Development of Global Optimization Algorithm for Series-Parallel PHEV Energy Management Strategy Based on Radau Pseudospectral Knotting Method

Zhao, Kegang; Bei, Jinghao; Liu, Yanwei; Liang, Zhihao

doi:10.3390/en12173268

Cited by 5 publications

(1 citation statement)

References 37 publications

(42 reference statements)

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“…[13][14][15] The common optimization algorithms include dynamic programming (DP), 16,17 Pontryagin's minimum principle (PMP), etc. 18 To address the above-mentioned limitation of global optimization strategy, the approximate optimal EMS is applied. Due to its global optimization ability as well as real-time computing potential, approximate optimal EMS has received significant research attention and has made substantial progress.…”

Section: Introductionmentioning

confidence: 99%

MPGA-based-ECMS for energy optimization of a hybrid electric city bus with dual planetary gear

Yang

Tan

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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To improve the fuel economy and reduce the exhaust emissions of a hybrid electric city bus (HECB) with dual planetary gear, a vehicle model is proposed based on the coupling mechanism between engine and battery motor in the gear. Then, two kinds of adaptive equivalent consumption minimization strategy (ECMS) algorithms based on fuzzy proportional-integral (PI) controller: Fuzzy PA-ECMS and Fuzzy MPGA-ECMS (MGPA: multiple population genetic algorithm), are established to improve the control effect of ECMS with equivalent factor (EF) as the core. Firstly, an approximate expression of optimal EF is derived based on the Pontryagin’s minimum principle (PMP). Subsequently, the deviation between the reference state of charge (SOC) and the actual SOC and the corresponding variation rate are calculated, which are combined with the fuzzy logic controller to adjust the EF. Finally, the driving style is introduced to rectify the trajectory of EF. According to different driving conditions (different initial values of SOC), the PI parameters of EF are optimized offline by multiple population genetic algorithm. Meanwhile, the interval of PI parameters is continuously optimized by the fuzzy controller. Then, simulation experiments are conducted to verify the efficacy of the two proposed algorithms. The simulation results show that compared to the conventional bus and rule-based control strategy, the proposed Fuzzy PA-ECMS algorithm can improve the fuel economy of bus by 41.70% and 5.29%, respectively. Further, compared to Fuzzy PA-ECMS, Fuzzy MPGA-ECMS algorithm can improve the fuel economy of bus by 0.3%.

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