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Adaptive iterative learning control of internal temperature for high-speed trains

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Abstract

The setting curve and control method of internal temperature in trains are studied in this paper to achieve energy saving control for air conditioning systems. First, a model of interior temperature that considers the heat transmission from infiltration air is established according to the relationship between the equivalent leakage area for the train body and the pressure difference between the internal and external carriage. Second, the interior temperature setting curve, which changes adaptively with the ambient temperature, is established considering the energy consumption of air conditioning systems and passengers’ thermal comfort. Finally, an adaptive iterative learning control (AILC) algorithm is proposed and applied to control the interior temperature according to the periodicity of train operation. In this paper, meteorological data from a typical meteorological year are used for simulation analysis. Simulation results show that the AILC algorithm can effectively track the interior temperature setting value and has a better control effect when the train runs on the same line repeatedly. Under the action of the AILC algorithm, the root-mean-square error is reduced to 0.069 °C in the third iteration. Furthermore, the energy saving for a single carriage in a single process reaches up to 1.534 kW·h, thus achieving energy saving control.

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Abbreviations

c :

Specific heat capacity (J/kg·°C)

d a :

Moisture content of air (kg/kg)

e :

Error

F :

Heat transfer area (m2)

h :

Enthalpy of air (J/kg)

h 1 :

Enthalpy of refrigerant before entering the compressor (J/kg)

h 4 :

Enthalpy of refrigerant flow out of the expansion valve (J/kg)

J :

Solar radiation intensity (W/m2)

k :

Heat transfer coefficient (W/m2·K)

K P :

Proportionality coefficient

L :

Length of the error signal

m :

Mass (kg)

m a :

Mass flow rate of refrigerant (kg/s)

M :

Molar mass (g/mol)

n :

Number of occupations

n r :

Speed of the compressor (r/s)

P :

Pressure (Pa)

q r :

Solar radiation entering the train through window glasses (W/m2)

q s :

Average heat dissipation for an adult (W/person)

q ρ :

Solar radiation absorbed by window glasses (W/m2)

Δ q :

Cooling capacity per unit of refrigerant (J/kg)

Q :

Heat changes in the carriages (W)

Q 0 :

Cooling capacity (W)

Q 1 :

Heat transmission from insulation walls (W)

Q 2 :

Heat transmission from window glasses (W)

Q 3 :

Heat dissipation from occupations (W)

Q 4 :

Heat dissipation from equipment (W)

Q 5 :

Heat transmission from fresh air (W)

Q 6 :

Heat transmission from infiltrating air (W)

R :

Molar gas constant (J/mol·K)

s :

Shading factor of window glasses

S :

Area of the sealing gap (m2)

t :

Time (s)

T :

Temperature (°C)

T B0 :

Setting value of interior temperature (°C)

T C :

Solar-air temperature (°C)

V :

Volume of the train (m3)

V fa :

Gas exchange caused by air conditions (m3/s)

V gap :

Gas exchange caused by the sealing gap (m3/s)

V s :

Compressor displacement per revolution (m3/r)

α :

Convection heat transfer coefficient (W/m2·K)

κ :

Absorptivity to the solar radiation

η :

Volumetric efficiency of compressors

ρ :

Density of air (kg/m3)

ρ α :

Density of refrigerant (kg/m3)

φ r :

Clustering coefficient

B :

Internal air

H :

External air

i, j :

Serial number

w :

Window glasses

AC :

Adaptive control

AE :

Absolute error

AILC :

Adaptive iterative learning control

APE :

Absolute percentage error

DP :

Difference in pressure

DT :

Difference between ambient temperature and interior temperature

ELA :

Equivalent leakage area

HVAC :

Heating, ventilation, and air conditioning

HSTs :

High-speed trains

ILC :

Iterative learning control

MAE :

Maximum absolute error

MAPE :

Maximum absolute percentage error

MDT :

Maximum DT

PC :

Power consumption

RMSE :

Root-mean-square error

TC :

Traditional control

VVVF :

Variable voltage and variable frequency

References

  1. China Ministry of Railways, Railway Statistical Communique in 2022, China Ministry of Railways, China, http://www.china-railway.com.cn.

  2. Z. Wang, J. Xu, Y. Wang, L. Wang and R. Wang, Annual energy simulation for the air conditioning of Fuxing high speed trains, Applied Thermal Engineering, 188 (2021) 116591.

    Article  Google Scholar 

  3. W. Liu, Q. Deng, W. Huang and R. Liu, Variation in cooling load of a moving air-conditioned train compartment under the effects of ambient conditions and body thermal storage, Applied Thermal Engineering, 31(6–7) (2011) 1150–1162.

    Article  Google Scholar 

  4. H. Wang, H. Bi, Y. Zhou and C. Li, Field measurements and numerical analysis of the energy consumption of urban rail vehicle air-conditioning systems, Applied Thermal Engineering, 177 (2020) 115497.

    Article  Google Scholar 

  5. J. Zhang, X. Li, T. Zhao and W. Dai, Experimental study on a novel fuzzy control method for static pressure reset based on the maximum damper position feedback, Energy and Buildings, 108 (2015) 215–222.

    Article  Google Scholar 

  6. J. Bai, S. Wang and X. Zhang, Development of an adaptive smith predictor-based self-tuning PI controller for an HVAC system in a test room, Energy and Buildings, 40(12) (2008) 2244–2252.

    Article  Google Scholar 

  7. S. Prívara, J. Široký, L. Ferkl and J. Cigler, Model predictive control of a building heating system: the first experience, Energy and Buildings, 43(2–3) (2011) 564–572.

