Comparison of architecture and adaptive energy management strategy for plug-in hybrid electric logistics vehicle

This paper deals with the comparison of architecture and adaptive energy management strategy (EMS) for hybrid powertrain system (HPS), including one or two electric motor, an engine and a battery, for a plug-in hybrid electric logistics vehicle (PHELV). The most attractive advantage deriving from HPSs is the possibility of reducing emission and improving fuel economic. For comparison purposes, the series, parallel, and series-parallel hybrid powertrain system are examined by dynamic programming (DP) al-gorithm using the same vehicular parameters. The approach of adaptive EMS is driving pattern recog-nition (DPR) to obtain optimum estimation of EMS parameters under different driving cycle. A back propagation (BP) neural network DPR optimized model by an improved genetic algorithm (IGA) has been proposed. Taking the costs of fuel consumption, the parameters of the fuzzy logic controller (FLC) and equivalent consumption minimization strategy (ECMS) are optimized. The comparative results show that the series PHELV fuel economy improvements are 7.60% and 6.53%, compared with parallel and series-parallel PHELV. The difference between the optimal fuzzy energy management strategy and the global optimization is 4.74%, and the ECMS is 4.66%. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This paper compares the architecture and adaptive energy management strategy (EMS) for plug-in hybrid electric logistics vehicles (PHELV) with different hybrid powertrain systems using dynamic programming (DP) algorithm. An approach of adaptive EMS based on driving pattern recognition (DPR) is proposed, optimizing parameters for fuel consumption cost, fuzzy logic controller (FLC), and equivalent consumption minimization strategy (ECMS). Comparative results show improvements in fuel economy for series PHELV compared to parallel and series-parallel PHELV, with differences between optimal energy management strategies and global optimization.