Abstract
Splitting power is a tricky problem for series plug-in hybrid electric vehicles (SPHEVs) for the multi-working modes of powertrain and the hard prediction of future power request of the vehicle. In this work, we present a methodology for splitting power for a battery pack and an auxiliary power unit (APU) in SPHEVs. The key steps in this methodology are (a) developing a hybrid automaton (HA) model to capture the power flows among the battery pack, the APU and a drive motor (b) forecasting a power request sequence through a Markov prediction model and the maximum likeli-hood estimation approach (c) formulating a constraint stochastic optimal control problem to minimize fuel consumption and at the same time guarantee the dynamic performance of the vehicle (d) solving the optimal control problem using the model predictive control technique and the YALMIP toolbox. Our simulation experimental results show that with our stochastic model predictive control strategy a series plug-in hybrid electric vehicle can save 1.544 L gasoline per 100 kilometers compared to another existing power splitting strategy.
Highlights
Series plug-in hybrid electric vehicles (SPHEVs) are emerging as an attractive alternative for fuel-efficient vehicles
In addition to the multiple working modes of the powertrain mentioned above, other hybrid dynamics create the hybrid nature of the powertrain of series plug-in hybrid electric vehicles (SPHEVs), such as the variations in engine state, and the limited availability of the battery pack due to the upper and lower boundaries on its state of charge (SOC) [4]
In order to guarantee the dynamic performance of the vehicle, the SOC of the battery pack is usually required to be higher than 25% during the whole driving range [5]
Summary
Series plug-in hybrid electric vehicles (SPHEVs) are emerging as an attractive alternative for fuel-efficient vehicles. Compared to HEVs, the power splitting of SPHEVs need to guarantee the dynamic performance of the vehicle while minimizing the fuel cost To this end, we add a time-varying constraint to the state of charge of the battery pack while splitting the power for SPHEVs. For modeling the powertrain of SPHEVs, previous works treat this as a linear system [19]. We propose a novel method with a SOC penalty function to guarantee the vehicle dynamic performance while minimizing the fuel consumption We solve this optimal control problem with stochastic model predictive control technique. We test the SMPC approach on the diving cycle, and compare the performance of SMPC approach with a deterministic MPC technique mentioned in [19]
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