Multi-Objective Design Optimization of an Electric Motor Thermal Management System for Autonomous Vehicles

2021 ◽  
Author(s):  
Shervin Shoai Naini ◽  
Richard Miller ◽  
Denise Rizzo ◽  
JOHN Wagner
Keyword(s):  
Design Optimization ◽  
Thermal Management ◽  
Electric Motor ◽  
Autonomous Vehicles ◽  
Management System ◽  
Multi Objective ◽  
Thermal Management System

An electric motor thermal management system for hybrid vehicles: modelling and control

2016 ◽  
Vol 2 (3) ◽  
pp. 207 ◽  
Author(s):  
Xinran ( ◽  
N.A. William) ◽  
N.A. Tao ◽  
Kan Zhou ◽  
John R. Wagner ◽  
...  
Keyword(s):  
Thermal Management ◽  
Electric Motor ◽  
Management System ◽  
Hybrid Vehicles ◽  
Thermal Management System ◽  
And Control

Multi objective optimization of structural parameters of air-cooled system for lithium battery pack based on surrogate model

2021 ◽  
pp. 1-24
Author(s):  
Nengsheng Bao ◽  
Wei Li ◽  
Ma Chong ◽  
Fan Yuchen ◽  
Li Tuyan
Keyword(s):  
Electric Vehicle ◽  
Thermal Management ◽  
Management System ◽  
Lithium Battery ◽  
Optimization Method ◽  
Battery Pack ◽  
Maximum Temperature ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Thermal Management System

Abstract The new energy electric vehicle, which takes clean electric energy as the main driving force, has no pollutants and exhaust emissions during its operation. And has a higher energy utilization ratio than the fuel locomotive. Therefore, electric vehicles have been widely developed in recent years. The maximum temperature and temperature consistency of battery pack in electric vehicle have great influence on the life and safety of battery. In this paper, the thermal management system of lithium battery pack was taken as the research object. The temperature distribution and uniformity of battery pack under different heat dissipation conditions were analyzed based on computational fluid dynamics (CFD). The multi-objective optimization method of battery pack thermal management system was carried out by combining sur-rogate model with fast non-dominated sorting genetic algorithm (NSGA-II). The maximum temperature of the battery pack obtained from candidate point 1 is 310.72K, which is 4.99K lower than the initial model temperature, and the temperature standard deviation is 0.76K, with a reduction rate of 51.9%. Experiment results showed that maximum difference between the optimized and experimental value of the maximum temperature is 0.8K, and the error was within 1K. Therefore, the multi-objective optimization method proposed in this paper has high accuracy.

Surrogate Based Multi-Objective Optimization of J-Type Battery Thermal Management System

2018 ◽  
Author(s):  
Yuanzhi Liu ◽  
Payam Ghassemi ◽  
Souma Chowdhury ◽  
Jie Zhang
Keyword(s):  
Energy Efficiency ◽  
Thermal Management ◽  
Management System ◽  
Type Systems ◽  
Maximum Temperature ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Battery Thermal Management ◽  
Thermal Management System ◽  
Battery Thermal Management System

This paper proposes a novel and flexible J-type air-based battery thermal management system (BTMS), by integrating conventional Z-type and U-type BTMS. With two controlling valves, the J-type BTMS can be adaptively controlled in real time to help balance the temperature uniformity and energy efficiency under various charging/discharging situations (especially extreme fast changing). Results of computational fluid dynamics simulations show that the J-type system performs better than the U-type and Z-type systems. To further improve the thermal performance of the proposed J-type BTMS, a surrogate-based multi-objective optimization is performed, with the consideration of the two major objectives, i.e., uniformity and energy efficiency. The concurrent surrogate selection (COSMOS) framework is adopted in this paper to determine the most suitable surrogate models. Optimization results show that: (i) the uniformity of the temperature distribution is improved by 38.6% compared to the benchmark, (ii) the maximum temperature is reduced by 19.1%, and (iii) the pressure drop is decreased by 14.5%.

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