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Research on Adaptive Optimal Control Strategy of Parallel Plug-In Hybrid Electric Vehicle Based on Route Information

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Abstract

Since fuel consumption of parallel Plug-in hybrid electric vehicles (PHEVs) is significantly different in various driving cycles, an adaptive optimal control strategy based on route information is proposed. First, the energy management strategy structure of adaptive Pontryagin’s Minimum Principle (APMP) is established. Second, genetic algorithm is employed to obtain a Map, which is co-state function of driving distances and state of charge (SOC) and makes this strategy used online. Furthermore, co-state value is updated in real time according to route information and reference SOC, which improves the adaptability of APMP control strategy. Finally, MATLAB/Simulink and AVL/Cruise software are employed as simulation tools to verify the proposed APMP control strategy. The performance of APMP control strategy is compared to rule-based (RB) control strategy. Results show that the proposed APMP control strategy could greatly improve the fuel economy of the parallel PHEV for different driving conditions.

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Abbreviations

f :

state variable function

G :

genetic function

H :

hamilton function

J :

fuel consumption, kg

ṁ f :

instantaneous fuel consumption rate, kg/s

n :

rotation speed, rpm

S :

distance, km

SOC :

state of charge

t :

time, s

T :

torque, Nm

u :

control variable

V :

vehicle speed, m/s

x :

state variable

λλ:

co-state variable, kg

f :

state variable function

ave:

average

ini:

initial

ter:

terminal

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Acknowledgement

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by National Key R&D Program of China (grant no. 2018YFB0106200) and the Jilin Province Key Scientific and Technological Research Projects of China (grant no. 20170204085GX) and the National Natural Science Foundation of China (grant no. 51775229).

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Authors and Affiliations

  1. School of Mechanical and Aerospace Engineering, Jilin University, Changchun, 130022, China

    Kangjie Liu

  2. State Key Laboratory of Automotive Dynamic Simulation and Control, Jilin University, Changchun, 130022, China

    Jianhua Guo, Liang Chu & Yuanbin Yu

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  1. Kangjie Liu
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  2. Jianhua Guo
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  3. Liang Chu
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  4. Yuanbin Yu
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Correspondence to Jianhua Guo.

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Liu, K., Guo, J., Chu, L. et al. Research on Adaptive Optimal Control Strategy of Parallel Plug-In Hybrid Electric Vehicle Based on Route Information. Int.J Automot. Technol. 22, 1097–1108 (2021). https://doi.org/10.1007/s12239-021-0098-z

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