A Calculation-Efficient Predictive Energy Management Strategy for Fuel Cell Hybrid Electric Bus based on Improved Optimization Solver

Miaojue Gao; Hongwen He; Ruchen Huang

doi:10.1109/ICIEA58696.2023.10241792

A Calculation-Efficient Predictive Energy Management Strategy for Fuel Cell Hybrid Electric Bus based on Improved Optimization Solver

Miaojue Gao, Hongwen He^*, Ruchen Huang

^*Corresponding author for this work

School of Mechanical Engineering

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper proposes a calculation-efficient predictive energy management strategy (EMS) for a fuel cell hybrid electric bus (FCHEB) based on an improved optimization solver. A deep neural network (DNN) based speed predictor is trained using real bus driving dataset and then adopted to predict the bus speed. After that, the direct collocation method is applied to transform the energy management problem to a nonlinear optimization one. Moreover, a sequential quadratic programming (SQP) based high-precision solver combined with a low-precision dynamic programming (DP) solver is used to optimize the power allocation. Simulation results indicate that the proposed EMS can improve both the calculation accuracy and efficiency, decreasing the equivalent hydrogen consumption by 0.92% compared to the predictive EMS with a traditional DP solver.

Original language	English
Title of host publication	Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023
Editors	Wenjian Cai, Guilin Yang, Jun Qiu, Tingting Gao, Lijun Jiang, Tianjiang Zheng, Xinli Wang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1123-1127
Number of pages	5
ISBN (Electronic)	9798350312201
DOIs	https://doi.org/10.1109/ICIEA58696.2023.10241792
Publication status	Published - 2023
Event	18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023 - Ningbo, China Duration: 18 Aug 2023 → 22 Aug 2023

Publication series

Name	Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

Conference

Conference	18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023
Country/Territory	China
City	Ningbo
Period	18/08/23 → 22/08/23

Keywords

direct collocation method
energy management strategy
fuel cell hybrid electric bus
model predictive control
sequential quadratic programming

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10.1109/ICIEA58696.2023.10241792

Cite this

Gao, M., He, H., & Huang, R. (2023). A Calculation-Efficient Predictive Energy Management Strategy for Fuel Cell Hybrid Electric Bus based on Improved Optimization Solver. In W. Cai, G. Yang, J. Qiu, T. Gao, L. Jiang, T. Zheng, & X. Wang (Eds.), Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023 (pp. 1123-1127). (Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIEA58696.2023.10241792

Gao, Miaojue ; He, Hongwen ; Huang, Ruchen. / A Calculation-Efficient Predictive Energy Management Strategy for Fuel Cell Hybrid Electric Bus based on Improved Optimization Solver. Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023. editor / Wenjian Cai ; Guilin Yang ; Jun Qiu ; Tingting Gao ; Lijun Jiang ; Tianjiang Zheng ; Xinli Wang. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 1123-1127 (Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023).

@inproceedings{5d017ce6e5c14e128e11ffa9987e4dc7,

title = "A Calculation-Efficient Predictive Energy Management Strategy for Fuel Cell Hybrid Electric Bus based on Improved Optimization Solver",

abstract = "This paper proposes a calculation-efficient predictive energy management strategy (EMS) for a fuel cell hybrid electric bus (FCHEB) based on an improved optimization solver. A deep neural network (DNN) based speed predictor is trained using real bus driving dataset and then adopted to predict the bus speed. After that, the direct collocation method is applied to transform the energy management problem to a nonlinear optimization one. Moreover, a sequential quadratic programming (SQP) based high-precision solver combined with a low-precision dynamic programming (DP) solver is used to optimize the power allocation. Simulation results indicate that the proposed EMS can improve both the calculation accuracy and efficiency, decreasing the equivalent hydrogen consumption by 0.92% compared to the predictive EMS with a traditional DP solver.",

keywords = "direct collocation method, energy management strategy, fuel cell hybrid electric bus, model predictive control, sequential quadratic programming",

author = "Miaojue Gao and Hongwen He and Ruchen Huang",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023 ; Conference date: 18-08-2023 Through 22-08-2023",

