基于液冷的电池热管理系统快充-冷却耦合规划方法
陈思琦 , 包能胜 , Akhil Garg , 彭雄斌 , 高亮
工程(英文) ›› 2021, Vol. 7 ›› Issue (8) : 1165 -1176.
基于液冷的电池热管理系统快充-冷却耦合规划方法
A Fast Charging–Cooling Coupled Scheduling Method for a Liquid Cooling-Based Thermal Management System for Lithium-Ion Batteries
高效的快速充电技术对电动汽车行驶里程的拓展十分重要。然而,锂离子电池在大电流充电倍率下会大量产热。为解决这一问题,急需一种高效的快速充电-冷却规划方法。此次研究针对锂离子电池组的快速充电过程,设计了一种配有微流道的基于液冷的热管理系统。基于81组实验数据,提出了一种基于神经网络的回归模型,由三个考虑以下输出的子模型构成:最高温度、温度标准差及功耗。训练后的子模型均呈现出较高的测试准确性(99.353%、97.332%和98.381%)。此回归模型用于预测一个设计方案全集的三个输出参数,此全集由不同充电阶段的充电电流倍率[0.5C、1C、1.5C、2C和2.5C(1C = 5 A)],以及不同的冷却液流量(0.0006 kg·s-1、0.0012 kg·s-1和0.0018 kg·s-1)组成。最终从预测得到的设计方案全集中筛选出一组最优过程方案,并经实验得到了验证。结果表明在功耗低于0.02 J的情况下电池组荷电状态(SOC)值经15 min充电后增长了0.5。同时最高温度和温度标准差可分别控制在33.35 ℃和0.8 ℃以内。本文所提出的方法可供电动汽车行业在实际快速充电工况下使用。此外,可以基于实验数据预测最佳快速充电-冷却计划,从而显著提高充电过程设计的效率,并控制冷却过程中的能耗。
Efficient fast-charging technology is necessary for the extension of the driving range of electric vehicles. However, lithium-ion cells generate immense heat at high-current charging rates. In order to address this problem, an efficient fast charging–cooling scheduling method is urgently needed. In this study, a liquid cooling-based thermal management system equipped with mini-channels was designed for the fast-charging process of a lithium-ion battery module. A neural network-based regression model was proposed based on 81 sets of experimental data, which consisted of three sub-models and considered three outputs: maximum temperature, temperature standard deviation, and energy consumption. Each sub-model had a desirable testing accuracy (99.353%, 97.332%, and 98.381%) after training. The regression model was employed to predict all three outputs among a full dataset, which combined different charging current rates (0.5C, 1C, 1.5C, 2C, and 2.5C (1C = 5 A)) at three different charging stages, and a range of coolant rates (0.0006, 0.0012, and 0.0018 kg•s−1). An optimal charging–cooling schedule was selected from the predicted dataset and was validated by the experiments. The results indicated that the battery module's state of charge value increased by 0.5 after 15 min, with an energy consumption lower than 0.02 J. The maximum temperature and temperature standard deviation could be controlled within 33.35 and 0.8 °C, respectively. The approach described herein can be used by the electric vehicles industry in real fast-charging conditions. Moreover, optimal fast charging–cooling schedule can be predicted based on the experimental data obtained, that in turn, can significantly improve the efficiency of the charging process design as well as control energy consumption during cooling.
