Adaptive Cruise Control Strategy for Electric Vehicles Considering Battery Degradation Characteristics
This paper proposes an economic adaptive cruise controller (EACC) that considers battery aging characteristics based on adaptive model predictive control (AMPC). By establishing a battery capacity decay model based on experimental data, the capacity loss during vehicle operation is determined, and t...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/7/4553 |
| _version_ | 1827747043576119296 |
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| author | Chaofeng Pan Chi Zhang Jian Wang Qian Liu |
| author_facet | Chaofeng Pan Chi Zhang Jian Wang Qian Liu |
| author_sort | Chaofeng Pan |
| collection | DOAJ |
| description | This paper proposes an economic adaptive cruise controller (EACC) that considers battery aging characteristics based on adaptive model predictive control (AMPC). By establishing a battery capacity decay model based on experimental data, the capacity loss during vehicle operation is determined, and the parameters in the equivalent circuit model are updated according to the actual capacity of the battery. The controller uses indicators that characterize driving safety, tracking performance, comfort, and economy. The economic indicator is the decrease in the value of the battery capacity. Fuzzy weight allocation is designed based on the host vehicle’s speed and the workshop’s relative distance to adjust the weight between different indicators under different working conditions. Additionally, the proposed controller is compared with other traditional controllers under different working conditions, cycle times, and battery state of health (SOH). The simulation results indicate that, under various battery SOH conditions, the performance of the controller which considers battery capacity degradation characteristics is better than that of traditional controllers. Moreover, the fixed-weight controller performs better when following a vehicle at medium and low speeds. Finally, the proposed strategy was validated through hardware-in-the-loop testing, demonstrating its ability to meet the real-time requirements of the system. |
| first_indexed | 2024-03-11T05:41:37Z |
| format | Article |
| id | doaj.art-24aab3005a8a4918b4247cc0b612dd61 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T05:41:37Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-24aab3005a8a4918b4247cc0b612dd612023-11-17T16:22:01ZengMDPI AGApplied Sciences2076-34172023-04-01137455310.3390/app13074553Adaptive Cruise Control Strategy for Electric Vehicles Considering Battery Degradation CharacteristicsChaofeng Pan0Chi Zhang1Jian Wang2Qian Liu3Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, ChinaAutomotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, ChinaAutomotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, ChinaAutomotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, ChinaThis paper proposes an economic adaptive cruise controller (EACC) that considers battery aging characteristics based on adaptive model predictive control (AMPC). By establishing a battery capacity decay model based on experimental data, the capacity loss during vehicle operation is determined, and the parameters in the equivalent circuit model are updated according to the actual capacity of the battery. The controller uses indicators that characterize driving safety, tracking performance, comfort, and economy. The economic indicator is the decrease in the value of the battery capacity. Fuzzy weight allocation is designed based on the host vehicle’s speed and the workshop’s relative distance to adjust the weight between different indicators under different working conditions. Additionally, the proposed controller is compared with other traditional controllers under different working conditions, cycle times, and battery state of health (SOH). The simulation results indicate that, under various battery SOH conditions, the performance of the controller which considers battery capacity degradation characteristics is better than that of traditional controllers. Moreover, the fixed-weight controller performs better when following a vehicle at medium and low speeds. Finally, the proposed strategy was validated through hardware-in-the-loop testing, demonstrating its ability to meet the real-time requirements of the system.https://www.mdpi.com/2076-3417/13/7/4553electric vehiclesbattery lifeadaptive cruise controladaptive model predictive controlfuzzy control |
| spellingShingle | Chaofeng Pan Chi Zhang Jian Wang Qian Liu Adaptive Cruise Control Strategy for Electric Vehicles Considering Battery Degradation Characteristics Applied Sciences electric vehicles battery life adaptive cruise control adaptive model predictive control fuzzy control |
| title | Adaptive Cruise Control Strategy for Electric Vehicles Considering Battery Degradation Characteristics |
| title_full | Adaptive Cruise Control Strategy for Electric Vehicles Considering Battery Degradation Characteristics |
| title_fullStr | Adaptive Cruise Control Strategy for Electric Vehicles Considering Battery Degradation Characteristics |
| title_full_unstemmed | Adaptive Cruise Control Strategy for Electric Vehicles Considering Battery Degradation Characteristics |
| title_short | Adaptive Cruise Control Strategy for Electric Vehicles Considering Battery Degradation Characteristics |
| title_sort | adaptive cruise control strategy for electric vehicles considering battery degradation characteristics |
| topic | electric vehicles battery life adaptive cruise control adaptive model predictive control fuzzy control |
| url | https://www.mdpi.com/2076-3417/13/7/4553 |
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