###### Abstract  
This study presents the optimization and tuning of a simulation framework to improve its simulation accuracy while evaluating the energy utilization of electric buses under various mission scenarios. The simulation framework was developed using the low fidelity (Lo-Fi) model of the forward-facing electric bus (e-bus) powertrain to achieve the fast simulation speeds necessary for real-time fleet simulations. The measurement data required to verify the proper tuning of the simulation framework is provided by the bus original equipment manufacturers (OEMs) and taken from the various demonstrations of 12 m and 18 m buses in the cities of Barcelona, Gothenburg, and Osnabruck. We investigate the different methodologies applied for the tuning process, including empirical and optimization. In the empirical methodology, the standard driving cycles that have been used in previous studies to simulate various use case (UC) scenarios are replaced with actual driving cycles derived from measurement data from buses traversing their respective routes. The key outputs, including the energy requirements, total cost of ownership (TCO), and impact on the grid are statistically compared. In the optimization scenario, the assumptions for the various vehicle and mission parameters are tuned to increase the correlation between the simulation and measurement outputs (the battery SoC profile), for the given scenario input (the velocity profile). Improved simple optimization (iSOPT) was used to provide a superfast optimization process to tune the passenger load in the bus, cabin setpoint temperature, battery's age as relative capacity degradation (RCD), SoC cutoff point between constant current (CC) and constant voltage charging (CV), charge decay factor used in CV charging, charging power, and cutoff in initial velocity during braking for which regenerative braking is activated. e-bus powertrain; tuning and optimization; iSOPT; digital twins; internet-of-things +
Footnote †: journal: _Applied_  
behave as its physical counterpart to corresponding input stimuli [4]. A fully trained and tuned DT offers several advantages, including quicker iterative testing of the virtual model, using multiple copies for evaluation of different aspects of the vehicle at a fraction of the cost and time. For a city bus operator (CBO) and electricity distribution system operator (DSO), the virtual models can substitute for their real counterparts during fleet use case (UC) simulations to determine the real-world feasibility of electrification of the bus routes. In [5], a simulation framework developed for the European Commission's Horizon 2020 project ASSURED was used to investigate the UCs of single buses and fleets of buses in various cities to determine their energy expenditure and TCO. The simulation framework was also used to study the reduction in energy utilization possible by applying different energy saving (ECO) strategies, and various optimization scenarios were investigated to determine the charging infrastructure that will minimize the fleet TCO (for the CBO) and load on the grid due to fleet charging (for the DSO). However, due to the lack of measurement data during the research conducted using various assumptions, including the use of a standard (hybrid SORT) driving cycle as the input scenario, constant average vehicle speed profile and a randomized passenger profile throughout the simulation period of one day, these assumptions naturally were not consistent with real-world conditions, including traffic situations on the road, and did not differentiate between peak and non-peak hours for passenger commutes. The hybrid SORT driving cycle can only be applied repetitively, synchronized to a constant average vehicle speed, throughout the simulation implying a constant traffic situation throughout the day. Furthermore, although the passenger profile was randomized, the output of the randomizer tended towards a full bus with time?, resulting in energy requirements that were aggressive.