{'fraction_non_alphanumeric': 0.01486988847583643, 'fraction_numerical': 0.0018587360594795538, 'mean_word_length': 5.358267716535433, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Model predictive control of a power split hybrid powertrain

Hybrid electrical vehicles (HEVs) are able to improve the fuel economy with reduced emissions due to their effective power management and regenerative power utilization. However, developing an optimal supervisory control strategy to distribute power is quite challenging due to the high degrees of freedom introduced by the multiple power sources in HEVs. Conventional approach uses so-called power follower scheme, and the power management can also be achieved by minimizing the equivalent fuel consumption at each given instant without guaranteeing that the battery state of charge (SOC) maintains at its target level. This paper proposes a model predictive control (MPC) strategy based on the linear quadratic tracking (LQT) control to follow the predicted driver power demand over a given horizon while keeping the battery SOC operated at its target level. The LQT controller is developed based upon the linearized control-oriented model and used to optimally track the predicted desired torque trajectory by minimizing the total equivalent fuel consumption of the internal combustion (IC) engine and electric motor (EM) over a given finite prediction horizon at each operational point. The performance of the MPC based LQT controller is validated in simulations under four typical driving cycles. The simulation results show that the fuel economy is greatly improved over the baseline power follower control strategy especially under FTP and IM240 driving cycles, and at the same time the battery state of charge is maintained at the desired level.