC simulations, which used a standard driving profile, and the actual driving profile from the demonstrations. The results show that in Barcelona, the energy requirements of the 12 m buses were 17.5% higher, while those of the 18 m buses were 33% higher, when using the driving profile of the demonstration. For Osnabruck, the energy requirements were 20% lower for the 12 m buses when using the driving profile of the demonstrations, while the 18 m buses had similar energy requirements to the baseline. This is because the Barcelona demonstrations had a higher average velocity compared with the baseline, while the Osnabruck 12 m bus demonstrations had a lower average velocity. The magnitude of the acceleration and deceleration had less effect on the energy requirements of an electric powertrain, since energy expended during accelerations are recovered during decelerations. Only in cases where the driving profile showed many hard decelerations did the energy requirement become higher; this was because during hard decelerations, the bus requires friction brakes to decelerate in addition to the electric motor, leading to less energy recovered via regeneration. Next, the measurement data of the vehicle's speed profile from the demonstration were used as inputs to the simulation framework, and the simulation results of the battery SoC profile were compared to measured battery SoC profiles from the demonstrations. A tuning methodology, based on iSOPT optimization, combined with splitting the simulation into smaller time windows during optimization, was used to minimize the NRMSE between the simulated and measured battery SoC signals and ensure that there is a high degree of correlation between them. The results show that the tuning process based on the window technique applied to the optimization process successfully synchronized the simulation and measurement outputs quicker than the technique presented in [5]. In rare cases, deviations are encountered between the simulated and measured output. Of these, the deviations that describe a situation that is physically impossible, based on the data provided, are ignored. Other deviations result from limitations in the assumptions made during the design of the simulation framework, and those were fixed by correcting the assumptions. However, in two cases, deviations occurred for which no suitable explanation could be determined, and those would require further research to fix. Overall, the optimization achieved more than 90% correlation between the simulated and measured SoC profile. The techniques utilized in this research will be refined further in future research to perform real-time tuning of the platform with the aim of deploying a cloud-based DT of the electric bus that will be able to make predictions in real-time based on the measurement data from the real vehicle. To achieve that goal requires two systems working in synergy: first, it would be necessary to invest in CAN dataloggers with WiFi or 3G/4G capability that will capture the sensor data from the vehicle's CAN network and periodically transmit these measurements to a cloud server. Then, a highspeed simulation model needs to be deployed in the cloud server that will periodically take in these measurements data as inputs and quickly simulate the outputs and tune itself using appropriate tuning techniques to minimize the error between the simulated and measured outputs. The key will be to reduce the simulation time needed during the tuning process (whether via machine learning or optimization), so the model can tune itself in real time. This requires further improvements to the optimization technique and utilizing machine learning using artificial neural networks.