Energy Management of a Parallel Hybrid Electric Vehicle using Model Predictive Static Programming

被引：18

作者：

Biswas, Dhrupad ^{[1
]}

Ghosh, Susenjit ^{[1
]}

Sengupta, Somnath ^{[2
]}

Mukhopadhyay, Siddhartha ^{[1
]}

机构：

[1] Indian Inst Technol Kharagpur, Elect Engn Dept, Kharagpur, W Bengal, India

[2] Indian Inst Technol Kharagpur, Adv Technol Dev Ctr, Kharagpur, W Bengal, India

来源：

ENERGY | 2022年 / 250卷

关键词：

Hybrid electric vehicle; Energy management; Model predictive control; Model predictive static programming; Real; -time; Theory of domination; STRATEGY; MPC; SYSTEMS;

D O I：

10.1016/j.energy.2022.123505

中图分类号：

O414.1 [热力学];

学科分类号：

摘要：

Energy management of Parallel Hybrid Electric Vehicles (PHEVs) involves computation of energy efficient torque-speed set-points for the engine and the motor. Application of Nonlinear Model Predictive Control (NMPC) for this problem has been reported in the recent literature. In this work, a fast iterative implementation of the NMPC, known as Model Predictive Static Programming (MPSP) is deployed for the first time for energy management of PHEVs, with an appropriate multi-objective cost function and the applicable equality and inequality constraints. Additionally, a systematic approach based on the Theory of Dominance is used to tune parameters of the controller efficiently. Performance is evaluated with a validated PHEV model on the ADVISOR simulation environment, in terms of various performance metrics on standard drive cycles. The results show that the performance of the MPSP-based technique is close to the ideal performance achieved with Dynamic Programming (DP) and improved over a standard implementation using the Linear Time-Varying MPC (LTV-MPC) and Sequential Quadratic Programming MPC (SQP-MPC) algorithm. Moreover, its execution time on an industry standard micro-controller board is seen to be significantly less than that of LTV-MPC and SQP-MPC. This algorithm therefore appears to be one of the best candidates for on-board vehicle implementation of an optimal energy management strategy for a PHEV. (c) 2022 Published by Elsevier Ltd.

引用

页数：16

共 36 条

[1] Energy Management Systems for Electrified Powertrains: State-of-the-Art Review and Future Trends [J].

Biswas, Atriya ;

Emadi, Ali .

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2019, 68 (07) :6453-6467

[2] MPC-Based Energy Management of a Power-Split Hybrid Electric Vehicle [J].

Borhan, Hoseinali ;

Vahidi, Ardalan ;

Phillips, Anthony M. ;

Kuang, Ming L. ;

Kolmanovsky, Ilya V. ;

Di Cairano, Stefano .

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2012, 20 (03) :593-603

[3] Stochastic model predictive control for energy management of power-split plug-in hybrid electric vehicles based on reinforcement learning [J].

Chen, Zheng ;

Hu, Hengjie ;

Wu, Yitao ;

Zhang, Yuanjian ;

Li, Guang ;

Liu, Yonggang .

ENERGY, 2020, 211 (211)

[4]

Dallas Texas United States Instruments T, 2016, BQ76PL455A Q1 16 CEL

[5]

Deb K., 2014, Search Methodologies: Introductory Tutorials in Optimisation and Decision Support Techniques, P403, DOI [DOI 10.1007/978-1-4614-6940-7_15, 10.1007/978-1-4614-6940-7_15]

[6] Stochastic MPC With Learning for Driver-Predictive Vehicle Control and its Application to HEV Energy Management [J].