Performance analysis on liquid-cooled battery thermal management for electric vehicles based on machine learning

Tang, Xingwang; Guo, Qing; Li, Ming; Wei, Changhua; Pan, Zhuo Hua; Wang, Yongqiang

doi:10.1016/j.jpowsour.2021.229727

Cited by 103 publications

(23 citation statements)

References 32 publications

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“…prolong the cycle life and ensure the safety of the battery. [14][15][16] Thermal management of battery packs is generally divided into three ways: air cooling, liquid cooling, and phase change materials (PCMs). The cooling effect is also gradually increasing respectively.…”

Section: Introductionmentioning

confidence: 99%

Liquid cooling system optimization for a cell‐to‐pack battery module under fast charging

Sun

Chen

Shen

et al. 2022

Intl J of Energy Research

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Summary Cell‐to‐pack (CTP) structure has been proposed for electric vehicles (EVs). However, massive heat will be generated under fast charging. To address the temperature control and thermal uniformity issues of CTP module under fast charging, experiments and computational fluid dynamics (CFD) analysis are carried out for a bottom liquid cooling plate based–CTP battery module. The impact of the channel height, channel width, coolant flow rate, and coolant temperature on the temperature and temperature difference are analyzed. A liquid cooling control method of exchanging the coolant inlet and outlet is proposed to optimize the temperature uniformity. Besides, the temperature uniformity enhancement by this control method at different intervals is compared. Results indicate that the flow rate and temperature positively affect the battery temperature; the maximum temperature can be reduced by 10.93% and 15.12%, respectively, under the same operations. However, the coolant temperature increment increases the maximum temperature difference by about 41.58%. Reversing flow enhances the cooling effect of conventional unidirectional flow of the CTP battery module under fast charging, especially for the thermal uniformity, which provides guidance for the battery thermal management system (BTMS) control under fast charging.

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Section: Introductionmentioning

confidence: 99%

Liquid cooling system optimization for a cell‐to‐pack battery module under fast charging

Sun

Chen

Shen

et al. 2022

Intl J of Energy Research

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“…Using ML (machine learning) algorithms, Warey et al [15] predicted and analysed the impact of the AC (air conditioning) system on passenger thermal comfort. Tang et al [16] developed an automated calibration model for the liquid-cooled BTMS to forecast cooling capacity and power utilization using support vector regression (SVR). The PSO (particle swarm optimization) method was used to optimize the hyperparameters of the SVR model in order to generate better results.…”

Section: Introductionmentioning

confidence: 99%

Modelling and Computational Experiment to Obtain Optimized Neural Network for Battery Thermal Management Data

et al. 2021

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The focus of this work is to computationally obtain an optimized neural network (NN) model to predict battery average Nusselt number (Nuavg) data using four activations functions. The battery Nuavg is highly nonlinear as reported in the literature, which depends mainly on flow velocity, coolant type, heat generation, thermal conductivity, battery length to width ratio, and space between the parallel battery packs. Nuavg is modeled at first using only one hidden layer in the network (NN1). The neurons in NN1 are experimented from 1 to 10 with activation functions: Sigmoidal, Gaussian, Tanh, and Linear functions to get the optimized NN1. Similarly, deep NN (NND) was also analyzed with neurons and activations functions to find an optimized number of hidden layers to predict the Nuavg. RSME (root mean square error) and R-Squared (R2) is accessed to conclude the optimized NN model. From this computational experiment, it is found that NN1 and NND both accurately predict the battery data. Six neurons in the hidden layer for NN1 give the best predictions. Sigmoidal and Gaussian functions have provided the best results for the NN1 model. In NND, the optimized model is obtained at different hidden layers and neurons for each activation function. The Sigmoidal and Gaussian functions outperformed the Tanh and Linear functions in an NN1 model. The linear function, on the other hand, was unable to forecast the battery data adequately. The Gaussian and Linear functions outperformed the other two NN-operated functions in the NND model. Overall, the deep NN (NND) model predicted better than the single-layered NN (NN1) model for each activation function.

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“…Other innovative studies have focused their attention on the thermal performance achievable using a two-phase refrigerant BTMS, comparing it with an ordinary liquid cooled one [40]. The thermal performances of liquid-based BTMSs coupled with heat-pump air-conditioning systems (HPACSs) were investigated by Tang et al [41] to predict the cooling capability of the liquid system on a basis of a machine learning method. Hydrogen-based cooling systems were realized by Alzareer et al [42] to maximize the BTMS cooling efficiency in hybrid fuel cell electric vehicles (HEVs) with prismatic battery packs.…”

Section: Introductionmentioning

confidence: 99%

Lithium-Ion Battery Thermal Management Systems: A Survey and New CFD Results

et al. 2021

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The environment has gained significant importance in recent years, and companies involved in several technology fields are moving in the direction of eco-friendly solutions. One of the most discussed topics in the automotive field is lithium-ion battery packs for electric vehicles and their battery thermal management systems (BTMSs). This work aims to show the most used lithium-ion battery pack cooling methods and technologies with best working temperature ranges together with the best performances. Different cooling methods are presented and discussed, with a focus on the comparison between air-cooling systems and liquid-cooling systems. In this context, a BTMS for cylindrical cells is presented, where the cells are arranged in staggered lines embedded in a solid structure and cooled through forced convection within channels. The thermal behavior of this BTMS is simulated by employing a computational fluid dynamics (CFD) approach. The effect of the geometry of the BTMS on the cell temperature distribution is obtained. It is shown that the use of materials with additives for the solid structure enhances the performance of the system, giving lower temperatures to the cells. The system is tested with air-cooling and water-cooling, showing that the best performances are obtained with water-cooling in terms of cell packing density and lowest cell temperatures.

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Performance analysis on liquid-cooled battery thermal management for electric vehicles based on machine learning

Cited by 103 publications

References 32 publications

Liquid cooling system optimization for a cell‐to‐pack battery module under fast charging

Liquid cooling system optimization for a cell‐to‐pack battery module under fast charging

Modelling and Computational Experiment to Obtain Optimized Neural Network for Battery Thermal Management Data

Lithium-Ion Battery Thermal Management Systems: A Survey and New CFD Results

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