MPC-ECMS Energy Management of Extended-Range Vehicles Based on LSTM Multi-Signal Speed Prediction
Abstract
:1. Introduction
2. Powertrain Modelling
2.1. Longitudinal Dynamics Model of the Vehicle
2.2. Engine Model
2.3. Generator and Drive Motor Models
2.4. Power Battery Model
3. Speed Prediction
3.1. Data Processing Based on Pearson’s Correlation
3.2. Vehicle Speed Prediction Based on SVM
3.3. Multi-Signal Vehicle Speed Prediction Based on LSTM
3.4. Vehicle Speed Prediction Results and Performance Comparison
4. MPC-ECMS
4.1. Energy Management Based on ECMS
4.2. MPC-ECMS Energy Management
4.3. HIL Simulation Experiment
5. Results and Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
LSTM | Long short-term memory neural networks |
MPC | Model predictive control |
ECMS | Equivalent fuel consumption minimum strategy |
SVM | Support vector machine |
SVR | Support vector regression |
SoC | State of charge |
DP | Dynamic programming |
WTVC | World transient vehicle cycle |
HIL | Hardware-in-the-loop |
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Name | Parameters | Value | Parameters | Value |
---|---|---|---|---|
Vehicle | Total mass | 7000 kg | Wheel radius | 0.60 m |
Aerodynamic drag coefficient | 0.6 | Front area | 9 m | |
Engine | Engine type | Diesel engine | Maximum torque | 2200 Nm |
Maximum speed | 2000 r/min | |||
Electric motor | Maximum torque | 1800 Nm | Maximum speed | 2650 r/min |
Type | Permanent magnet synchronous | |||
Generator | Maximum torque | 2200 Nm | Maximum speed | 2000 r/min |
Battery pack | Voltage | 580 V | Capacity | 200 Ah |
CAN Bus Signal | Pearson’s Correlation Coefficient |
---|---|
Accelerator pedal opening | 0.6192 |
Brake pedal opening | −0.2737 |
Motor speed | 1 |
Air resistance | 0.9687 |
Engine speed | 0.2085 |
Alternator speed | 0.2085 |
Motor torque | 0.0356 |
Multi-Signal LSTM | SVM | |||
---|---|---|---|---|
(s) | RMSE | (s) | RMSE | |
3 s | 0.0044 | 1.6085 | 0.00611 | 2.3735 |
5 s | 0.0034 | 3.0936 | 0.009597 | 4.2482 |
7 s | 0.0045 | 6.6171 | 0.0234 | 6.9404 |
Control Strategy | SoC | Fuel Consumption (kg) | ||
---|---|---|---|---|
Pre-Filter | After-Filter | Pre-Filter | After-Filter | |
DP | 0.2938 | 0.2902 | 2.9637 | 2.536 |
ECMS ( 2.5) | 0.308 | 0.3045 | 3.112 | 2.663 |
Multi-signal-LSTM-MPC-ECMS | 0.3031 | 0.2976 | 3.02 | 2.627 |
Power-follow | 0.306 | 0.3052 | 3.103 | 2.902 |
SVM-MPC-ECMS | 0.3013 | 0.2985 | 3.505 | 3.115 |
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Lu, L.; Zhao, H.; Liu, X.; Sun, C.; Zhang, X.; Yang, H. MPC-ECMS Energy Management of Extended-Range Vehicles Based on LSTM Multi-Signal Speed Prediction. Electronics 2023, 12, 2642. https://doi.org/10.3390/electronics12122642
Lu L, Zhao H, Liu X, Sun C, Zhang X, Yang H. MPC-ECMS Energy Management of Extended-Range Vehicles Based on LSTM Multi-Signal Speed Prediction. Electronics. 2023; 12(12):2642. https://doi.org/10.3390/electronics12122642
Chicago/Turabian StyleLu, Laiwei, Hong Zhao, Xiaotong Liu, Chuanlong Sun, Xinyang Zhang, and Haixu Yang. 2023. "MPC-ECMS Energy Management of Extended-Range Vehicles Based on LSTM Multi-Signal Speed Prediction" Electronics 12, no. 12: 2642. https://doi.org/10.3390/electronics12122642