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Implementation of Portable Emissions Measurement Systems PEMS for the Real-driving Emissions RDE Regulation in Europe
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A Predictive Energy Management Strategy for Multi-Energy Source Vehicles Based on Full-Factor Trip Information
Fenglai Yue1, Qiao Liu2, Yan Kong2
1State Key Laboratory of Engines, Tianjin University, 135 Yaguan Rd., Tianjin 300350, China.
Sensors (Basel, Switzerland)
|January 11, 2022
Summary
This study introduces a predictive energy management strategy (PEMS) for real-time dynamic programming control. It accurately predicts vehicle speed, slip ratio, and slope for optimized energy usage in electric vehicles.
Area of Science:
- Automotive Engineering
- Control Systems
- Energy Management
Background:
- Real-time application of dynamic programming (DP) control strategies is challenging for energy management.
- Accurate prediction of driving conditions is crucial for optimizing energy consumption in vehicles.
Purpose of the Study:
- To develop a predictive energy management strategy (PEMS) for real-time DP control.
- To improve energy efficiency by utilizing full-factor trip information prediction.
Main Methods:
- Proposed a prediction model for vehicle speed, slip ratio, and slope using trip information.
- Vehicle speed predicted via state transition probability matrix; slope via Markov model.
- Slip ratio predicted using a neural network based on predicted speed and adhesion coefficient.
Main Results:
- Developed a PEMS utilizing predicted full-factor trip information for global optimization.
- Generated a reference state of charge (SOC) trajectory for feasible state domain determination.
- Simulations on UDDS and WLTC driving cycles verified the strategy's effectiveness.
Conclusions:
- The proposed PEMS effectively integrates predicted driving information for enhanced energy management.
- The strategy achieves global sub-optimality, improving energy efficiency in real-world driving conditions.
- This approach enables the real-time application of advanced DP control strategies for vehicles.
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