Predictive Energy Management Strategy for Heavy-Duty Series Hybrid Electric Vehicles Based on Drive Power Prediction
The driving power of hybrid electric vehicles serves as a crucial foundation for optimizing energy management strategies. The substantial load carried by heavy-duty vehicles significantly impacts the driving power through slope and acceleration. To minimize energy consumption in heavy-duty series hy...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | World Electric Vehicle Journal |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2032-6653/16/3/186 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850088109515997184 |
|---|---|
| author | Yuan Cao Changshui Liang Shi Cheng Xinxian Yin Daxin Chen Zhixi Liu Chaoyang Sun Tao Chen |
| author_facet | Yuan Cao Changshui Liang Shi Cheng Xinxian Yin Daxin Chen Zhixi Liu Chaoyang Sun Tao Chen |
| author_sort | Yuan Cao |
| collection | DOAJ |
| description | The driving power of hybrid electric vehicles serves as a crucial foundation for optimizing energy management strategies. The substantial load carried by heavy-duty vehicles significantly impacts the driving power through slope and acceleration. To minimize energy consumption in heavy-duty series hybrid electric vehicles, key variables are identified and predicted individually, employing the predictive equivalent energy consumption minimization strategy (ECMS) to optimize power distribution. In order to accurately forecast the driving power of heavy-duty vehicles, the vehicle mass is determined using the least squares method. To enhance time series data forecasting capabilities, a CNN-LSTM hybrid network is utilized to predict future vehicle speed and road slope based on historical time series data. By applying a longitudinal dynamics model, the identified vehicle weight, predicted speed, and slope can be converted into actual vehicle driving power. Within the prediction timeframe, different rolling calculation energy distribution methods utilizing equivalent factors are employed to achieve optimal energy consumption reduction. Road experiment data demonstrate that identification errors for various vehicle weights remain below 3%. The average RMSE for single-step drive power prediction stands at 14.8 kW. Simulation results using a test road reveal that the predictive ECMS reduces energy consumption by 6.2% to 15% compared to the original rule-based strategy. |
| format | Article |
| id | doaj-art-a31aedf4e1184c69ac1dd7042fbf038f |
| institution | DOAJ |
| issn | 2032-6653 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj-art-a31aedf4e1184c69ac1dd7042fbf038f2025-08-20T02:43:05ZengMDPI AGWorld Electric Vehicle Journal2032-66532025-03-0116318610.3390/wevj16030186Predictive Energy Management Strategy for Heavy-Duty Series Hybrid Electric Vehicles Based on Drive Power PredictionYuan Cao0Changshui Liang1Shi Cheng2Xinxian Yin3Daxin Chen4Zhixi Liu5Chaoyang Sun6Tao Chen7State Key Laboratory of Engine and Powertrain System, Weichai Power Co., Ltd., Weifang 261000, ChinaState Key Laboratory of Engine and Powertrain System, Weichai Power Co., Ltd., Weifang 261000, ChinaState Key Laboratory of Engine and Powertrain System, Weichai Power Co., Ltd., Weifang 261000, ChinaState Key Laboratory of Engine and Powertrain System, Weichai Power Co., Ltd., Weifang 261000, ChinaState Key Laboratory of Engines, School of Mechanical Engineering, Tianjin University, Tianjin 300350, ChinaState Key Laboratory of Engines, School of Mechanical Engineering, Tianjin University, Tianjin 300350, ChinaState Key Laboratory of Engines, School of Mechanical Engineering, Tianjin University, Tianjin 300350, ChinaState Key Laboratory of Engines, School of Mechanical Engineering, Tianjin University, Tianjin 300350, ChinaThe driving power of hybrid electric vehicles serves as a crucial foundation for optimizing energy management strategies. The substantial load carried by heavy-duty vehicles significantly impacts the driving power through slope and acceleration. To minimize energy consumption in heavy-duty series hybrid electric vehicles, key variables are identified and predicted individually, employing the predictive equivalent energy consumption minimization strategy (ECMS) to optimize power distribution. In order to accurately forecast the driving power of heavy-duty vehicles, the vehicle mass is determined using the least squares method. To enhance time series data forecasting capabilities, a CNN-LSTM hybrid network is utilized to predict future vehicle speed and road slope based on historical time series data. By applying a longitudinal dynamics model, the identified vehicle weight, predicted speed, and slope can be converted into actual vehicle driving power. Within the prediction timeframe, different rolling calculation energy distribution methods utilizing equivalent factors are employed to achieve optimal energy consumption reduction. Road experiment data demonstrate that identification errors for various vehicle weights remain below 3%. The average RMSE for single-step drive power prediction stands at 14.8 kW. Simulation results using a test road reveal that the predictive ECMS reduces energy consumption by 6.2% to 15% compared to the original rule-based strategy.https://www.mdpi.com/2032-6653/16/3/186heavy-duty hybrid electric vehiclesmass estimationspeed and slope predictionenergy management strategy |
| spellingShingle | Yuan Cao Changshui Liang Shi Cheng Xinxian Yin Daxin Chen Zhixi Liu Chaoyang Sun Tao Chen Predictive Energy Management Strategy for Heavy-Duty Series Hybrid Electric Vehicles Based on Drive Power Prediction World Electric Vehicle Journal heavy-duty hybrid electric vehicles mass estimation speed and slope prediction energy management strategy |
| title | Predictive Energy Management Strategy for Heavy-Duty Series Hybrid Electric Vehicles Based on Drive Power Prediction |
| title_full | Predictive Energy Management Strategy for Heavy-Duty Series Hybrid Electric Vehicles Based on Drive Power Prediction |
| title_fullStr | Predictive Energy Management Strategy for Heavy-Duty Series Hybrid Electric Vehicles Based on Drive Power Prediction |
| title_full_unstemmed | Predictive Energy Management Strategy for Heavy-Duty Series Hybrid Electric Vehicles Based on Drive Power Prediction |
| title_short | Predictive Energy Management Strategy for Heavy-Duty Series Hybrid Electric Vehicles Based on Drive Power Prediction |
| title_sort | predictive energy management strategy for heavy duty series hybrid electric vehicles based on drive power prediction |
| topic | heavy-duty hybrid electric vehicles mass estimation speed and slope prediction energy management strategy |
| url | https://www.mdpi.com/2032-6653/16/3/186 |
| work_keys_str_mv | AT yuancao predictiveenergymanagementstrategyforheavydutyserieshybridelectricvehiclesbasedondrivepowerprediction AT changshuiliang predictiveenergymanagementstrategyforheavydutyserieshybridelectricvehiclesbasedondrivepowerprediction AT shicheng predictiveenergymanagementstrategyforheavydutyserieshybridelectricvehiclesbasedondrivepowerprediction AT xinxianyin predictiveenergymanagementstrategyforheavydutyserieshybridelectricvehiclesbasedondrivepowerprediction AT daxinchen predictiveenergymanagementstrategyforheavydutyserieshybridelectricvehiclesbasedondrivepowerprediction AT zhixiliu predictiveenergymanagementstrategyforheavydutyserieshybridelectricvehiclesbasedondrivepowerprediction AT chaoyangsun predictiveenergymanagementstrategyforheavydutyserieshybridelectricvehiclesbasedondrivepowerprediction AT taochen predictiveenergymanagementstrategyforheavydutyserieshybridelectricvehiclesbasedondrivepowerprediction |