锂离子电池组 / 快速充电 / 神经网络回归 / 规划 / 荷电状态 / 功耗
Lithium-ion battery module / Fast-charging / Neural network regression / Scheduling / State of charge / Energy consumption
| Cooling method | Advantages | Disadvantages | Applicable type of vehicles |
|---|---|---|---|
| Air cooling | Simple structure and low cost | Large volume cost, low cooling efficiency, and easily influenced by the environment | Bus and car |
| Phase change material | Uniform temperature distribution | Large volume and mass cost, and high cost for replacement and maintenance | Currently being tested in the laboratory |
| Liquid cooling | High cooling efficiency, uniform temperature distribution, and continuous and stable cooling performance | High mass cost for equipment and high cost for maintenance | Car, logistics car, and sports car |
| Number | Input parameters | Evaluating parameter ∆SOC | Output parameters | |||||
|---|---|---|---|---|---|---|---|---|
| I1 (A) | I2 (A) | I3 (A) | Q (mL·min-1) | Tmax (K) | TSD (K) | W (J) | ||
| 1 | 2.5 | 2.5 | 2.5 | 108 | 0.125000 | 26.1 | 0.4129 | 0.069725 |
| 2 | 7.5 | 2.5 | 2.5 | 108 | 0.208333 | 27.3 | 0.3682 | 0.063342 |
| 3 | 12.5 | 2.5 | 2.5 | 108 | 0.291667 | 28.3 | 0.5135 | 0.071086 |
| 4 | 2.5 | 7.5 | 2.5 | 108 | 0.208333 | 27.4 | 0.2596 | 0.084564 |
| 5 | 7.5 | 7.5 | 2.5 | 108 | 0.291667 | 27.6 | 0.3949 | 0.068818 |
| 6 | 12.5 | 7.5 | 2.5 | 108 | 0.375000 | 31.8 | 0.7023 | 0.084451 |
| 7 | 2.5 | 12.5 | 2.5 | 108 | 0.291667 | 29.1 | 0.5339 | 0.070259 |
| 8 | 7.5 | 12.5 | 2.5 | 108 | 0.375000 | 31.8 | 0.9332 | 0.077144 |
| 9 | 12.5 | 12.5 | 2.5 | 108 | 0.458333 | 32.3 | 0.8238 | 0.072041 |
| 10 | 2.5 | 2.5 | 7.5 | 108 | 0.208333 | 27.4 | 0.3809 | 0.069482 |
| 11 | 7.5 | 2.5 | 7.5 | 108 | 0.291667 | 28.5 | 0.4258 | 0.062953 |
| 12 | 12.5 | 2.5 | 7.5 | 108 | 0.375000 | 30.6 | 0.8403 | 0.072074 |
| 13 | 2.5 | 7.5 | 7.5 | 108 | 0.291667 | 28.1 | 0.3837 | 0.073613 |
| 14 | 7.5 | 7.5 | 7.5 | 108 | 0.375000 | 31.6 | 0.7798 | 0.072317 |
| 15 | 12.5 | 7.5 | 7.5 | 108 | 0.458333 | 33.2 | 0.9740 | 0.081875 |
| 16 | 2.5 | 12.5 | 7.5 | 108 | 0.375000 | 32.7 | 0.6894 | 0.075719 |
| 17 | 7.5 | 12.5 | 7.5 | 108 | 0.458333 | 32.4 | 0.7955 | 0.087269 |
| 18 | 12.5 | 12.5 | 7.5 | 108 | 0.541667 | 35.1 | 1.1011 | 0.073321 |
| 19 | 2.5 | 2.5 | 12.5 | 108 | 0.291667 | 30.9 | 0.5572 | 0.079315 |
| 20 | 7.5 | 2.5 | 12.5 | 108 | 0.375000 | 30.6 | 0.6335 | 0.072236 |
| 21 | 12.5 | 2.5 | 12.5 | 108 | 0.458333 | 32.3 | 0.7241 | 0.083608 |
| 22 | 2.5 | 7.5 | 12.5 | 108 | 0.375000 | 30.2 | 0.5730 | 0.069498 |
| 23 | 7.5 | 7.5 | 12.5 | 108 | 0.458333 | 32.0 | 0.7632 | 0.080449 |
| 24 | 12.5 | 7.5 | 12.5 | 108 | 0.541667 | 34.3 | 1.1734 | 0.083981 |
| 25 | 2.5 | 12.5 | 12.5 | 108 | 0.458333 | 29.4 | 0.5591 | 0.094235 |
