Model Predictive Control for GPS based Eco-Driving

Abstract

In the past century, the depletion of petroleum and its harmful effects on the environment has increasingly become a concern to the people. To help mitigate these issues, it is imperative that a proper optimisation system is devised to make vehicles more fuel-efficient. Model predictive control (MPC) is a process control method capable of of anticipating future events over a predictive horizon and adapting to them before they occur. The MPC controller can be applied to a vehicle and fuel consumption model to obtain the optimal vehicle velocity and acceleration at each timestep. This would in turn minimise the overall fuel consumption of the vehicle by avoiding any unnecessary engine power usage. A route on the Putrajaya-Dengkil-Sepang road is chosen as the road profile for its constantly varying altitudes, allowing the MPC controller's capability to be assessed. An adaptive cruise control system modelled as a proportional-integrative-derivative (PID) controller and a pulse-and-glide system were modelled to act as comparisons for the MPC controller. It was found that the MPC controller consumed 4.76 L of fuel at the end of the run, resulting in carbon dioxide emissions of 10.94 kg, 1.36% less than the PID controller and 7.06% less than the pulse-and-glide one. This means that the MPC controller is the most fuel-efficient of the three at adapting to a road with varying altitudes.