year = "2023",

doi = "10.1109/ICIEA58696.2023.10241792",

language = "English",

series = "Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1123--1127",

editor = "Wenjian Cai and Guilin Yang and Jun Qiu and Tingting Gao and Lijun Jiang and Tianjiang Zheng and Xinli Wang",

booktitle = "Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023",

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Gao, M, He, H & Huang, R 2023, A Calculation-Efficient Predictive Energy Management Strategy for Fuel Cell Hybrid Electric Bus based on Improved Optimization Solver. in W Cai, G Yang, J Qiu, T Gao, L Jiang, T Zheng & X Wang (eds), Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023. Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023, Institute of Electrical and Electronics Engineers Inc., pp. 1123-1127, 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023, Ningbo, China, 18/08/23. https://doi.org/10.1109/ICIEA58696.2023.10241792

A Calculation-Efficient Predictive Energy Management Strategy for Fuel Cell Hybrid Electric Bus based on Improved Optimization Solver. / Gao, Miaojue; He, Hongwen; Huang, Ruchen.
Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023. ed. / Wenjian Cai; Guilin Yang; Jun Qiu; Tingting Gao; Lijun Jiang; Tianjiang Zheng; Xinli Wang. Institute of Electrical and Electronics Engineers Inc., 2023. p. 1123-1127 (Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Calculation-Efficient Predictive Energy Management Strategy for Fuel Cell Hybrid Electric Bus based on Improved Optimization Solver

AU - Gao, Miaojue

AU - He, Hongwen

AU - Huang, Ruchen

PY - 2023

Y1 - 2023

N2 - This paper proposes a calculation-efficient predictive energy management strategy (EMS) for a fuel cell hybrid electric bus (FCHEB) based on an improved optimization solver. A deep neural network (DNN) based speed predictor is trained using real bus driving dataset and then adopted to predict the bus speed. After that, the direct collocation method is applied to transform the energy management problem to a nonlinear optimization one. Moreover, a sequential quadratic programming (SQP) based high-precision solver combined with a low-precision dynamic programming (DP) solver is used to optimize the power allocation. Simulation results indicate that the proposed EMS can improve both the calculation accuracy and efficiency, decreasing the equivalent hydrogen consumption by 0.92% compared to the predictive EMS with a traditional DP solver.

AB - This paper proposes a calculation-efficient predictive energy management strategy (EMS) for a fuel cell hybrid electric bus (FCHEB) based on an improved optimization solver. A deep neural network (DNN) based speed predictor is trained using real bus driving dataset and then adopted to predict the bus speed. After that, the direct collocation method is applied to transform the energy management problem to a nonlinear optimization one. Moreover, a sequential quadratic programming (SQP) based high-precision solver combined with a low-precision dynamic programming (DP) solver is used to optimize the power allocation. Simulation results indicate that the proposed EMS can improve both the calculation accuracy and efficiency, decreasing the equivalent hydrogen consumption by 0.92% compared to the predictive EMS with a traditional DP solver.

KW - direct collocation method

KW - energy management strategy

KW - fuel cell hybrid electric bus

KW - model predictive control

KW - sequential quadratic programming

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DO - 10.1109/ICIEA58696.2023.10241792

M3 - Conference contribution

AN - SCOPUS:85173613208

T3 - Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

SP - 1123

EP - 1127

BT - Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

A2 - Cai, Wenjian

A2 - Yang, Guilin

A2 - Qiu, Jun

A2 - Gao, Tingting

A2 - Jiang, Lijun

A2 - Zheng, Tianjiang

A2 - Wang, Xinli

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

Y2 - 18 August 2023 through 22 August 2023

ER -

Gao M, He H, Huang R. A Calculation-Efficient Predictive Energy Management Strategy for Fuel Cell Hybrid Electric Bus based on Improved Optimization Solver. In Cai W, Yang G, Qiu J, Gao T, Jiang L, Zheng T, Wang X, editors, Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 1123-1127. (Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023). doi: 10.1109/ICIEA58696.2023.10241792

A Calculation-Efficient Predictive Energy Management Strategy for Fuel Cell Hybrid Electric Bus based on Improved Optimization Solver

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