| 26 | 7.5 | 12.5 | 12.5 | 108 | 0.541667 | 35.3 | 0.9918 | 0.074471 |
| 27 | 12.5 | 12.5 | 12.5 | 108 | 0.625000 | 38.0 | 1.4759 | 0.075411 |
| 28 | 2.5 | 2.5 | 2.5 | 36 | 0.125000 | 26.8 | 0.2462 | 0.015973 |
| 29 | 7.5 | 2.5 | 2.5 | 36 | 0.208333 | 27.2 | 0.4668 | 0.019013 |
| 30 | 12.5 | 2.5 | 2.5 | 36 | 0.291667 | 28.8 | 0.5869 | 0.020596 |
| 31 | 2.5 | 7.5 | 2.5 | 36 | 0.208333 | 26.8 | 0.6606 | 0.019694 |
| 32 | 7.5 | 7.5 | 2.5 | 36 | 0.291667 | 28.4 | 0.3939 | 0.021778 |
| 33 | 12.5 | 7.5 | 2.5 | 36 | 0.375000 | 29.2 | 0.6031 | 0.017685 |
| 34 | 2.5 | 12.5 | 2.5 | 36 | 0.291667 | 28.6 | 0.5454 | 0.023150 |
| 35 | 7.5 | 12.5 | 2.5 | 36 | 0.375000 | 31.1 | 0.7081 | 0.016902 |
| 36 | 12.5 | 12.5 | 2.5 | 36 | 0.458333 | 33.7 | 0.7630 | 0.019591 |
| 37 | 2.5 | 2.5 | 7.5 | 36 | 0.208333 | 27.1 | 0.5199 | 0.021292 |
| 38 | 7.5 | 2.5 | 7.5 | 36 | 0.291667 | 27.3 | 0.4634 | 0.018824 |
| 39 | 12.5 | 2.5 | 7.5 | 36 | 0.375000 | 31.8 | 0.5669 | 0.018619 |
| 40 | 2.5 | 7.5 | 7.5 | 36 | 0.291667 | 28.2 | 0.5400 | 0.019219 |
| 41 | 7.5 | 7.5 | 7.5 | 36 | 0.375000 | 30.3 | 0.7927 | 0.020504 |
| 42 | 12.5 | 7.5 | 7.5 | 36 | 0.458333 | 32.1 | 0.6227 | 0.020407 |
| 43 | 2.5 | 12.5 | 7.5 | 36 | 0.375000 | 30.3 | 0.4976 | 0.020957 |
| 44 | 7.5 | 12.5 | 7.5 | 36 | 0.458333 | 32.8 | 0.6300 | 0.020682 |
| 45 | 12.5 | 12.5 | 7.5 | 36 | 0.541667 | 33.8 | 0.8014 | 0.018479 |
| 46 | 2.5 | 2.5 | 12.5 | 36 | 0.291667 | 28.7 | 0.5205 | 0.020585 |
| 47 | 7.5 | 2.5 | 12.5 | 36 | 0.375000 | 31.1 | 0.5642 | 0.016524 |
| 48 | 12.5 | 2.5 | 12.5 | 36 | 0.458333 | 33.5 | 0.7250 | 0.021827 |
| 49 | 2.5 | 7.5 | 12.5 | 36 | 0.375000 | 33.7 | 1.1370 | 0.017167 |
| 50 | 7.5 | 7.5 | 12.5 | 36 | 0.458333 | 32.2 | 0.5482 | 0.018571 |
| 51 | 12.5 | 7.5 | 12.5 | 36 | 0.541667 | 34.2 | 0.8794 | 0.018630 |
| 52 | 2.5 | 12.5 | 12.5 | 36 | 0.458333 | 34.1 | 1.3452 | 0.018808 |
| 53 | 7.5 | 12.5 | 12.5 | 36 | 0.541667 | 33.6 | 0.9299 | 0.022351 |
| 54 | 12.5 | 12.5 | 12.5 | 36 | 0.625000 | 35.4 | 0.9096 | 0.023992 |
| 55 | 2.5 | 2.5 | 2.5 | 72 | 0.125000 | 26.2 | 0.3001 | 0.041008 |
| 56 | 7.5 | 2.5 | 2.5 | 72 | 0.208333 | 26.7 | 0.3742 | 0.046310 |
| 57 | 12.5 | 2.5 | 2.5 | 72 | 0.291667 | 29.0 | 0.5135 | 0.046926 |
| 58 | 2.5 | 7.5 | 2.5 | 72 | 0.208333 | 26.7 | 0.4392 | 0.049216 |
| 59 | 7.5 | 7.5 | 2.5 | 72 | 0.291667 | 28.4 | 0.4938 | 0.051646 |
| 60 | 12.5 | 7.5 | 2.5 | 72 | 0.375000 | 30.1 | 0.4827 | 0.039182 |
| 61 | 2.5 | 12.5 | 2.5 | 72 | 0.291667 | 28.4 | 0.4117 | 0.039064 |
| 62 | 7.5 | 12.5 | 2.5 | 72 | 0.375000 | 30.4 | 0.4655 | 0.043211 |
| 63 | 12.5 | 12.5 | 2.5 | 72 | 0.458333 | 32.7 | 0.7315 | 0.044064 |
| 64 | 2.5 | 2.5 | 7.5 | 72 | 0.208333 | 27.5 | 0.6321 | 0.041278 |
| 65 | 7.5 | 2.5 | 7.5 | 72 | 0.291667 | 27.6 | 0.4295 | 0.044345 |
| 66 | 12.5 | 2.5 | 7.5 | 72 | 0.375000 | 30.0 | 0.5010 | 0.048924 |
