Abstract
The powertrain model of the series-parallel plug-in hybrid electric vehicles (PHEVs) is more complicated, compared with series PHEVs and parallel PHEVs. Using the traditional dynamic programming (DP) algorithm or Pontryagin minimum principle (PMP) algorithm to solve the global-optimization-based energy management strategies of the series-parallel PHEVs is not ideal, as the solution time is too long or even impossible to solve. Chief engineers of hybrid system urgently require a handy tool to quickly solve global-optimization-based energy management strategies. Therefore, this paper proposed to use the Radau pseudospectral knotting method (RPKM) to solve the global-optimization-based energy management strategy of the series-parallel PHEVs to improve computational efficiency. Simulation results showed that compared with the DP algorithm, the global-optimization-based energy management strategy based on the RPKM improves the computational efficiency by 1806 times with a relative error of only 0.12%. On this basis, a bi-level nested component-sizing method combining the genetic algorithm and RPKM was developed. By applying the global-optimization-based energy management strategy based on RPKM to the actual development, the feasibility and superiority of RPKM applied to the global-optimization-based energy management strategy of the series-parallel PHEVs were further verified.
Highlights
Hybrid electric vehicles (HEVs) combine the advantages of traditional internal combustion engine vehicles and pure electric vehicles [1]
By applying the global-optimization-based energy management strategy based on Radau pseudospectral knotting method (RPKM) to the actual development, the feasibility and superiority of RPKM applied to the global-optimization-based energy management strategy of the series-parallel Plug-in hybrid electric vehicles (PHEVs) were further verified
The energy management strategy based on global optimization needs to predict all the information regarding driving conditions, and its calculated amount is large, which is difficult to perform in real time [14]
Summary
Hybrid electric vehicles (HEVs) combine the advantages of traditional internal combustion engine vehicles and pure electric vehicles [1]. The energy management strategy based on global optimization needs to predict all the information regarding driving conditions, and its calculated amount is large, which is difficult to perform in real time [14]. To solve the problems of existing global optimization algorithms and meet the needs of chief engineers of hybrid system, a pseudospectral knotting-based method that solves the global optimization problem of PHEV energy management strategies to improve computational efficiency is proposed in this paper. Radau pseudospectral knotting method (RPKM) was used to determine the series-parallel PHEV power system scheme with the best fuel economy It showed that the hybrid-powertrain optimization matching program combined with the RPKM can better meet the needs of the actual development process.
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