Simulation and Control System Optimization of Pure Electric Vehicle Air Conditioning System Based on Multi-Objective Genetic Algorithm

Power-based electric vehicle energy consumption model: Model development and validation

Lithium-ion batteries are highly sensitive to temperature, with excessive heat leading to accelerated aging, reduced cycle life, and safety risks such as thermal runaway. Traditional thermal management systems often struggle to maintain optimal battery temperature, especially under high ambient temperatures or dynamic operating conditions. This paper proposes a novel reinforcement learning (RL)-based control method for the coupled thermal management system of a power battery and air conditioning system in a Plug-in Hybrid Electric Vehicle (PHEV). The system integrates the battery cooling circuit with the air conditioning system via a heat exchanger, eliminating the need for a separate radiator. The Deep Deterministic Policy Gradient (DDPG) algorithm is employed to control the water pump speed, optimizing battery temperature in real-time. Experimental results under New European Driving Cycle (NEDC) conditions at 40°C ambient temperature demonstrate that the RL-controlled pump responds faster than the Model Predictive Control (MPC) algorithm, allowing the battery temperature to reach the target temperature of 28°C 1 min earlier with a maximum fluctuation of only 0.8°C. The RL-controlled pump consumes 4 W h more energy than the MPC-controlled pump, while the compressor energy consumption is only 1.68% higher. Additionally, the temperature fluctuations in the passenger cabin under RL control are only 0.1°C higher than under MPC, ensuring passenger comfort. This study highlights the potential of RL-based control for improving the efficiency and stability of integrated thermal management systems in electric vehicles.

Electric vehicles (EVs) have the advantage of using both regenerative braking and mechanical braking to produce the necessary braking torque. Currently, vehicles use Anti-lock Braking Systems (ABS) which provide intermittent braking torque to the vehicle and thereby prevents skidding or slipping of the vehicle by ensuring pure rolling of the wheels. Regenerative braking on the other hand generates the necessary braking torque by converting the kinetic energy of the vehicle to electrical energy via the motor(s). To coordinate both braking systems, the conventional method involves the use of crisp logic controllers that depend on lookup tables containing the data about various aspects of the vehicle such as battery state of charge (SoC), brake pedal input etc., to enable co-operative control of both systems. This paper presents a combined implementation of both braking systems in electric vehicles using separate fuzzy controllers for both ABS and regenerative braking. The control scheme is implemented such that when regenerative braking is not capable of providing enough braking torque, braking will be one by ABS. Two fuzzy logic controllers have been used, one to implement regenerative braking and the other to implement mechanical braking via ABS. If such a control algorithm is used, the driver will not have to manually adjust the level of regenerative braking in the vehicle and such an approach ensures that the braking process of an EV is smooth, safe and efficient. It also reduces the wear and tear of the brake pads thereby extending their life and reducing the cost of maintenance and by extension the total cost of ownership of the vehicle. The results demonstrate that the controllers for both ABS and regenerative braking can coordinate efficiently and reliably to produce proper and safe braking of the vehicle.

Optimization of the energy management system in hybrid electric vehicles considering cabin temperature

Enhancement of electric vehicle air-conditioning system with dual condensers

A regenerative braking system, which could restore kinetic energy and potential energy to a battery or any other forms of power during braking, is a special system for electric and hybrid electric vehicles. However, the large current produced by the generator during braking would affect battery performance and shorten battery life. A hybrid-power system is an excellent energy storage system, in which an ultra-capacitor bank and a battery pack are connected together through a DC/DC power converter. And the ultra-capacitor in this system can store instantaneous large currents quickly, which is able to protect the battery from large currents. The application advantages of the hybrid-power system are analyzed in this paper, as well as a series control strategy for regenerative braking based on it. In order to verify the feasibility of the strategy, a braking force distribution model, which contains one S-Function, between mechanical and regenerative braking is built in MATLAB/Simulink by taking into account the ultra-capacitor voltage, the motor regenerative braking ability and other factors. Simulation results suggest that the ultra-capacitor is able to restore the regenerative energy very quickly, which will improve starting and acceleration performance, extend driving range, and increase fuel economy.

Thermal-hydraulic performance and optimization of attack angle of delta winglets in plain and wavy finned-tube heat exchangers

A semi-empirical model for hermetic rolling piston compressors

In order to manage the air-conditioning thermal system, battery thermal system and motor thermal system in a unified manner, the author proposes a self-developed integrated thermal management system for electric vehicles to recover battery energy. Firstly, the problems in the development of electric vehicles and the importance of thermal management system are introduced, secondly, the self-developed thermal management system scheme and the principle of each part are analyzed, the experimental results of thermal system in enthalpy difference chamber are also introduced. The experimental results show that: Under the double evaporation system, when the compressor speed is 4500 rpm, the maximum COP is 2.46, and the maximum COP charge is 1180 g, the maximum heat transfer capacity is 4819 W (wind side heat transfer + water side heat transfer), the evaporation temperature is 5.35?C, the evaporation superheat is 9.5?C, the condensation temperature is 59.3?C, the undercooling degree is 10.4?C, the suction pressure is 280 kPa, and the exhaust pressure is 1694 kPa. In conclusion, the thermal management system has great energy saving effect, which ensures that the electric vehicle range will not be greatly attenuated under winter heating conditions, and meets the requirements of comfort.

