A Thompson Sampling Efficient Multi-Objective Optimization Algorithm (TSEMO) for Lithium-Ion Battery Liquid-Cooled Thermal Management System: Study of Hydrodynamic, Thermodynamic, and Structural Performance

Abstract The efficient design of battery thermal management systems (BTMSs) plays an important role in enhancing the performance, life, and safety of electric vehicles (EVs). This paper aims at designing and optimizing cold plate-based liquid cooling BTMS. Pitch sizes of channels, inlet velocity, and inlet temperature of the outermost channel are considered as design parameters. Evaluating the influence and optimization of design parameters by repeated computational fluid dynamics calculations is time consuming. To tackle this, the effect of design parameters is studied by using surrogate modeling. Optimized design variables should ensure a perfect balance between certain conflicting goals, namely, cooling efficiency, BTMS power consumption (parasitic power), and size of the battery. Therefore, the optimization problem is decoupled into hydrodynamic performance, thermodynamic performance, and mechanical structure performance. The optimal design involving multiple conflicting objectives in BTMS is solved by adopting the Thompson sampling efficient multi-objective optimization algorithm. The results obtained are as follows. The optimized average battery temperature after optimization decreased from 319.86 K to 319.2759 K by 0.18%. The standard deviation of battery temperature decreased from 5.3347 K to 5.2618 K by 1.37%. The system pressure drop decreased from 7.3211 Pa to 3.3838 Pa by 53.78%. The performance of the optimized battery cooling system has been significantly improved.

Download Full-text

Multi-Objective Optimization of Piezoelectric Actuator Placement for Shape Control of Plates Using Genetic Algorithms

Journal of Mechanical Design ◽

10.1115/1.3160313 ◽

2009 ◽

Vol 131 (9) ◽

Cited By ~ 12

Author(s):

Rajesh Kudikala ◽

Deb Kalyanmoy ◽

Bishakh Bhattacharya

Keyword(s):

Optimization Algorithm ◽

Shape Control ◽

Optimization Problem ◽

Piezoelectric Actuators ◽

Multi Objective Optimization ◽

Multi Objective ◽

Input Control ◽

Wide Range ◽

Conflicting Objectives ◽

Lag Model

Shape control of adaptive structures using piezoelectric actuators has found a wide range of applications in recent years. In this paper, the problem of finding optimal distribution of piezoelectric actuators and corresponding actuation voltages for static shape control of a plate is formulated as a multi-objective optimization problem. The two conflicting objectives considered are minimization of input control energy and minimization of mean square deviation between the desired and actuated shapes with constraints on the maximum number of actuators and maximum induced stresses. A shear lag model of the smart plate structure is created, and the optimization problem is solved using an evolutionary multi-objective optimization algorithm: nondominated sorting genetic algorithm-II. Pareto-optimal solutions are obtained for different case studies. Further, the obtained solutions are verified by comparing them with the single-objective optimization solutions. Attainment surface based performance evaluation of the proposed optimization algorithm has been carried out.

Download Full-text

Switching harmonic suppression design based on multi-objective optimization algorithm with constraint processing

COMPEL The International Journal for Computation and Mathematics in Electrical and Electronic Engineering ◽

10.1108/compel-12-2019-0489 ◽

2020 ◽

Vol 39 (4) ◽

pp. 899-913

Author(s):

Songtao Huang ◽

Jie Ye ◽

Haozhe Wang ◽

Baojin Li ◽

Anwen Shen ◽

...

Keyword(s):

Optimization Algorithm ◽

Filter Design ◽

Design Parameters ◽

Harmonic Suppression ◽

Multi Objective Optimization ◽

Nsga Ii ◽

Filter Parameter ◽

Content Type ◽

Domain Experts ◽

Multi Objective

Purpose Traditional switching harmonic suppressor design methods require domain experts to adjust design parameters due to various complex performance requirements and practical limitations in switching ripple suppressor designs. The purpose of this paper is to present a method for filter parameter design. Design/methodology/approach An improved non-dominated sorting genetic algorithm II (NSGA II) was used in the inductor-capacitor-inductor (LCL) filter design to find the optimal design parameters, and a method was proposed to handle the constraints by transforming the them into decision variables. Findings The performance of the proposed algorithm in parameter designing was verified by simulation on MATLAB and experimental results on hardware-in-the-loop plat-form with StarSim software. The results indicate that the optimization algorithm has a better effect than the traditional expert parameters on each optimization index, especially on the switching harmonic suppression. Originality/value The paper presents an improved multi-objective optimization algorithm with ingenious constraints handing to obtain better filter parameters and reduces switching harmonics.

