Predictive Energy Management Strategy for Range-Extended Electric Vehicles Based on ITS Information and Start–Stop Optimization with Vehicle Velocity Forecast
Range-extended Electric Vehicles (REVs) have become popular due to their lack of emissions while driving in urban areas, and the elimination of range anxiety when traveling long distances with a combustion engine as the power source. The fuel consumption performance of REVs depends greatly on the en...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/20/7774 |
| _version_ | 1827650389355266048 |
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| author | Weiyi Lin Han Zhao Bingzhan Zhang Ye Wang Yan Xiao Kang Xu Rui Zhao |
| author_facet | Weiyi Lin Han Zhao Bingzhan Zhang Ye Wang Yan Xiao Kang Xu Rui Zhao |
| author_sort | Weiyi Lin |
| collection | DOAJ |
| description | Range-extended Electric Vehicles (REVs) have become popular due to their lack of emissions while driving in urban areas, and the elimination of range anxiety when traveling long distances with a combustion engine as the power source. The fuel consumption performance of REVs depends greatly on the energy management strategy (EMS). This article proposes a practical energy management solution for REVs based on an Adaptive Equivalent Fuel Consumption Minimization Strategy (A-ECMS), wherein the equivalent factor is dynamically optimized by the battery’s State of Charge (SoC) and traffic information provided by Intelligent Transportation Systems (ITS). Furthermore, a penalty function is incorporated with the A-ECMS strategy to achieve the quasi-optimal start–stop control of the range extender. The penalty function is designed based on more precise vehicle velocity forecasting through a nonlinear autoregressive network with exogeneous input (NARX). A model of the studied REV is established in the AVL Cruise environment and the proposed energy management strategy is set up in Matlab/Simulink. Lastly, the performance of the proposed strategy is evaluated over multiple Worldwide Light-duty Test Cycles (WLTC) and real-world driving cycles through model simulation. The simulation conditions are preset such that the range extender must be switched on to finish the planned route. Compared with the basic Charge-Depleting and Charge-Sustaining (CD-CS) strategy, the proposed A-ECMS strategy achieves a fuel-consumption benefit of up to 9%. With the implementation of range extender start–stop optimization, which is based on velocity forecasting, the fuel saving rate can be further improved by 6.7% to 18.2% compared to the base A-ECMS. The proposed strategy is energy efficient, with a simple structure, and it is intended to be implemented on the studied vehicle, which will be available on the market at the end of October 2022. |
| first_indexed | 2024-03-09T20:16:33Z |
| format | Article |
| id | doaj.art-7855ec9386ad4b7ba66bcd4c966fdb57 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T20:16:33Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-7855ec9386ad4b7ba66bcd4c966fdb572023-11-24T00:00:42ZengMDPI AGEnergies1996-10732022-10-011520777410.3390/en15207774Predictive Energy Management Strategy for Range-Extended Electric Vehicles Based on ITS Information and Start–Stop Optimization with Vehicle Velocity ForecastWeiyi Lin0Han Zhao1Bingzhan Zhang2Ye Wang3Yan Xiao4Kang Xu5Rui Zhao6School of Automobile and Transportation Engineering, Hefei University of Technology, Hefei 230009, ChinaNational and Local Joint Engineering Research Center for Automotive Technology and Equipment, Hefei University of Technology, Hefei 230009, ChinaSchool of Automobile and Transportation Engineering, Hefei University of Technology, Hefei 230009, ChinaIntelligent Vehicle Division, Hozon New Energy Automobile Co., Ltd., Tongxiang 314505, ChinaIntelligent Vehicle Division, Hozon New Energy Automobile Co., Ltd., Tongxiang 314505, ChinaIntelligent Vehicle Division, Hozon New Energy Automobile Co., Ltd., Tongxiang 314505, ChinaIntelligent Vehicle Division, Hozon New Energy Automobile Co., Ltd., Tongxiang 314505, ChinaRange-extended Electric Vehicles (REVs) have become popular due to their lack of emissions while driving in urban areas, and the elimination of range anxiety when traveling long distances with a combustion engine as the power source. The fuel consumption performance of REVs depends greatly on the energy management strategy (EMS). This article proposes a practical energy management solution for REVs based on an Adaptive Equivalent Fuel Consumption Minimization Strategy (A-ECMS), wherein the equivalent factor is dynamically optimized by the battery’s State of Charge (SoC) and traffic information provided by Intelligent Transportation Systems (ITS). Furthermore, a penalty function is incorporated with the A-ECMS strategy to achieve the quasi-optimal start–stop control of the range extender. The penalty function is designed based on more precise vehicle velocity forecasting through a nonlinear autoregressive network with exogeneous input (NARX). A model of the studied REV is established in the AVL Cruise environment and the proposed energy management strategy is set up in Matlab/Simulink. Lastly, the performance of the proposed strategy is evaluated over multiple Worldwide Light-duty Test Cycles (WLTC) and real-world driving cycles through model simulation. The simulation conditions are preset such that the range extender must be switched on to finish the planned route. Compared with the basic Charge-Depleting and Charge-Sustaining (CD-CS) strategy, the proposed A-ECMS strategy achieves a fuel-consumption benefit of up to 9%. With the implementation of range extender start–stop optimization, which is based on velocity forecasting, the fuel saving rate can be further improved by 6.7% to 18.2% compared to the base A-ECMS. The proposed strategy is energy efficient, with a simple structure, and it is intended to be implemented on the studied vehicle, which will be available on the market at the end of October 2022.https://www.mdpi.com/1996-1073/15/20/7774range-extended vehiclespredictive energy managementadaptive-ECMS strategystart–stop optimizationvehicle velocity forecast |
| spellingShingle | Weiyi Lin Han Zhao Bingzhan Zhang Ye Wang Yan Xiao Kang Xu Rui Zhao Predictive Energy Management Strategy for Range-Extended Electric Vehicles Based on ITS Information and Start–Stop Optimization with Vehicle Velocity Forecast Energies range-extended vehicles predictive energy management adaptive-ECMS strategy start–stop optimization vehicle velocity forecast |
| title | Predictive Energy Management Strategy for Range-Extended Electric Vehicles Based on ITS Information and Start–Stop Optimization with Vehicle Velocity Forecast |
| title_full | Predictive Energy Management Strategy for Range-Extended Electric Vehicles Based on ITS Information and Start–Stop Optimization with Vehicle Velocity Forecast |
| title_fullStr | Predictive Energy Management Strategy for Range-Extended Electric Vehicles Based on ITS Information and Start–Stop Optimization with Vehicle Velocity Forecast |
| title_full_unstemmed | Predictive Energy Management Strategy for Range-Extended Electric Vehicles Based on ITS Information and Start–Stop Optimization with Vehicle Velocity Forecast |
| title_short | Predictive Energy Management Strategy for Range-Extended Electric Vehicles Based on ITS Information and Start–Stop Optimization with Vehicle Velocity Forecast |
| title_sort | predictive energy management strategy for range extended electric vehicles based on its information and start stop optimization with vehicle velocity forecast |
| topic | range-extended vehicles predictive energy management adaptive-ECMS strategy start–stop optimization vehicle velocity forecast |
| url | https://www.mdpi.com/1996-1073/15/20/7774 |
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