Experimental study on combined defrosting performance of heat pump air conditioning system for pure electric vehicle in low temperature

Application of ice cold energy storage (ICES) is for reducing power consumption in air-conditioning systems. ICES systems have two full and partial operating modes. Partial operating mode of ICES system is modeled here at two demand limiting (POM-DL) and load leveling (POM-LL) conditions. After energy, exergy, and economic and environmental modeling/analysis of POM-DL and POM-LL, multi-objective (exergy and total cost) genetic algorithm (GA) optimization technique is applied to find the optimum values of objective functions and system design parameters. Then, the performance of POM-DL and POM-LL systems is compared. For our case study, the acquired results show 11.20% and 10.94% reduction in electricity consumption for POM-DL and POM-LL systems, respectively, in comparison with that for a traditional VCR (an electrical chiller). Furthermore, these systems reduce 13.58% and 12.36% electricity cost by shifting the electricity consumption of cooling system from on peak (ONP) hours to off peak (OFP) hours for POM-DL and POM-LL, respectively.

Optimization of HVAC system energy consumption in a building using artificial neural network and multi-objective genetic algorithm

The air conditioning (AC) system of electric vehicles (EVs) consumes a large part of electricity of on-board batteries and influences the continue voyage course seriously. The feasibility of sorption type AC for EVs has been verified theoretically to decrease this part of energy consumption. However, the choice of optimal working pairs based on local working conditions is not considered before, which can realize not only high efficiency but also the steady and reliable operation. Thus in this paper, different solid sorption working pairs used in sorption type AC under different temperature zones are studied. We utilized Rubotherm balance test unit to study the sorption properties of various working pairs (halide-ammonia) and selected candidate working pairs by Clapeyron equation and energy analysis. Results show that MnCl2 is the only choice for cold temperate zone (CTZ) and CaCl2 is optimal for warm temperate zone (WTZ), while the mixed double halide (MnCl2 and CaCl2) is recommended in other zones. In middle temperate zone (MTZ), the probability for performance dropping down is relatively large, thus the ratio (CaCl2:MnCl2) is recommended as 0.33-1 to take advantage of the stability property of MnCl2. While in Qinghai-Tibet plateau cold area (QTPCA, the special temperature zone in China), the ratio is chosen as 2–3 because only under the limiting condition reaction [Ca(NH3)8]VCl2 ⇔ [Ca(NH3)4]Cl2+4NH3 is invalid. Because the continued high environmental temperature will increase the probability of limiting condition, ratio of subtropical zone (STZ) is still selected as 2–3 while that of tropical Zone (TZ) is 1–2. Taking WTZ under summer condition as example, by using sorption type AC with CaCl2 as sorbent, the increment voyage course (IVC) over the practical voyage course with conventional compression AC system (PVC) ranges from 9.4% to 37.7% for different type of EVs, i.e. the continue voyage course is increased effectively. This work provides the guidance for choosing optimal working pairs for actual utilization.

With the increasingly serious environmental problems, electric vehicles are becoming more and more popular. The major factor restricting the further development of electric vehicles is the limitation of driving range. Therefore, it is of great significance to study the energy recovery technology of electric vehicles. At the same time, with the increasing maturity of computer technology, it also provides us with more research methods.This paper first introduces the principle of energy recovery system and regenerative braking control strategy of electric vehicle based on computer.Secondly, computer modeling software is used to model the control strategy of braking energy recovery of electric vehicles. Finally, simulation software is used to analyze the model.

Nanolubricant are containing nanoparticles that are mixed with the base lubricant. Nanoparticle consists of ultra-fine size of particles from 1 to 100 nm. It was widely used as additional material to enhance properties of lubricant. The thermo-physical properties of the nanolubricant enhanced and thus able to improve the performance of vapour compression refrigeration system (VCRS) and automotive air-conditioning (AAC) system. The stability condition is one of the priority in formulation of nanolubricant for new application in AAC system of hybrid electric vehicles (HEV). The main objective of this study is to investigate the stability of TiO2 nanoparticles dispersed in Polyol-Ester (POE) lubricant. The TiO2/POE nanolubricant was prepared at volume concentration of 0.01 to 0.1% using the two-step method without any surfactant. The stability investigations were conducted by using visual sedimentation observation, micrograph observation, UV-Vis Spectrophotometer measurement and zeta potential measurement. The findings by visual sedimentation observation showed the best stability condition for more than 14 days at 0.01% and 0.03% volume concentration. Meanwhile the optimum sonication time is observed at 5 hours by visual and supported by UV-Vis evaluation. Further, the concentration ratio from UV-Vis evaluation was recorded above 95% for 5 hours sonication time and more than 30 days observation. Finally, the Zeta potential for the present nanolubricant was measured with 81.1 mV and obtained within the range of very good stability condition. Hence, this can confirm a good stability condition for the present TiO2/POE nanolubricant. Further investigation is required for the properties evaluation and performance of the nanolubricant in AAC system of HEV.