Handling State Constraints in Fast-computing Optimal Control for Hybrid Powertrains
Abstract
To optimally design hybrid powertrains, optimal energy management strategies must be automatically and rapidly generated. Pontryagin’s minimum principle-derived optimization tool called Hybrid Optimization Tool (HOT) can guarantee the fast computing of minimal fuel consumption using an array operation as well as Picard’s method. However, in presence of state constraints (e.g., the battery state of charge limitations), the near-optimality of HOT no longer holds. Herein, we use the interior- and exterior-penalty method to impose the state constraints in HOT and highlight numerical difficulties encountered in their implementation. Then, a factor that causes the numerical difficulties is optimized by quantifying trade-off between the state constraints violation and computational demanding. Finally, through a case study of a parallel hybrid electric vehicle, the results show that despite of a complex problem with rapidly changing dynamics, the penalty methods are able to generate results comparable with dynamic programming ones while guaranteeing the low computational burden.