An Augmented Lagrangian-Based Optimal Control: An Application towards an Electric Vehicle

Electric vehicles are a vital addition to the intelligent transportation systems. They bring features which could provide enhancements to decision making while improving on information and support systems. The critical nature of control is one of the main concerns for their advanced operations. In this work, the vehicle has been considered as a constrained system, where parameter optimization is required during the transition between different set-points of degree of freedom. Firstly, dynamics of the vehicle have been derived. Each vehicle dynamic has been monitored by a modeled sensor. Secondly, constrained optimization-based cost function has been set. This cost function is considered here to be the main building block for deriving augmented Lagrangian-based optimization to provide optimal control to the vehicle. Thirdly, co-states of the derived constraints are determined to maintain convergence. Numerical evaluations were made on a vehicle model. Results showed an ensured performance and accuracy of the proposed scheme.