    Article  Google Scholar 

  8. Z. Qiu and Y. Fang, Simulation of refrigeration control system in air-condition of railway passenger car, Electric Drive for Locomotives, 1 (2016) 62–66+70.

    Google Scholar 

  9. X. Sun, Temperature and humidity control of air conditioning system for CRH3 high-speed train, Dissertation, Dalian Jiaotong University (2015) (in Chinese).

  10. C. Li, J. Wu and F. Wang, Dynamic load calculation and energy conservation strategy study for “four vertical and four horizontal” high-speed rail air conditioner, Journal of Mechanical Engineering, 54(18) (2018) 162–169 (in Chinese).

    Article  Google Scholar 

  11. M. Uchiyama, Formation of high-speed motion pattern of a mechanical arm by trial, Transactions of the Society of Instrument and Control Engineers, 14(6) (1978) 706–712.

    Article  Google Scholar 

  12. S. Arimoto, S. Kawamura and F. Miyazaki, Bettering operation of robots by learning, Journal of Robotic Systems, 1(2) (1984) 123–140.

    Article  Google Scholar 

  13. J. Yan and Z. Hou, Convergence of MFAC based feedback-feedforword ILC systems, Proceedings of the 27th Chinese Control Conference, Kunming, China (2008) 2–6.

  14. Y. Li, Lyapunov approach to design of iterative learning controllers, Dissertation, Zhejiang University of Technology (2017) (in Chinese).

  15. Q. Yu and Z. Hou, Adaptive fuzzy iterative learning control for high-speed trains with both randomly varying operation lengths and system constraints, IEEE Transactions on Fuzzy Systems, 29(8) (2021) 2408–2418.

    Article  Google Scholar 

  16. G. G. Maidment and J. F. Missenden, Evaluation of an underground railway carriage operating with a sustainable ground-water cooling system, International Journal of Refrigeration, 22(5) (2002) 569–574.

    Article  Google Scholar 

  17. F. Ampofo, G. Maidment and J. Missenden, Underground railway environment in the UK part 2: investigation of heat load, Applied Thermal Engineering, 24(5–6) (2004) 633–645.

    Article  Google Scholar 

  18. W. Yu, Analysis of high speed train air-conditioning system energy consumption calculation and prediction, Dissertation, Southwest Jiaotong University (2014) (in Chinese).

  19. TB 1951–87, Design Parameters for Air-conditioning Units of Passenger Trains, China Ministry of Railways, Beijing, China (1988).

    Google Scholar 

  20. B. Zhang, Railway Vehicle Refrigeration and Air Conditioning, China Railway Publishing House, Beijing, China (2017).

    Google Scholar 

  21. C. Chen, Z. He, Y. Feng and L. Yang, Semi-empirical model of internal pressure for a high-speed train under the excitation of tunnel pressure waves, Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 236(7) (2022) 803–815.

    Article  Google Scholar 

  22. Q. Zhang, Standard Meteorological Database for Chinese Architecture, China Machine Press, Beijing, China (2004).

    Google Scholar 

  23. R. Zhao, Investigation of transient thermal environments, Building and Environment, 42(12) (2007) 3926–3932.

    Article  Google Scholar 

  24. P. Chen, L. Ruey, Y. Shih and T. Yu, Effects of temperature steps on human skin physiology and thermal sensation response, Building and Environment, 46(11) (2011) 2387–1323.

    Article  Google Scholar 

  25. GB/T 33193.2-2016, Air Conditioning for Main Line Rolling Stock-Part 2: Type Tests, Standardization Administration of the People’s Republic of China, Beijing, China (2016).

    Google Scholar 

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Acknowledgments

This work is supported by the National Natural Science Foundation of China under grant nos. 51975487 and 52372402.

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, China

    Chunjun Chen, Qin Zheng & Lu Yang

  2. Technology and Equipment of Rail Transit Operation and Maintenance Key Laboratory of Sichuan Province, Chengdu, 610031, China

    Chunjun Chen

Authors
  1. Chunjun Chen
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  2. Qin Zheng
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  3. Lu Yang
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Corresponding author

Correspondence to Chunjun Chen.

Additional information

Chunjun Chen received his Ph.D. from Southwest Jiaotong University in 2006 and his M.A. from University of Electronic Science and Technology of China in 1993. He is a Professor at the School of Mechanical Engineering, Southwest Jiaotong University; Director of Department of Measurement and Control and Mechano-electronic Measurement and Control Laboratorial Center; and Deputy Director of the Technology and Equipment of Rail Transit Operation and Maintenance Key Laboratory of Sichuan Province. His research interests include vibration, noise and aerodynamics of high-speed trains, traffic equipment, electromechanical systems, advanced control and measurement theory, and electromechanical control and measurement system.

Qin Zheng is currently a master’s candidate at the School of Mechanical Engineering at Southwest Jiaotong University. Her research interests are intelligent sensing, diagnosis, and control of rail transportation operation and maintenance equipment.

Lu Yang received his M.S. in vehicle engineering from Southwest Jiaotong University, Chengdu, China, in 2021. He is currently pursuing his Ph.D. at Southwest Jiaotong University, Chengdu, China. His research interests include performance testing, diagnosis, and control of rail transit equipment.

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Chen, C., Zheng, Q. & Yang, L. Adaptive iterative learning control of internal temperature for high-speed trains. J Mech Sci Technol 38, 463–473 (2024). https://doi.org/10.1007/s12206-023-1238-3

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  • DOI: https://doi.org/10.1007/s12206-023-1238-3

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