Performance Comparison of Real-Time Solver Implementations for Powertrain Nonlinear Energy Management Optimization with MPC.

During recent years, commercial vehicle manufacturers have introduced legally required and advanced predictive functionalities into their vehicles. By applying modern supervisory Model Predictive Controllers (MPCs) for vehicle energy management and by also using the live traffic data, it is possible to reduce efficiently the fuel consumption and pollutant emissions. The general objective is to minimize fuel consumption by optimizing the speed and gear without affecting the overall travel time. In this work, the objective is to compare the performance of two modern optimization solvers, the interior-point solver FORCES Pro and ACADO that uses the active-set solver qpOASES for solving the energy management problem without controlling the powertrain states like gear selection. The novel energy management cost function consists of 6 optimization variables, different distance based discretization levels with velocity and time as state variables, whereby a solution is obtained within a few milliseconds at each iteration. Such controllers are applied to a 40-ton truck simulator and verified with several driving cycles. The novel approach achieves a fuel consumption decrease by more than 7% and less than 2% increase in execution time with the FORCES Pro solver compared to non-predictive rule based strategies. Also, the FORCES Pro solver was found to be 50 times faster than ACADO. The innovative software and solver framework provide an efficient energy management solution for future conventional and hybrid drivetrains.