The air conditioning (AC) systems of electric vehicle usually consumes 30% of total energy, resulting in a decrease in driving range. An AC-cabin intelligent control strategy based on dynamic thermal modeling and reinforcement learning (RL) is proposed. The thermal model is validated by road test cycle (RTC) experimental results. The intelligent control strategy is trying to find the mathematical relationship between the environmental states and the temperature of the passenger compartment and the dynamic responses of different control methods are compared. The results show that the dynamic response of the compressor speed to environmental changes is better and the temperature fluctuation is smaller under the RL-based control strategy. Compared with the Logic Threshold (LT) control strategy, the RL control strategy saved 6.7%, 9.6%, and 8.6% of compressor energy consumption, respectively, under three operating conditions (NEDC, WLTC, and RTC), while the PID control strategy saved 4.6%, 5.3%, and 4.5%, respectively.
HeLJingHZhangY, et al. Review of thermal management system for battery electric vehicle. J Energy Storage2023; 59: 106443.
2.
ZhengSZhuXXiangZ, et al. Technology trends, challenges, and opportunities of reduced-rare-earth PM motor for modern electric vehicles. Green Energy Intell Transp2022; 1(1): 100012.
3.
HeHSunFWangZ, et al. China’s battery electric vehicles lead the world: achievements in technology system architecture and technological breakthroughs. Green Energy Intell Transp2022; 1(1): 100020.
4.
RughJFarringtonR. Vehicle ancillary load reduction project close-out report: An overview of the task and a compilation of the research results. U. S. Dept. of National Renewable Energy Laboratory, Technical Report NREL/TP-540-42454. 2008.
5.
FarringtonRRughJ. Impact of vehicle air-conditioning on fuel economy, tailpipe emissions, and electric vehicle range: preprint. Electric Vehicles, 2000.
6.
HanBLiuFLiM, et al. Research on electric vehicle thermal management system with coupled temperature regulation between crew cabin and power battery pack. Proc IMechE, Part D: J Automobile Engineering2021; 235(10–11): 2740–2752.
7.
JiWCaiLMengQ, et al. Research on the drive system and logic threshold control strategy of the hybrid power gas engine heat pump. Build Simul2016; 9: 165–173.
8.
Al-DhaifallahM.Fuzzy fractional-order PID control for heat exchanger. Alex Eng J2023; 63: 11–16.
9.
ShojaeiK.A prescribed performance PID control of robotic cars with only posture measurements considering path curvature. Eur J Control2022; 65: 100616.
10.
MaDSongMYuP, et al. Research of RBF-PID control in maglev system. Symmetry2020; 12(11): 1780.
KhayyamHNahavandiSHuE, et al. Intelligent energy management control of vehicle air conditioning via look-ahead system. Appl Therm Eng2011; 31(16): 3147–3160.
13.
KhayyamH.Adaptive intelligent control of vehicle air conditioning system. Appl Therm Eng2013; 51(1–2): 1154–1161.
14.
NgBCDarusIZMJamaluddinH, et al. Application of adaptive neural predictive control for an automotive air conditioning system. Appl Therm Eng2014; 73(1): 1244–1254.
15.
YanMHeHJiaH, et al. Model predictive control of the air-conditioning system for electric bus. Energy Procedia2017; 105: 2415–2421.
16.
HeHYanMSunC, et al. Predictive air-conditioner control for electric buses with passenger amount variation forecast☆. Appl Energy2018; 227: 249–261.
17.
HuangYKhajepourABagheriF, et al. Optimal energy-efficient predictive controllers in automotive air-conditioning/refrigeration systems. Appl Energy2016; 184: 605–618.
18.
LimTHShinYKimS, et al. Predictive control of car refrigeration cycle with an electric compressor. Appl Therm Eng2017; 127: 1223–1232.
19.
HuangYWangHKhajepourA, et al. Model predictive control power management strategies for HEVs: a review. J Power Sources2017; 341: 91–106.
20.
SchwenzerMAyMBergsT, et al. Review on model predictive control: an engineering perspective. Int J Adv Manuf Technol2021; 117(5–6): 1327–1349.
21.
ChenHGuoGTangB, et al. Data-driven transferred energy management strategy for hybrid electric vehicles via deep reinforcement learning. Energy Rep2023; 10: 2680–2692.
22.
WangKYangRHuangW, et al. Deep reinforcement learning-based energy management strategies for energy-efficient driving of hybrid electric buses. Proc IMechE, Part D: J Automobile Engineering2023; 237(8): 1790–1804.
23.
YuanXPanYYangJ, et al. Study on the application of reinforcement learning in the operation optimization of HVAC system. Build Simul2021; 14: 75–87.
24.
XieYLiuZLiK, et al. An improved intelligent model predictive controller for cooling system of electric vehicle. Appl Therm Eng2021; 182: 116084.
25.
MaJSunYZhangS, et al. Experimental study on the performance of vehicle integrated thermal management system for pure electric vehicles. Energy Convers Manag2022; 253: 115183.
26.
JadhavSHSarwadnyaRV.Modeling of pine sleek PID control system in AC heat exchanger. J Ambient Intell Humaniz Comput2023; 14(12): 16159–16171.
27.
DongRDuJLiuY, et al. An enhanced deep deterministic policy gradient algorithm for intelligent control of robotic arms. Front Neuroinform2023; 17: 1096053.
28.
HuDZhangY.Deep reinforcement learning based on driver experience embedding for energy management strategies in hybrid electric vehicles. Energy Technol2022; 10(6): 2200123.
