CO2 heat pump air conditioning (HPAC) systems for electric vehicles (EVs) have received widespread attention for their excellent low-temperature heating capabilities. However, the range of EVs is limited by the battery energy storage, which makes the energy demand of the heating system affect the energy use efficiency of the drive battery. In order to measure the thermal economy of the air conditioning (AC) system in terms of heating, the index of coefficient of performance (COP) is often used. Accurate COP prediction can help optimize the performance of heat HPAC systems for EVs to avoid energy wastage and thus improve the range of the vehicle. In this study, we use a backpropagation (BP) neural network combined with the particle swarm optimization (PSO) algorithm to predict and optimize the COP of the CO2 HPAC system for EVs. First, a COP prediction model of the CO2 HPAC system for EVs was established, which can consider a variety of influencing factors, and the key parameters affecting the COP of the AC system were obtained through experiments. Second, a BP neural network is used to predict the COP of the CO2 HPAC system, and in order to overcome the shortcomings of the BP neural network, which is slow and prone to fall into the minimum value, the particle swarm algorithm PSO is introduced to optimize the weights and biases of the BP neural network, so as to improve the accuracy and stability of the prediction. Through this study, we combine the BP neural network with the PSO algorithm to achieve accurate prediction and optimization of the COP of the HPAC system of an EV, which provides a strong support for the improvement of energy use efficiency. Second, we considered a variety of influencing factors, such as outdoor temperature, compressor speed, and EV status, which made the prediction model more accurate and applicable. Finally, the method proposed in this study is validated on a real dataset, and the optimization of the BP neural network using the particle swarm algorithm PSO can improve the accuracy of COP prediction for HPAC systems by 65.8%.

1.
L.
Jiangfeng
,
L.
Shuaiqi
,
R.
Xianzhen
,
X.
Lei
,
Z.
Xiaochun
,
S.
Wenji
, and
F.
Ziping
, “
Overview of CO2 heat pump air conditioning and vehicle thermal management for pure electric vehicles
,”
Energy Storage Sci. Technol.
09
,
2959
2970
(
2022
).
2.
L.
Cecchinato
,
M.
Corradi
,
E.
Fornasieri
, and
L.
Zamboni
, “
Carbon dioxide as refrigerant for tap water heat pumps: A comparison with the traditional solution
,”
Int. J. Refrig.
28
(
8
),
1250
1258
(
2005
).
3.
M.
Khanam
and
T. U.
Daim
, “
A regional technology roadmap to enable the adoption of CO2 heat pump water heater: A case from the Pacific Northwest, USA
,”
Energy Strategy Rev.
18
,
157
174
(
2017
).
4.
Y.
Wang
,
T.
Xue
,
K. K.
Tamma
,
D.
Maxam
, and
G.
Qin
, “
A three-time-level a posteriori error estimator for GS4-2 framework: Adaptive time stepping for second-order transient systems
,”
Comput. Methods Appl. Mech. Eng.
384
(
13
),
113920
(
2021
).
5.
R. A.
Sian
and
C.-C.
Wang
, “
Comparative study for CO2 and R-134a heat pump tumble dryer—A rational approach
,”
Int. J. Refrig.
106
,
474
491
(
2019
).
6.
P.
Gullo
,
B.
Elmegaard
, and
G.
Cortella
, “
Energy and environmental performance assessment of R744 booster supermarket refrigeration systems operating in warm climates
,”
Int. J. Refrig.
64
,
61
79
(
2016
).
7.
Y. B.
Tao
,
Y. L.
He
,
W. Q.
Tao
, and
Z. G.
Wu
, “
Experimental study on the performance of CO2 residential air-conditioning system with an internal heat exchanger
,”
Energy Convers. Manage.
51
(
1
),
64
70
(
2010
).
8.
J.
Fang
,
X.
Yin
,
A.
Wang
,
X.
Sun
,
Y.
Liu
, and
F.
