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Electrified transportation has received significant interest recently because of sustainable and clean energy goals. However, the degradation of electrical components such as energy storage systems raises system reliability and economic concerns. In this paper, a prognostic-based control strategy is proposed for hybrid electric vehicles (HEVs) to abate the degradation of energy systems. Degradation forecasting models of electrical components are developed to predict their degradation paths. The predicted results are then used to control HEVs in order to reduce the degradation of components.

This study proposes a multi-objective finite control set model predictive control (FCS-MPC) for traction motor drive systems in electric/hybrid-electric vehicles. The proposed method seeks to find the most optimal drive with respect to three objectives, i.e., electric power quality, inverter thermal cycling, and motor thermal cycling. Suitable lumped-parameter thermal models are used for the inverter and the motor based on validated methods in the literature to estimate temperatures. The estimated temperatures are integrated into the multi-objective control law to obtain the desired trade-off performances from the drive system. This paper shows that by adding inverter and motor thermal models into the FCS-MPC, thermal cycling can be reduced in the inverter and the motor while maintaining satisfying speed/torque requirements. The proposed methodology is tested via a standard driving schedule for an interior permanent magnet traction motor in a hybrid electric vehicle.

The increasing electrification of vehicles raises system reliability concerns as the electrical and electronic components deteriorate faster after an event. In addition, the traditional method of scheduled maintenance is not efficient for managing a fleet of vehicles; because, the degradation processes are distinct in different vehicles. Therefore, integrating an online degradation forecasting and abatement module into a vehicle that is able to assess the vehicle status and predict the degradation process to take timely appropriate actions to reach satisfactory reliability and long-term goals, is valuable. Quantifying uncertainty is one of the main challenges of degradation forecasting; because, the degradation process of a vehicular system is distinct. This paper proposes an online degradation forecasting framework to predict the degradation processes to reallocate energy sources in the system, obtaining long-term goals while adhering to the reliability requirements.

This research proposes to leverage the fast response time of Permanent Magnet Synchronous Motors (PMSMs) to compensate for crank angle resolved engine torque variations caused by cycle-by-cycle combustion variations. This method reduces powertrain vibration and enables engine calibrations with high combustion variation that produces low fuel consumption. This research integrates a Field Oriented Control (FOC) strategy with an Explicit Model Predictive Control (EMPC) to trace previewed current references. The previewed current references are computed from the engine torque difference between predicted nominal operation and the measured torque output. This research reveals that the MPC can track a d-q current reference without overshoot, rendering current magnitude constraints unnecessary in the MPC formulation. A control rate penalty is used to tune the aggressiveness of transient voltage demand and meet with the DC voltage limit.