基于最小能耗的动力电池风冷控制策略
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摘要
针对目前电动汽车动力电池风冷散热能耗高、散热滞后的问题,提出一种基于最小能耗的动力电池风冷控制策略,根据车载导航系统预报的工况信息预测动力电池的未来温升,在满足动力电池散热需求的前提下以风机能耗最少为目标,运用分段式动态规划算法确定风机在未来路段的开启时机与最优风速。以添加了坡度信息的ARB02、HWFET和UDDSHDV的组合工况为测试工况,对动力电池未来温升的精度进行了硬件在环试验,得出实际路况试验温度与预报工况试验温度的最大差值为0.3℃,最大偏差率为0.7%。与其他两种控制策略进行了Fluent仿真对比,结果表明基于最小能耗控制策略下动力电池的最高温度为39.87℃,最大温差为1.1℃;风机能耗是全程开启型控制策略的77.2%,是温度开关型控制策略的53.7%。该策略能有效控制动力电池的温度且风机能耗最小。
To solve the problems of high energy consumption and lag in heat dissipation of power batteries of electric vehicles, a control strategy of air cooling for power batteries based on minimum energy consumption is proposed. The strategy predicts the future temperature rise of power battery according to the working conditions which is predicted by vehicle navigation system. On the premise of meeting the heat dissipation requirement of power battery, the optimal objective is to minimize the energy consumption of fan, the sectional dynamic programming algorithm is applied to determining the starting time and optimal wind speed of fan in the future section. Taking the combined working conditions of ARB02, HWFET and UDDSHDV with slope information as test conditions, the hardwarein-the-loop test for the accuracy of predicting the future temperature rise of power battery is carried out.The maximum difference between the real-time road condition test temperature and the test temperature in predicting working conditions is 0.3 ℃, the maximum deviation rate is 0.7%. Fluent simulation results are compared with the other two control strategies, The results show that under the control strategy, the temperature of power battery is always within 40 ℃, and the maximum temperature difference of battery pack is 1.1 ℃; the energy consumption of fan is 77.2% of that of the control strategy of fan opening in the whole process, and 53.7% of that of fan based on temperature control strategy. The strategy can effectively control the temperature of power battery and minimize the energy consumption of fan.
To solve the problems of high energy consumption and lag in heat dissipation of power batteries of electric vehicles, a control strategy of air cooling for power batteries based on minimum energy consumption is proposed. The strategy predicts the future temperature rise of power battery according to the working conditions which is predicted by vehicle navigation system. On the premise of meeting the heat dissipation requirement of power battery, the optimal objective is to minimize the energy consumption of fan, the sectional dynamic programming algorithm is applied to determining the starting time and optimal wind speed of fan in the future section. Taking the combined working conditions of ARB02, HWFET and UDDSHDV with slope information as test conditions, the hardwarein-the-loop test for the accuracy of predicting the future temperature rise of power battery is carried out.The maximum difference between the real-time road condition test temperature and the test temperature in predicting working conditions is 0.3 ℃, the maximum deviation rate is 0.7%. Fluent simulation results are compared with the other two control strategies, The results show that under the control strategy, the temperature of power battery is always within 40 ℃, and the maximum temperature difference of battery pack is 1.1 ℃; the energy consumption of fan is 77.2% of that of the control strategy of fan opening in the whole process, and 53.7% of that of fan based on temperature control strategy. The strategy can effectively control the temperature of power battery and minimize the energy consumption of fan.
引文
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[3]陈凯,汪双凤.基于遗传算法的风冷式动力电池热管理系统优化[J].工程热物理学报,2018,39(2):384-388.CHEN Kai,WANG Shuangfeng.Optimization of thermal management system for air-cooled power batteries based on genetic algorithm[J].Journal of Engineering Thermophysics,2018,39(2):384-388.
