In the transportation industry, the trend is towards electric vehicles. It is facilitated by intensive government subsidies and other incentives. The aim is to reduce the dependence on fossil fuel-based transportation. This has accelerated the proliferation of electric vehicles on roads, especially light-duty vehicles such as electric cars. The electric car industry has fully matured, and now electric cars have cost and quality competitiveness vis-à-vis vehicles with internal combustion engines. However, this does not extend towards heavy-duty electric vehicles such as electric buses and electric trucks. The same issues that plagued electric cars now hamper the electric bus; namely, the electric driving range. At present time, the energy storage in electric buses needs to have sufficient capacity to allow the bus to make one return trip in each bus route for traffic scenarios. Furthermore, the charging infrastructure should be able to deliver this energy content to the battery in a single charging duration, within a limited timeframe, so as not to negatively affect the bus schedule. This is because, at present, the charging infrastructure for heavy-duty vehicles is not widespread; thus, there are limited locations where the buses can charge – most likely at the ends of routes or at the depot. The bus also needs to employ significant energy savings technologies to ensure that energy consumption remains at a minimum, to further reduce the battery size and increase driving range. The impetus for this PhD comes from a desire to achieve this reduction in energy consumption through the implementation of energy saving ECO-strategies leading to optimization of its energy and thermal management. The major obstacle standing in the way of extensive charging infrastructure deployments for heavy-duty vehicles, is the expense needed to purchase and install high-powered chargers and connect them to the grid. These expenses need to be borne by the public transport operator alone, and once installed, the chargers can only be utilized by the fleet of buses operating for that public transport operator. To be more cost effective, the operational duration of each charger needs to be maximized, since the public transport operator cannot deploy too many chargers. Furthermore, due to the limited charging duration allocated to buses, due to the constraints of their respective route schedules, it necessitates high-powered superfast charging. However, when the electricity grid experiences multiple simultaneous high-powered charging, there are severe strains placed on it leading to reductions in grid quality. This problem is addressed using charging management strategies to lower the average and peak load in the grid. Simulations are a great way to theoretically test out new methodologies; however, they require experimental data to validate the results. This is important as the simulation model was designed as a low fidelity model, handling only basic energy transfer computations, with the objective to increase simulation speed necessary to perform optimization for large fleets of buses. To improve the accuracy of this low fidelity model requires validation using experimental data from actual vehicles in the field. Towards this end the measured vehicle speed from the field was utilized along with the measured SoC data of the battery to validate the accuracy of the simulation using optimization to tune powertrain parameters. This PhD oversaw the design and development of an open vehicle powertrain platform that incorporates the standard Hardware in the Loop simulator along with Internet of Things technology to collect measurement data, and send them to the cloud, to train a Digital Twin of the powertrain component in real time using realistic driving scenario.