Intelligent control system for the electric vehicle heat pump air conditioner based on machine learning

Abstract

In order to break through the existing battery technology of electric vehicles, this paper proposes to use heat pump air conditioning instead of the original PTC heating system potential. First, the advantages and disadvantages of different heat pump models for new energy vehicles are analyzed and compared. Second, a fuzzy inference system is constructed based on the machine learning model to observe the temperature of the passenger compartment using the temperature sensor inside the tram and to determine the need for the air conditioning system to be turned on in the heating/cooling mode by comparing it with the set temperature. Finally, the results show that the machine learning algorithm is able to monitor and adaptively adjust the interior temperature to further enhance the adaptability of the system with low volatility and high accuracy. The proposed research study can lay the foundation for further optimizing the design of heat pump air conditioners for electric vehicles.

Energy efficient smart home heating system using renewable energy source with fuzzy control design

“…Comparisons were made between the conventional 45 ℃ supplied water temperature, and the simulation results showed that the system energy consumption saved by 11.3% and unsatisfied time saved by 9.0% in the whole heating season. The potential use of heat pump air conditioning instead of the original PTC heating system is proposed by Miao (2023). First, the advantages and disadvantages of different heat pump models for new energy vehicles are analyzed and compared.…”

PID-Based Design of Automatic Control System for a Travel Speed of the 4UM-120D Electric Leafy Vegetable Harvester

“…Xincheng Li et al [12] employed a photoelectric encoder to measure the travel speed of a leafy vegetable harvester in the field of automatic control of harvester travel speed and created a speed detection and control system. The benefits and drawbacks of PID and NQL-PID control strategies were discussed by Peng Miao et al, who also created a model of the leafy vegetable harvester travel drive system based on the NQL-PID control algorithm [13]. When it comes to the automatic adjustment of the cutting height in harvesters, Zhang Jian et al created a system that uses an ultrasonic sensor to determine the height of the cutting device and a DC motor and electro-hydraulic cylinder to adjust the cutter height in real-time so that the harvester's cutting height is kept within the predetermined range [14].…”