Optimization of PHEV energy management in emission-controlled environments through non-linear model predictive control and long-term cost evaluation

As regulations on pollutant emissions rapidly advance and the demand for sustainable mobility grows, the necessity for innovative technological solutions becomes crucial. To address these challenges, this research focuses on the development and application of a control-oriented model for plug-in hybrid electric vehicles (PHEVs), aimed at minimizing fuel consumption and NOx emissions while respecting operational constraints imposed during the vehicle’s operation. Accordingly, the model developed integrates the powertrain and the after-treatment system based on non-linear model predictive control (NLMPC) framework, strategically modulating the power distribution between the internal combustion engine (ICE) and the electric motor (EM), along with the ammonia injection strategy for effective NOx abatement and fuel savings. To overcome the finite horizon limitations of NLMPC, an offline dynamic programming (DP) was embedded, improving predictive capabilities through a cost-to-go matrix that reflects optimal control actions under specific conditions. This hybrid approach combines the global optimization of DP with the real-time flexibility of NLMPC, allowing dynamic adjustments to vehicle operation in response to real-time data and future scenarios. The applicability of the proposed strategy is demonstrated in routes containing a zero-emission zone and vehicles with different battery sizes, underlining its adaptability to complex driving conditions and distinct vehicle designs, thereby demonstrating its potential for significant contributions to sustainable mobility solutions.