Optimal rule extraction-based real-time energy management strategy for series-parallel hybrid electric vehicles

Energy management strategy plays an important role in improving the fuel economy of hybrid electric vehicles. In this paper, an energy management strategy optimized by dynamic programming for a series-parallel hybrid electric vehicle is proposed. The key components of the powertrain system are modeled, and the model accuracy is corrected with the experimental data. To construct the real-time energy management strategy, the dynamic programming algorithm is employed first to find the optimal control benchmark, and then the control sequence with optimal fuel economy is obtained to analyze and build the mapping relationship between control variables and driving conditions. To fulfill the requirement of real-world application, the influence of the engine warm-up process on fuel consumption is also investigated. Finally, according to the optimal control datasets, the optimal operating mode distribution of clutches and the optimal power of the engine and motor are extracted to generate the real-time strategy. Benefiting from the optimal control sequence of dynamic programming, this study explores optimal operating characteristics of powertrain components towards fuel saving and enables the strategy conversion from global optimal to real-time application, which provides opportunities to achieve approximate optimal fuel economy of hybrid electric vehicles in real-time.

Automated summary

This paper proposes an energy management strategy optimized by dynamic programming for a series-parallel hybrid electric vehicle. The strategy is modeled and validated with experimental data, and the mapping relationship between control variables and driving conditions is analyzed. The influence of the engine warm-up process on fuel consumption is investigated, and the optimal operating mode distribution of clutches and the optimal power of the engine and motor are extracted to generate the real-time strategy.