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Abstract
The energy management strategy of hybrid electric vehicles is of significant importance to improve the fuel economy. In this regard, two energy management strategies are designed for power-split hybrid electric city bus (HECB), which are based on the linear time-varying stochastic model predictive control (LTV-SMPC) and stochastic model predictive control based on Pontriagin’s minimum principle (PMP-SMPC). In the present study, the Markov chain and long short-term memory (LSTM) forecast demand torque and velocity respectively are applied to establish a combination forecast model. Then several processes, including linear approximation, processing simplified control model, the proposed nonlinear vehicle model is converted into a linear time-varying model. Meanwhile, the energy management problem in a linear quadratic programming problem is solved. Considering linearization error and limitations of the quadratic optimization, Pontriagin’s minimum principle (PMP) is applied to optimize the nonlinear model predictive control. Based on the reference theory, the range of coordinated variables is derived, and the optimal coordination variable is searched by dichotomy to realize the rolling optimization of the model predictive control (MPC). Finally, the effectiveness of the proposed energy management strategy is verified by simulating several case studies. Obtained results show that compared with the rule-based (RB) control strategy, the fuel economy of LTV-SMPC and PMP-SMPC increases by 8.79% and 14.42%, respectively. Meanwhile, it is concluded that the two strategies have real-time computing potential.
Highlights
With the rapidly increasing number of automobiles worldwide, the limitations of petroleum resources and environmental pollution issues have become more prominent in the past few decades
The back propagation (BP) neural network is initially applied to predict the vehicle speed, and the reference state of charge (SOC) is constructed with the optimal depth of discharge (DOD) and it is combined with the Pontriagin’s minimum principle (PMP) to achieve an optimized rolling solution
Compared with the traditional bus and RB, the fuel economy of LTV-SMPC is increased by 38.20% and 7.71%, and that of PMP-SMPC is increased by 40.96% and 11.83%, respectively
Summary
With the rapidly increasing number of automobiles worldwide, the limitations of petroleum resources and environmental pollution issues have become more prominent in the past few decades. The neural network is applied to select a corresponding optimal SOC curve as a reference for a part of the working condition information obtained through the intelligent transportation system In this method, the selected optimal curve was used in the fuzzy logic controller. Xie et al proposed a MPC method based on the optimal depth of discharge (DOD) of the battery In this method, the back propagation (BP) neural network is initially applied to predict the vehicle speed, and the reference SOC is constructed with the optimal DOD and it is combined with the PMP to achieve an optimized rolling solution. It is worth noting that the ICE cannot operate below the idle speed
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