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Advanced Driver Assistance Systems (ADAS) is an essential aspect of the automotive technology in this era of technological revolution, where the goal is to make vehicles more convenient, safe, and energy efficient. Taking advantage of more degrees of freedom available within vehicle “energy management” allows more margin to maximize efficiency in the propulsion systems. It is envisioned by this research that future fuel economy regulations will consider the potential benefits of emerging connectivity and automation technologies of vehicle’s fuel consumption. The application focuses on reducing the energy consumption in vehicles by acquiring information about the road grade. Road elevation are obtained by use of Geographic Information System (GIS) maps in order to optimize the controller. The optimization is then reflected on the powertrain of the vehicle. The approach uses a Model Predictive Control (MPC) algorithm that allows the energy management strategy to leverage road grade.

Electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) are considered as a promising future solution for sustainable transportation. This is due to the reduction in energy consumption when compared to conventional internal combustion engine (ICE) based vehicles. EVs and PHEVs contain an Energy Storage Systems (ESS). This increases the complexity of the system but also provides additional margins and fields for optimization. One of the most important elements of these vehicles is the ESS. The electrochemistry nature of battery systems is inherently sensitive to the temperature shifts. The shifts are controlled by the thermal management system of the traction battery systems, for electric-drive vehicles, which directly affects the overall vehicle dynamics. These dynamics include performance, long-term durability, and cost of the battery systems. Hence, thermal management becomes an essential element in the achievement to meet the demand for better performance.