Nonlinear model predictive control based fuel-efficient adaptive vehicle spacing strategy for heavy-duty vehicle platooning

This thesis presents an approach for enhancing fuel efficiency in heavy-duty vehicle platooning through the implementation of an adaptive spacing strategy. The optimization design incorporates two crucial components, namely the nonlinear fuel consumption model of a diesel engine and the nonlinear air drag model. By integrating these elements into the overall cost function, a nonlinear model predictive controller is devised to calculate an adaptive time headway strategy. The primary focus is minimizing fuel consumption by adjusting the time headway which also affects the air drag coefficient. However, simply reducing the time headway and the air drag coefficient may not always be the most fuel-efficient strategy. In some scenarios, keeping up to the minimum set time headway can lead to excessive control effort, resulting in higher fuel consumption because the vehicle has to operate its engine within the inefficient fuel map region. The proposed dynamic strategy allows modifying the intervehicular distance within certain boundaries in order to optimize the potential benefits of aerodynamic drag reduction while respecting the engine's fuel map. To assess the effectiveness of the control design and validate the expected outcomes, extensive closed-loop simulations are conducted. A benchmark truck model is utilized, and various road topography conditions involving uphill and downhill slopes are considered. The simulation results underscore the efficacy of the adaptive time headway strategy in reducing fuel consumption for heavy-duty trucks across different scenarios. When compared to the lead vehicle, fuel consumption is reduced by up to 8%, and compared to a constant time headway approach, reductions of up to 3% are observed.