| 67 | 2.5 | 7.5 | 7.5 | 72 | 0.291667 | 27.9 | 0.4536 | 0.047855 |
| 68 | 7.5 | 7.5 | 7.5 | 72 | 0.375000 | 29.7 | 0.3648 | 0.037930 |
| 69 | 12.5 | 7.5 | 7.5 | 72 | 0.458333 | 32.7 | 0.7269 | 0.037552 |
| 70 | 2.5 | 12.5 | 7.5 | 72 | 0.375000 | 30.4 | 0.7312 | 0.044539 |
| 71 | 7.5 | 12.5 | 7.5 | 72 | 0.458333 | 31.9 | 0.7326 | 0.045144 |
| 72 | 12.5 | 12.5 | 7.5 | 72 | 0.541667 | 34.1 | 0.7250 | 0.042120 |
| 73 | 2.5 | 2.5 | 12.5 | 72 | 0.291667 | 29.0 | 0.5479 | 0.054475 |
| 74 | 7.5 | 2.5 | 12.5 | 72 | 0.375000 | 31.1 | 0.6193 | 0.047423 |
| 75 | 12.5 | 2.5 | 12.5 | 72 | 0.458333 | 34.4 | 1.1914 | 0.040241 |
| 76 | 2.5 | 7.5 | 12.5 | 72 | 0.375000 | 31.3 | 0.5785 | 0.052909 |
| 77 | 7.5 | 7.5 | 12.5 | 72 | 0.458333 | 33.3 | 0.8902 | 0.049464 |
| 78 | 12.5 | 7.5 | 12.5 | 72 | 0.541667 | 35.9 | 1.0829 | 0.049399 |
| 79 | 2.5 | 12.5 | 12.5 | 72 | 0.458333 | 33.3 | 0.7716 | 0.055804 |
| 80 | 7.5 | 12.5 | 12.5 | 72 | 0.541667 | 35.6 | 1.0460 | 0.044712 |
| 81 | 12.5 | 12.5 | 12.5 | 72 | 0.625000 | 36.9 | 1.0378 | 0.052693 |
| Model | Input parameters | Output parameter |
|---|---|---|
| NN1 | I1, I2, I3, Q | Tmax |
| NN2 | I1, I2, I3, Q | TSD |
| NN3 | I1, I2, I3, Q | W |
| Parameter | I1 (A) | I2 (A) | I3 (A) | Q (mL·min-1) | ∆SOC | Tmax (K) | TSD (K) | W (J) |
|---|---|---|---|---|---|---|---|---|
| Predicted data | 12.5 | 12.5 | 5.0 | 36 | 0.5 | 33.268 | 0.725872 | 0.019196 |
| Experimental validation | 12.5 | 12.5 | 5.0 | 36 | 0.5 | 32.800 | 0.680500 | 0.017502 |
| [1] |
Fathabadi H. A novel design including cooling media for lithium-ion batteries pack used in hybrid and electric vehicles. J Power Sources 2014;245:495–500. |
| [2] |
Jaguemont J, Boulon L, Dubé Y. A comprehensive review of lithium-ion batteries used in hybrid and electric vehicles at cold temperatures. Appl Energy 2016;164:99–114. |
| [3] |
Sun J, Li J, Zhou T. Toxicity, a serious concern of thermal runaway from commercial Li-ion battery. Nano Energy 2016;27:313–9. |
| [4] |
Wang Q, Jiang B, Li B, Yan Y. A critical review of thermal management models and solutions of lithium-ion batteries for the development of pure electric vehicles. Renew Sustain Energy Rev 2016;64:106–28. |
| [5] |
Pesaran AA. Battery thermal models for hybrid vehicle simulations. J Power Sources 2002;110(2):377–82. |
| [6] |
Dan D, Yao C, Zhang Y, Zhang H, Zeng Z, Xu X. Dynamic thermal behavior of micro heat pipe array-air cooling battery thermal management system based on thermal network model. Appl Therm Eng 2019;162:114183. |
| [7] |
Park H. A design of air flow configuration for cooling lithium ion battery in hybrid electric vehicles. J Power Sources 2013;239:30–6. |
| [8] |
Liu Z, Wang Y, Zhang J, Liu Z. Shortcut computation for the thermal management of a large air-cooled battery pack. Appl Therm Eng 2014;66(1– 2):445–52. |
