Multi-Objective Optimization of Two-Stage Thermo-Electric Cooler Using Differential Evolution

2016 ◽  
pp. 139-170
Author(s):  
Doan V. K. Khanh ◽  
Pandian Vasant ◽  
Irraivan Elamvazuthi ◽  
Vo N. Dieu
Keyword(s):  
Genetic Algorithm ◽  
Differential Evolution ◽  
Cooling Rate ◽  
Input Current ◽  
Coefficient Of Performance ◽  
Multi Objective Optimization ◽  
Two Stage ◽  
Thermo Electric ◽  
Multi Objective ◽  
Technical Issues

In this chapter, the technical issues of two-stage TEC were discussed. After that, a new method of optimizing the dimension of TECs using differential evolution to maximize the cooling rate and coefficient of performance was proposed. A input current to hot side and cold side of and the number ratio between the hot stage and cold stage are searched the optima solutions. Thermal resistance is taken into consideration. The results of optimization obtained by using differential evolution were validated by comparing with those obtained by using genetic algorithm and show better performance in terms of stability, computational efficiency, robustness. This work revealed that differential evolution more stable than genetic algorithm and the Pareto front obtained from multi-objective optimization balances the important role between cooling rate and coefficient of performance.

scholarly journals Optimization of Two-Stage Combined Thermoelectric Devices by a Three-Dimensional Multi-Physics Model and Multi-Objective Genetic Algorithm

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2832 ◽  
Author(s):  
Jing-Hui Meng ◽  
Hao-Chi Wu ◽  
Tian-Hu Wang
Keyword(s):  
Genetic Algorithm ◽  
Three Dimensional ◽  
Cooling Capacity ◽  
Coefficient Of Performance ◽  
Thermoelectric Devices ◽  
Two Stage ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Element Number ◽  
Physics Model

Due to their advantages of self-powered capability and compact size, combined thermoelectric devices, in which a thermoelectric cooler module is driven by a thermoelectric generator module, have become promising candidates for cooling applications in extreme conditions or environments where the room is confined and the power supply is sacrificed. When the device is designed as two-stage configuration for larger temperature difference, the design degree is larger than that of a single-stage counterpart. The element number allocation to each stage in the system has a significant influence on the device performance. However, this issue has not been well-solved in previous studies. This work proposes a three-dimensional multi-physics model coupled with multi-objective genetic algorithm to optimize the optimal element number allocation with the coefficient of performance and cooling capacity simultaneously as multi-objective functions. This method increases the accuracy of performance prediction compared with the previously reported examples studied by the thermal resistance model. The results show that the performance of the optimized device is remarkably enhanced, where the cooling capacity is increased by 23.3% and the coefficient of performance increased by 122.0% compared with the 1# Initial Solution. The mechanism behind this enhanced performance is analyzed. The results in this paper should be beneficial for engineers and scientists seeking to design a combined thermoelectric device with optimal performance under the constraint of total element number.

Multi-objective optimization of all-wheel drive electric formula vehicle for performance and energy efficiency using evolutionary algorithms

2019 ◽  
Vol 234 (5) ◽  
pp. 1472-1479 ◽  
Author(s):  
Tey Jing Yuen ◽  
Rahizar Ramli
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Evolutionary Algorithm ◽  
Time Frame ◽  
Motor Power ◽  
Multi Objective Optimization ◽  
Pareto Solution ◽  
Multi Objective

A new method based on constraint multi-objective optimization using evolutionary algorithms is proposed to optimize the powertrain design of a battery electric formula vehicle with an all-wheel independent motor drive. The electric formula vehicle has a maximum combined motor power of 80 kW, which is a constraint for delivering maximum vehicle performance with minimal energy consumption. The performance of the vehicle will be simulated and measured against different driving events, that is, acceleration event, autocross event, and endurance event. Each event demands a different aspect of performance to be delivered by the motor. The respective event lap time or energy rating will be measured for performance assessment. In this study, a non-dominated sorting genetic algorithm II and constrained multi-objective evolutionary algorithm based on decomposition by using differential evolution are employed to optimize the motor transmission ratio, motor torque scaling, and downforce scale of both front and rear wheels against the acceleration event to minimize energy consumption and event lap time while constraining the combined motor power of all wheels to not exceed 80 kW. The optimization will be performed through software-in-the-loop between MATLAB and VI-Grade, where the high-fidelity vehicle will be modeled in VI-Grade and optimization algorithms will be implemented on the host in MATLAB. Results show that the non-dominated sorting genetic algorithm II outperforms the constrained multi-objective evolutionary algorithm based on decomposition by using differential evolution in obtaining a wider distributed Pareto solution and converges at a relatively shorter time frame. The optimized results show a promising increase in the performance of the electric formula vehicle in completing those events with the highest combined performance scoring, that is, the lap time of acceleration events improves by 9.18%, that of autocross event improves by 6.1%, and that of endurance event improves by 4.97%, with minimum decrease in energy rating of 32.54%.

NARMAX Model Identification Using Multi-Objective Optimization Differential Evolution

2018 ◽  
Vol 10 (7) ◽  
Author(s):  
Mohd Zakimi Zakaria ◽  
◽  
Zakwan Mansor ◽  
Azuwir Mohd Nor ◽  
Mohd Sazli Saad ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Model Identification ◽  
Multi Objective Optimization ◽  
Multi Objective

MULTI-OBJECTIVE OPTIMIZATION OF OFFSHORE TSUNAMI OBSERVATION POINTS BY GENETIC ALGORITHM

2020 ◽  
Vol 76 (2) ◽  
pp. I_325-I_330
Author(s):  
Kazutoshi KURAMOTO ◽  
Fumiyasu MAKINOSHIMA ◽  
Anawat SUPPASRI ◽  
Fumihiko IMAMURA
Keyword(s):  
Genetic Algorithm ◽  
Multi Objective Optimization ◽  
Multi Objective

PERTURBATION PARAMETERS TUNING OF MULTI-OBJECTIVE OPTIMIZATION DIFFERENTIAL EVOLUTION AND ITS APPLICATION TO DYNAMIC SYSTEM MODELING

2015 ◽  
Vol 75 (11) ◽  
Author(s):  
Mohd Zakimi Zakaria ◽  
Hishamuddin Jamaluddin ◽  
Robiah Ahmad ◽  
Azmi Harun ◽  
Radhwan Hussin ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Dynamic System ◽  
Dynamic Systems ◽  
System Modeling ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Structure Selection ◽  
Dynamic System Modeling ◽  
Perturbation Parameters ◽  
Model Structure Selection

This paper presents perturbation parameters for tuning of multi-objective optimization differential evolution and its application to dynamic system modeling. The perturbation of the proposed algorithm was composed of crossover and mutation operators.  Initially, a set of parameter values was tuned vigorously by executing multiple runs of algorithm for each proposed parameter variation. A set of values for crossover and mutation rates were proposed in executing the algorithm for model structure selection in dynamic system modeling. The model structure selection was one of the procedures in the system identification technique. Most researchers focused on the problem in selecting the parsimony model as the best represented the dynamic systems. Therefore, this problem needed two objective functions to overcome it, i.e. minimum predictive error and model complexity.  One of the main problems in identification of dynamic systems is to select the minimal model from the huge possible models that need to be considered. Hence, the important concepts in selecting good and adequate model used in the proposed algorithm were elaborated, including the implementation of the algorithm for modeling dynamic systems. Besides, the results showed that multi-objective optimization differential evolution performed better with tuned perturbation parameters.

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