Cao
, “
Cooling performance enhancement for the automobile transcritical CO2 air conditioning system with various internal heat exchanger effectiveness
,”
Appl. Therm. Eng.
196
,
117274
(
2021
).
9.
H.
Li
,
Z.
Zhang
,
X.
Song
,
J.
Chen
,
H.
Li
,
Z.
Zhang
,
X.
Song
, and
J.
Chen
, “
Experimental study of a trans-critical CO2 mobile air conditioning system with an ejector
,”
J. Shanghai Jiao Tong Univ.
55
,
179
187
(
2021
).
10.
T.
Yang
,
H.
Zou
,
M.
Tang
,
C.
Tian
, and
Y.
Yan
, “
Experimental performance of a vapor-injection CO2 heat pump system for electric vehicles in −30 °C to 50 °C range
,”
Appl. Therm. Eng.
217
,
119149
(
2022
).
11.
D.
Wang
,
Y.
Wang
,
B.
Yu
,
J.
Shi
, and
J.
Chen
, “
Numerical study on heat transfer performance of micro-channel gas coolers for automobile CO2 heat pump systems
,”
Int. J. Refrig.
106
,
639
649
(
2019
).
12.
Q. H.
Zhao
,
J.
Lv
, and
K.
Cao
, “
Simulation study on the influence of fin structure of CO2 microchannel air cooler on heat exchange performance
,”
Therm. Power Eng.
S1
,
28
35, 127
(
2017
).
13.
N.
Li
, “
Comparison of the characteristics of the control strategies based on artificial neural network and genetic algorithm for air conditioning systems
,”
J. Build. Eng.
66
,
105830
(
2023
).
14.
J.
Navarro-Esbrí
,
V.
Berbegall
,
G.
Verdu
,
R.
Cabello
, and
R.
Llopis
, “
A low data requirement model of a variable-speed vapour compression refrigeration system based on neural networks
,”
Int. J. Refrig.
30
(
8
),
1452
1459
(
2007
).
15.
Y.
Zhao
,
W.
Li
,
J.
Zhang
,
C.
Jiang
, and
S.
Chen
, “
Real-time energy consumption prediction method for air-conditioning system based on long short-term memory neural network
,”
Energy Build.
298
,
113527
(
2023
).
16.
L.
Zhao
,
W.-J.
Cai
, and
Z.-H.
Man
, “
Neural modeling of vapor compression refrigeration cycle with extreme learning machine
,”
Neurocomputing
128
,
242
248
(
2014
).
17.
Z.
Tian
,
C.
Qian
,
B.
Gu
,
L.
Yang
, and
F.
Liu
, “
Electric vehicle air conditioning system performance prediction based on artificial neural network
,”
Appl. Therm. Eng.
89
,
101
114
(
2015
).
18.
P.
Develope
,
W.
Mingshan
, and
H.
Haisheng
, “
Environmental temperature affects the performance of the electric car heat pump air conditioning system
,”
J. Beijing Univ. Aeronaut. Astronaut.
40
(
12
),
1741
1746
(
2014
).
19.
Y.
Xu
,
C.
Mao
,
Y.
Huang
,
X.
Shen
,
X.
Xu
, and
G.
Chen
, “
Performance evaluation and multi-objective optimization of a low-temperature CO2 heat pump water heater based on artificial neural network and new economic analysis
,”
Energy
216
,
119232
(
2021
).
20.
J.
Wang
,
B.
Qin
,
Y.
Liu
, and
Y.
Yang
, “
Thermal error prediction of numerical control machine based on improved particle swarm optimized back propagation neural network
,” in
11th International Conference on Natural Computation (ICNC)
(
IEEE
,
2015
), pp.
820
824
.
21.
S.
Phommixay
,
M. L.
Doumbia
, and
D.
Lupien St-Pierre
, “
Review on the cost optimization of microgrids via particle swarm optimization
,”
Int. J. Energy Environ. Eng.
11
(
1
),
73
89
(
2020
).
You do not currently have access to this content.