[4]陈凯,汪双凤.基于贪婪算法的风冷式动力电池热管理系统优化[J].工程热物理学报,2018,39(5):1092-1096.CHEN Kai,WANG Shuangfeng.Optimization of thermal management system for air-cooled power batteries based on greedy algorithm[J].Journal of Engineering Thermophysics,2018,39(5):1092-1096.
[5]JIANG F,XU X,HU D.Analysis of heat dissipation performance between a horizontal and longitudinal battery pack based on forced air cooling[J].Heat Transfer-asian Research,2017,46(7):778-792.
[6]MAHAMUD R,PARK C.Reciprocating air flow for Li-ion battery thermal management to improve temperature uniformity[J].Journal of Power Sources,2011,196(13):5685-5696.
[7]GAO Yuping,SHAO Shuangquan,TIAN Shen,et al.Energy consumption analysis of the forced-air cooling process with alternating ventilation mode for fresh horticultural produce[J].Energy Procedia,2017,142:2642-2647.
[8]TAO W,TSENG K J,ZHAO J.Development of efficient air-cooling strategies for lithium-ion battery module based on empirical heat source model[J].Applied Thermal Engineering,2015,90:521-529.
[9]郭阳东.基于典型城市工况的电动汽车动力电池热管理策略研究[D].南京:南京航空航天大学,2017.GUO Yangdong.Research on thermal management strategy of power battery based on typical city driving cycles of electric vehicles[D].Nanjing:Nanjing University of Aeronautics and Astronautics,2017.
[10]HE F,MA L.Thermal management of batteries employing active temperature control and reciprocating cooling flow[J].International Journal of Heat and Mass Transfer,2015,83:164-172.
[11]CHOI Y S,KANG D M.Prediction of thermal behaviors of an aircooled lithium-ion battery system for hybrid electric vehicles[J].Journal of Power Sources,2014,270:273-280.
[12]CHONG Z,FEI L,HUA Z,et al.A real-time battery thermal management strategy for connected and automated hybrid electric vehicles(CAHEVs)based on iterative dynamic programming[J].IEEETransactions on Vehicular Technology,2018,67(9):8077-8084.
[13]CHONG Z,FEI L,HUA Z,et al.A finite-set model-based predictive battery thermal management in connected and automated hybrid electric vehicles[C]//Institute of Electrical and Electronics Engineers(IEEE)NY.Curran Associates,Inc.2018:3428-3433.
[14]RAJAGOPALAN A,WASHINGTON G.Intelligent control of hybrid electric vehicle using GPS information[C]//SAE Technical Paper,2002.
[15]CHEWPUTTANAGUL P,JACKSON D J.A road recognition system using GPS/GIS integrated system[C]//Tencon IEEE Region 10Conference,2004.
[16]PESARAN A A,SWAN D,OLSON J,et al.Thermal analysis and performance of a battery pack for a hybrid electric vehicles[R].National Renewable Energy Laboratory,1998.
[17]余志生.汽车理论[M].北京:机械工业出版社,2004.YU Zhisheng.Automobile theory[M].Beijing:Machinery Industry Press,2004.
[18]NEWMAN,BERNARDI,PAWLIKOWSKI.A general energy-balance for battery systems[J].Journal of the Electrochemical Society,1985,132(1):5-12.
[19]赵镇南.传热学[M].北京:高等教育出版社,2002:220-222.ZHAO Zhennan.Heat Transfer[M].Beijing:Higher Education Press,2002:220-222.
[20]宋秋红.工程流体力学[M].第2版,上海:上海交通大学出版社,2012.SONG Qiuhong.Engineering fluid dynamics[M].2nd Edition,Shanghai:Shanghai Jiaotong University Press,2012.
[21]RICHARD BELLMAN.Dynamic programming[M].New York:Dover Publications,2003.
[22]中华人民共和国交通部.JTG D20-2006公路线路设计规范[S].北京:人民交通出版社,2006.Ministry of Communications of the People's Republic of China.JTG D20-2006 highway line design code[S].Beijing:People's Transportation Press.2006.