Download Full-text

A self-adaptive population Rao algorithm for optimization of selected bio-energy systems

Journal of Computational Design and Engineering ◽

10.1093/jcde/qwaa063 ◽

2020 ◽

Author(s):

R Venkata Rao ◽

Hameer Singh Keesari

Keyword(s):

Optimization Algorithm ◽

Optimization Problems ◽

Optimization Algorithms ◽

Energy Systems ◽

Design Parameters ◽

Perturbation Equation ◽

Multi Objective Optimization ◽

Multi Objective ◽

Fitness Value ◽

Self Adaptive

Abstract This work proposes a metaphor-less and algorithm-specific parameter-less algorithm, named as self-adaptive population Rao algorithm, for solving the single-, multi-, and many-objective optimization problems. The proposed algorithm adapts the population size based on the improvement in the fitness value during the search process. The population is randomly divided into four sub-population groups. For each sub-population, a unique perturbation equation is randomly allocated. Each perturbation equation guides the solutions toward different regions of the search space. The performance of the proposed algorithm is examined using standard optimization benchmark problems having different characteristics in the single- and multi-objective optimization scenarios. The results of the application of the proposed algorithm are compared with those obtained by the latest advanced optimization algorithms. It is observed that the results obtained by the proposed method are superior. Furthermore, the proposed algorithm is used to identify optimum design parameters through multi-objective optimization of a fertilizer-assisted microalgae cultivation process and many-objective optimization of a compression ignition biodiesel engine system. From the results of the computational tests, it is observed that the performance of the self-adaptive population Rao algorithm is superior or competitive to the other advanced optimization algorithms. The performances of the considered bio-energy systems are improved by the application of the proposed optimization algorithm. The proposed optimization algorithm is more robust and may be easily extended to solve single-, multi-, and many-objective optimization problems of different science and engineering disciplines.

Download Full-text

Simulation and Multi-Objective Optimization of the Vehicle Thermal Management System of Electric Cars

International Journal of Heat and Technology ◽

10.18280/ijht.390334 ◽

2021 ◽

Vol 39 (3) ◽

pp. 969-978

Author(s):

Fei Zhao ◽

Xiaowei Li ◽

Jiangli Hou

Keyword(s):

Thermal Management ◽

High Speed ◽

Heat Dissipation ◽

Cooling System ◽

Thermal Adaptation ◽

Multi Objective Optimization ◽

Electric Cars ◽

Multi Objective ◽

Engine Cooling ◽

Medium Speed

The research on the vehicle thermal management (VTM) system is very important for ensuring the driving reliability of electric cars, however, currently there’re few research concerned about this topic, and the existing ones mostly focus on matching and optimizing parameters to improve the management of driving kinetic energy, and the heat dissipation and cooling performance of the cars; however, there isn’t a uniform standard for evaluating these performances, and the research on closed thermal energy management and control based on the evaluation results is pending. This paper studied the simulation and multi-objective optimization of the VTM system of electric cars, and proposed accurate methods and ideas for evaluating the heat dissipation efficiency of the engine cooling system, the cooling efficiency of the air conditioning system, and the thermal management performance of the VTM of electric cars. Based on the model predictive control (MPC) algorithm of vehicle motion control, this paper constructed temperature control optimization objective functions for electric cars under various thermal adaptation working conditions such as low-speed slope climbing, medium-speed gentle slope climbing, high-speed driving, and idling; and it designed several strategies for the coordinated control of the VTM system of electric cars. At last, this paper used test results to verify the effectiveness of the proposed strategies.

