Self-Adapting Intelligent Battery Thermal Management System via Artificial Neural Network Based Model Predictive Control

Abstract This paper develops a self-adaptive control strategy for a newly-proposed J-type air-based battery thermal management system (BTMS) for electric vehicles (EVs). The structure of the J-type BTMS is first optimized through surrogate-based optimization in conjunction with computational fluid dynamics (CFD) simulations, with the aim of minimizing temperature rise and maximizing temperature uniformity. Based on the optimized J-type BTMS, an artificial neural network (ANN)-based model predictive control (MPC) strategy is set up to perform real-time control of mass flow rate and BTMS mode switch among J-, Z-, and U-mode. The ANN-based MCP strategy is tested with the Urban Dynamometer Driving Schedule (UDDS) driving cycle. With a genetic algorithm optimizer, the control system is able to optimize the mass flow rate by considering several steps ahead. The results show that the ANN-based MPC strategy is able to constrain the battery temperature difference within a narrow range, and to satisfy light-duty daily operations like the UDDS driving cycle for EVs.

Volume 2: Dynamic Modeling and Diagnostics in Biomedical Systems; Dynamics and Control of Wind Energy Systems; Vehicle Energy Management Optimization; Energy Storage, Optimization; Transportation and Grid Applications; Estimation and Identification Methods, Tracking, Detection, Alternative Propulsion Systems; Ground and Space Vehicle Dynamics; Intelligent Transportation Systems and Control; Energy Harvesting; Modeling and Control for Thermo-Fluid Applications, IC Engines, Manufacturing ◽

Cooling Air Temperature and Mass Flow Rate Control for Hybrid Electric Vehicle Battery Thermal Management

10.1115/dscc2014-6001 ◽

2014 ◽

Cited By ~ 7

Author(s):

Xinran (William) Tao ◽

John Wagner

Keyword(s):

Mass Flow Rate ◽

Thermal Management ◽

Air Temperature ◽

Mass Flow ◽

Flow Rate ◽

Cooling System ◽

Battery Thermal Management System ◽

Cooling Air

Lithium-Ion (Li-ion) batteries are widely used in electric and hybrid electric vehicles for energy storage. However, a Li-ion battery’s lifespan and performance is reduced if it’s overheated during operation. To maintain the battery’s temperature below established thresholds, the heat generated during charge/discharge must be removed and this requires an effective cooling system. This paper introduces a battery thermal management system (BTMS) based on a dynamic thermal-electric model of a cylindrical battery. The heat generation rate estimated by this model helps to actively control the air mass flow rate. A nonlinear back-stepping controller and a linear optimal controller are developed to identify the ideal cooling air temperature which stabilizes the battery core temperature. The simulation of two different operating scenarios and three control strategies has been conducted. Simulation results indicate that the proposed controllers can stabilize the battery core temperature with peak tracking errors smaller than 2.4°C by regulating the cooling air temperature and mass flow rate. Overall the controllers developed for the battery thermal management system show improvements in both temperature tracking and cooling system power conservation, in comparison to the classical controller. The next step in this study is to integrate these elements into a holistic cooling configuration with AC system compressor control to minimize the cooling power consumption.

Self-adapting J-type air-based battery thermal management system via model predictive control

Applied Energy ◽

10.1016/j.apenergy.2020.114640 ◽

2020 ◽

Vol 263 ◽

pp. 114640 ◽

Cited By ~ 5

Author(s):

Yuanzhi Liu ◽

Jie Zhang

Keyword(s):

Model Predictive Control ◽

Thermal Management ◽

Predictive Control ◽

Management System ◽

Nonlinear and Adaptive Model Predictive Control Methods for Battery Thermal Management System

10.4271/2021-01-0217 ◽

2021 ◽

Author(s):

Mingru Zhao ◽

Feng Ding ◽

Le Li ◽

Yujiao Cheng

Keyword(s):

Model Predictive Control ◽

Thermal Management ◽

Predictive Control ◽

Management System ◽

Control Methods ◽

Adaptive Model ◽

Battery Thermal Management System ◽

Adaptive Model Predictive Control

A Neural Network-Based Regression Study for a Hybrid Battery Thermal Management System under Fast Charging

SAE International Journal of Electrified Vehicles ◽

10.4271/14-11-02-0015 ◽

2021 ◽

Vol 11 (2) ◽

Author(s):

Siqi Chen ◽

Guangxu Zhang ◽

Dongdong Qiao ◽

Xueyuan Wang ◽

Bo Jiang ◽

...

Keyword(s):

Neural Network ◽

Thermal Management ◽

Management System ◽

Fast Charging ◽

Performance Evaluation of Battery Thermal Management System in Electric Vehicle using Induction Heater (Part 1: Parallel System)

International Journal of Air-Conditioning and Refrigeration ◽

10.1142/s2010132520500030 ◽

2020 ◽

Vol 28 (01) ◽

pp. 2050003

Author(s):

Waseem Raza ◽

Gwang Soo Ko ◽

Youn Cheol Park

Keyword(s):

Heating Rate ◽

Thermal Management ◽

Flow Rate ◽

Management System ◽

Parallel System ◽

Working Fluid ◽

Induction Heater ◽

The fast evolving Electric vehicles (EVs) have become popular due to their zero-emission, fuel economy and better technology. However, the performance and life of batteries are very sensitive to temperature, it is important to maintain the proper temperature range. The battery thermal management system (BTMS) plays an important role in the performance of EVs. In this context, this study is conducted to evaluate the thermal performance of a battery with a parallel system using an induction heater. The GT-Suite software is used for simulation and evaluation. Mixture of water and ethylene glycol 50:50 is used as a working fluid and controlled by pump and valves. The heating rate of battery was analyzed by changing the capacity of induction heater 2, 4 and 6[Formula: see text]kW and the flow rate of fluid was 2, 3, 5, 7, 10 and 27 LPM. The simulation work predicts that the battery heating rate increases with the increase in fluid flow. The study concluded that the battery heating rate is maximum with a flow rate of 27 LPM which is the highest amount of LPM, indicating that the rise in flow rate causes the increase in heating rate of the system which is also affected by induction heater capacity.