{"title":"考虑燃油效率和锂离子电池退化的串并联混合动力汽车能量管理策略","authors":"Kyungjin Yu, S. Choe, Jinseong Kim","doi":"10.4271/14-12-03-0022","DOIUrl":null,"url":null,"abstract":"Lithium-ion batteries are the most crucial component of hybrid electric vehicles\n (HEVs) with respect to cost and performance. In this article, a new energy\n management strategy (EMS) is developed that improves fuel efficiency (FE) and\n suppresses the degradation of the battery. A hybridized two-layer algorithm that\n combines multi-objective nonlinear model predictive control (NMPC) with a\n rule-based (RB) algorithm is proposed as a new EMS that is called RB-NMPC. The\n RB-NMPC is designed to optimize the torque split between the engine and electric\n motors while maintaining the maximum and minimum constraints of each component.\n The proposed EMS is incorporated into control-oriented vehicle models, and their\n performances are analyzed for different driving cycles by comparing with RB,\n dynamic programming (DP), and NMPC. In addition, the RB-NMPC algorithm is\n applied for two different powertrain configurations of HEV, P0P2 and P1P2\n configurations for both an Urban Dynamometer Driving Schedule (UDDS) and a\n Highway Fuel Economy Test (HWFET). For P0P2, the results show that RB-NMPC\n outperforms other methods for UDDS with an FE that is 4.7% higher than that of\n RB and is the closest to that of DP, which is an optimal standard that is\n limited for real-time application due to its complexity among others. The\n capacity loss of the battery using RB-NMPC is 19.1% less than that using DP when\n applied to the UDDS. The FE of P1P2 is higher than that of P0P2, but the similar\n capacity fade is comparable. RB-NMPC shows the lowest capacity loss for both\n P0P2 and P1P2 configurations. Parallel comparisons are performed for the HWFET.\n For the HWFET, the FEs of P0P2 and P1P2 are similar. However, the capacity fades\n by RB-NMPC are 16.3% and 67.0% reduced compared to that by DP for P0P2 and P1P2,\n respectively. Finally, to verify the effectiveness of the RB-NMPC in reducing\n battery aging, the currents from DP and RB-NMPC EMSs are applied to pouch-type\n lithium-ion batteries and tested for multiple UDDSs using a battery test\n station. The results demonstrate that the RB-NMPC can effectively reduce battery\n aging.","PeriodicalId":36261,"journal":{"name":"SAE International Journal of Electrified Vehicles","volume":"94 1","pages":""},"PeriodicalIF":0.7000,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Energy Management Strategies for Series-Parallel Hybrid Electric\\n Vehicles Considering Fuel Efficiency and Degradation of Lithium-Ion\\n Batteries\",\"authors\":\"Kyungjin Yu, S. Choe, Jinseong Kim\",\"doi\":\"10.4271/14-12-03-0022\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Lithium-ion batteries are the most crucial component of hybrid electric vehicles\\n (HEVs) with respect to cost and performance. In this article, a new energy\\n management strategy (EMS) is developed that improves fuel efficiency (FE) and\\n suppresses the degradation of the battery. A hybridized two-layer algorithm that\\n combines multi-objective nonlinear model predictive control (NMPC) with a\\n rule-based (RB) algorithm is proposed as a new EMS that is called RB-NMPC. The\\n RB-NMPC is designed to optimize the torque split between the engine and electric\\n motors while maintaining the maximum and minimum constraints of each component.\\n The proposed EMS is incorporated into control-oriented vehicle models, and their\\n performances are analyzed for different driving cycles by comparing with RB,\\n dynamic programming (DP), and NMPC. In addition, the RB-NMPC algorithm is\\n applied for two different powertrain configurations of HEV, P0P2 and P1P2\\n configurations for both an Urban Dynamometer Driving Schedule (UDDS) and a\\n Highway Fuel Economy Test (HWFET). For P0P2, the results show that RB-NMPC\\n outperforms other methods for UDDS with an FE that is 4.7% higher than that of\\n RB and is the closest to that of DP, which is an optimal standard that is\\n limited for real-time application due to its complexity among others. The\\n capacity loss of the battery using RB-NMPC is 19.1% less than that using DP when\\n applied to the UDDS. The FE of P1P2 is higher than that of P0P2, but the similar\\n capacity fade is comparable. RB-NMPC shows the lowest capacity loss for both\\n P0P2 and P1P2 configurations. Parallel comparisons are performed for the HWFET.\\n For the HWFET, the FEs of P0P2 and P1P2 are similar. However, the capacity fades\\n by RB-NMPC are 16.3% and 67.0% reduced compared to that by DP for P0P2 and P1P2,\\n respectively. Finally, to verify the effectiveness of the RB-NMPC in reducing\\n battery aging, the currents from DP and RB-NMPC EMSs are applied to pouch-type\\n lithium-ion batteries and tested for multiple UDDSs using a battery test\\n station. The results demonstrate that the RB-NMPC can effectively reduce battery\\n aging.\",\"PeriodicalId\":36261,\"journal\":{\"name\":\"SAE International Journal of Electrified Vehicles\",\"volume\":\"94 1\",\"pages\":\"\"},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2023-06-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SAE International Journal of Electrified Vehicles\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4271/14-12-03-0022\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"TRANSPORTATION SCIENCE & TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SAE International Journal of Electrified Vehicles","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4271/14-12-03-0022","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"TRANSPORTATION SCIENCE & TECHNOLOGY","Score":null,"Total":0}
Energy Management Strategies for Series-Parallel Hybrid Electric
Vehicles Considering Fuel Efficiency and Degradation of Lithium-Ion
Batteries
Lithium-ion batteries are the most crucial component of hybrid electric vehicles
(HEVs) with respect to cost and performance. In this article, a new energy
management strategy (EMS) is developed that improves fuel efficiency (FE) and
suppresses the degradation of the battery. A hybridized two-layer algorithm that
combines multi-objective nonlinear model predictive control (NMPC) with a
rule-based (RB) algorithm is proposed as a new EMS that is called RB-NMPC. The
RB-NMPC is designed to optimize the torque split between the engine and electric
motors while maintaining the maximum and minimum constraints of each component.
The proposed EMS is incorporated into control-oriented vehicle models, and their
performances are analyzed for different driving cycles by comparing with RB,
dynamic programming (DP), and NMPC. In addition, the RB-NMPC algorithm is
applied for two different powertrain configurations of HEV, P0P2 and P1P2
configurations for both an Urban Dynamometer Driving Schedule (UDDS) and a
Highway Fuel Economy Test (HWFET). For P0P2, the results show that RB-NMPC
outperforms other methods for UDDS with an FE that is 4.7% higher than that of
RB and is the closest to that of DP, which is an optimal standard that is
limited for real-time application due to its complexity among others. The
capacity loss of the battery using RB-NMPC is 19.1% less than that using DP when
applied to the UDDS. The FE of P1P2 is higher than that of P0P2, but the similar
capacity fade is comparable. RB-NMPC shows the lowest capacity loss for both
P0P2 and P1P2 configurations. Parallel comparisons are performed for the HWFET.
For the HWFET, the FEs of P0P2 and P1P2 are similar. However, the capacity fades
by RB-NMPC are 16.3% and 67.0% reduced compared to that by DP for P0P2 and P1P2,
respectively. Finally, to verify the effectiveness of the RB-NMPC in reducing
battery aging, the currents from DP and RB-NMPC EMSs are applied to pouch-type
lithium-ion batteries and tested for multiple UDDSs using a battery test
station. The results demonstrate that the RB-NMPC can effectively reduce battery
aging.