| [9] |
He F, Li X, Ma L. Combined experimental and numerical study of thermal management of battery module consisting of multiple Li-ion cells. Int J Heat Mass Transfer 2014;72:622–9. |
| [10] |
Chen K, Song M, Wei W, Wang S. Structure optimization of parallel air-cooled battery thermal management system with U-type flow for cooling efficiency improvement. Energy 2018;145:603–13. |
| [11] |
Yang N, Zhang X, Li Z, Hua D. Assessment of the forced air-cooling performance for cylindrical lithium-ion battery packs: a comparative analysis between aligned and staggered cell arrangements. Appl Therm Eng 2015;80:55–65. |
| [12] |
Mahamud R, Park C. Reciprocating air flow for Li-ion battery thermal management to improve temperature uniformity. J Power Sources 2011;196 (13):5685–96. |
| [13] |
Fathabadi H. High thermal performance lithium-ion battery pack including hybrid active–passive thermal management system for using in hybrid/electric vehicles. Energy 2014;70:529–38. |
| [14] |
Sabbah R, Kizilel R, Selman JR. Active (air-cooled) vs. passive (phase change material) thermal management of high power lithium-ion packs: limitation of temperature rise and uniformity of temperature distribution. J Power Sources 2008;182(2):630–8. |
| [15] |
Wu W, Yang X, Zhang G. Experimental investigation on the thermal performance of heat pipe-assisted phase change material based battery thermal management system. Energy Convers Manage 2017;138:486–92. |
| [16] |
Zheng Y, Shi Y, Huang Y. Optimisation with adiabatic interlayers for liquiddominated cooling system on fast charging battery packs. Appl Therm Eng 2019;147:636–46. |
| [17] |
Li J, Huang J, Cao M. Properties enhancement of phase-change materials via silica and Al honeycomb panels for the thermal management of LiFeO4 batteries. Appl Therm Eng 2018;131:660–8. |
| [18] |
Park Y, Jun S, Kim S, Lee DH. Design optimization of a loop heat pipe to cool a lithium ion battery onboard a military aircraft. J Mech Sci Technol 2010;24 (2):609–18. |
| [19] |
Rao Z, Wang S, Wu M, Lin Z, Li F. Experimental investigation on thermal management of electric vehicle battery with heat pipe. Energy Convers Manage 2013;65:92–7. |
| [20] |
Wu MS, Liu KH, Wang YY, Wan CC. Heat dissipation design for lithium-ion batteries. J Power Sources 2002;109(1):160–6. |
| [21] |
Ye Y, Bernard LHS, Shi Y, Tay AAO. Numerical analyses on optimizing a heat pipe thermal management system for lithium-ion batteries during fast charging. Appl Therm Eng 2015;86:281–91. |
| [22] |
Zhao Y, Zhang K, Diao Y, inventors; Guangwei Hetong Energy Technology Beijing Co. Ltd., assignee. Heat pipe with micro-pore tubes array and making method thereof and heat exchanging system. US patent US 20110203777 A1. 2011 Aug 25. |
