Predictive Energy Management Strategy for Heavy-Duty Series Hybrid Electric Vehicles Based on Drive Power Prediction - Yuan Cao; Changshui Liang; Shi Cheng; Xinxian Yin; Daxin Chen; Zhixi Liu; Chaoyang Sun; Tao Chen (World Electric Vehicle Journal)
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.