Battery Health Aware Nonlinear Model Predictive Control of a Parallel Electric–Hydraulic Hybrid Wheel Loader

Parallel electric–hydraulic hybrid (PEHH) powertrains offer benefits of lower energy consumption and increased battery lifetime compared to pure electric ones. These merits can be extended with advanced control methods that optimally deploy on-board energy sources. This paper proposes a nonlinear model predictive control (NMPC) energy management strategy (EMS) for a PEHH wheel loader. The optimization minimizes energy usage and battery degradation by selecting the optimal power ratio between the electric and hydraulic subsystems. The state prediction is based on a discrete nonlinear dynamic model and an estimate of the future exogenous inputs developed from a high-fidelity digital-twin model of a wheel loader. The NMPC formulation is compared to a baseline rule-based EMS inspired by offline optimal control. The proposed NMPC results in 31.7% less battery degradation and 9.14% energy consumption reduction even with a 20% error in the preview information. Hardware-in-the-loop (HiL) experiments validate our results and show that the NMPC EMS can be implemented in real time even with higher prediction error increasing the maximum computational time.