| [23] |
Khateeb SA, Farid MM, Selman JR, Al-Hallaj S. Design and simulation of a lithium-ion battery with a phase change material thermal management system for an electric scooter. J Power Sources 2004;128(2):292–307. |
| [24] |
Mei W, Duan Q, Zhao C, Lu W, Sun J, Wang Q. Three-dimensional layered electrochemical-thermal model for a lithium-ion pouch cell. Part II. The effect of units number on the performance under adiabatic condition during the discharge. Int J Heat Mass Transf 2020;148:119082. |
| [25] |
Jarrett A, Kim IY. Design optimization of electric vehicle battery cooling plates for thermal performance. J Power Sources 2011;196(23): 10359–68. |
| [26] |
Qian Z, Li Y, Rao Z. Thermal performance of lithium-ion battery thermal management system by using mini-channel cooling. Energy Convers Manage 2016;126:622–31. |
| [27] |
Basu S, Hariharan KS, Kolake SM. Coupled electrochemical thermal modelling of a novel Li-ion battery pack thermal management system. Appl Energy 2016;181:1–13. |
| [28] |
Huo Y, Rao Z. The numerical investigation of nanofluid based cylinder battery thermal management using lattice Boltzmann method. Int J Heat Mass Transf 2015;91:374–84. |
| [29] |
Yang XH, Tan SC, Liu J. Thermal management of Li-ion battery with liquid metal. Energy Convers Manage 2016;117:577–85. |
| [30] |
Wu F, Rao Z. The lattice Boltzmann investigation of natural convection for nanofluid based battery thermal management. Appl Therm Eng 2017;115:659–69. |
| [31] |
Chen S, Peng X, Bao N. A comprehensive analysis and optimization process for an integrated liquid cooling plate for a prismatic lithium-ion battery module. Appl Therm Eng 2019;156:324–39. |
| [32] |
Panchal S, Dincer I, Agelinchaab M. Thermal modeling and validation of temperature distributions in a prismatic lithium-ion battery at different discharge rates and varying boundary conditions. Appl Therm Eng 2016;96:190–9. |
| [33] |
Zhang T, Gao Q, Wang G. Investigation on the promotion of temperature uniformity for the designed battery pack with liquid flow in cooling process. Appl Therm Eng 2017;116:655–62. |
| [34] |
Tong S, Lacap JH, Park JW. Battery state of charge estimation using a loadclassifying neural network. J Energy Storage 2016;7:236–43. |
| [35] |
Kalogirou SA. Artificial neural networks in renewable energy systems applications: a review. Renew Sustain Energy Rev 2001;5(4): 373–401. |
| [36] |
Shen WX, Chan CC, Lo EWC, Chau KT. A new battery available capacity indicator for electric vehicles using neural network. Energy Convers Manage 2002;43(6):817–26. |
| [37] |
Cheng B, Bai Z, Cao B. State of charge estimation based on evolutionary neural network. Energy Convers Manage 2008;49(10):2788–94. |
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