Download Full-text

Optimal Microgrid Topology Design and Siting of Distributed Generation Sources Using a Multi-Objective Substrate Layer Coral Reefs Optimization Algorithm

Sustainability ◽

10.3390/su11010169 ◽

2018 ◽

Vol 11 (1) ◽

pp. 169 ◽

Cited By ~ 5

Author(s):

Silvia Jiménez-Fernández ◽

Carlos Camacho-Gómez ◽

Ricardo Mallol-Poyato ◽

Juan Fernández ◽

Javier Del Ser ◽

...

Keyword(s):

Renewable Energy ◽

Coral Reefs ◽

Optimization Algorithm ◽

Optimization Techniques ◽

Topology Design ◽

Multi Objective Optimization ◽

Multi Objective ◽

Conflicting Objectives ◽

Coral Reefs Optimization Algorithm ◽

Photovoltaic Generators

In this work, a problem of optimal placement of renewable generation and topology design for a Microgrid (MG) is tackled. The problem consists of determining the MG nodes where renewable energy generators must be optimally located and also the optimization of the MG topology design, i.e., deciding which nodes should be connected and deciding the lines’ optimal cross-sectional areas (CSA). For this purpose, a multi-objective optimization with two conflicting objectives has been used, utilizing the cost of the lines, C, higher as the lines’ CSA increases, and the MG energy losses, E, lower as the lines’ CSA increases. To characterize generators and loads connected to the nodes, on-site monitored annual energy generation and consumption profiles have been considered. Optimization has been carried out by using a novel multi-objective algorithm, the Multi-objective Substrate Layers Coral Reefs Optimization algorithm (Mo-SL-CRO). The performance of the proposed approach has been tested in a realistic simulation of a MG with 12 nodes, considering photovoltaic generators and micro-wind turbines as renewable energy generators, as well as the consumption loads from different commercial and industrial sites. We show that the proposed Mo-SL-CRO is able to solve the problem providing good solutions, better than other well-known multi-objective optimization techniques, such as NSGA-II or multi-objective Harmony Search algorithm.

Download Full-text

Multi-objective optimization algorithm based on dynamic multiple particle swarms

Journal of Computer Applications ◽

10.3724/sp.j.1087.2013.03375 ◽

2013 ◽

Vol 33 (12) ◽

pp. 3375-3379 ◽

Cited By ~ 2

Author(s):

Bin LIU ◽

Renjin ZHANG

Keyword(s):

Optimization Algorithm ◽

Multi Objective Optimization ◽

Particle Swarms ◽

Multi Objective ◽

Multiple Particle

Download Full-text

Modelling and Multi-Objective Optimization of the Sulphur Dioxide Oxidation Process

Processes ◽

10.3390/pr9061072 ◽

2021 ◽

Vol 9 (6) ◽

pp. 1072

Author(s):

Mohammad Reza Zaker ◽

Clémence Fauteux-Lefebvre ◽

Jules Thibault

Keyword(s):

Sulphuric Acid ◽

Sulphur Dioxide ◽

Variable Number ◽

Inlet Temperature ◽

Multi Objective Optimization ◽

Absorption Column ◽

Maximum Reaction Rate ◽

Multi Objective ◽

Maximum Reaction

Sulphuric acid (H2SO4) is one of the most produced chemicals in the world. The critical step of the sulphuric acid production is the oxidation of sulphur dioxide (SO2) to sulphur trioxide (SO3) which takes place in a multi catalytic bed reactor. In this study, a representative kinetic rate equation was rigorously selected to develop a mathematical model to perform the multi-objective optimization (MOO) of the reactor. The objectives of the MOO were the SO2 conversion, SO3 productivity, and catalyst weight, whereas the decisions variables were the inlet temperature and the length of each catalytic bed. MOO studies were performed for various design scenarios involving a variable number of catalytic beds and different reactor configurations. The MOO process was mainly comprised of two steps: (1) the determination of Pareto domain via the determination a large number of non-dominated solutions, and (2) the ranking of the Pareto-optimal solutions based on preferences of a decision maker. Results show that a reactor comprised of four catalytic beds with an intermediate absorption column provides higher SO2 conversion, marginally superior to four catalytic beds without an intermediate SO3 absorption column. Both scenarios are close to the ideal optimum, where the reactor temperature would be adjusted to always be at the maximum reaction rate. Results clearly highlight the compromise existing between conversion, productivity and catalyst weight.

Download Full-text