# MPC-ECMS Energy Management of Extended-Range Vehicles Based on LSTM Multi-Signal Speed Prediction

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## 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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**Figure 4.**Range-extender optimal fuel consumption curve and battery open circuit voltage. (

**a**) Range-extender optimal fuel consumption curve; (

**b**) battery open circuit voltage.

**Figure 12.**Vehicle speed prediction effect of different prediction methods. (

**a**) Comparison of multi-signal LSTM and SVM vehicle speed prediction; (

**b**) comparison of multi-signal LSTM vehicle speed prediction in different prediction time domains.

**Figure 13.**Vehicle speed prediction effect of the different prediction methods. (

**a**) SVM speed forecast heat map comparison; (

**b**) multi-signal LSTM speed prediction heat map comparison.

**Figure 14.**Simulation results of different equivalent factors. (

**a**) Changes in the power battery SoC under different equivalent factors; (

**b**) vehicle fuel consumption under different equivalent factors.

**Figure 17.**Performance comparison chart before and after output power filtering for each control strategy.

**Figure 19.**Graph of changes under various control strategy SOCs. (

**a**) Comparison of the changes in SoC for different control strategies; (

**b**) the fuel consumption of the different control strategies.

**Figure 22.**Distribution of the engine and generator operating points for the different control strategies.

Name | Parameters | Value | Parameters | Value |
---|---|---|---|---|

Vehicle | Total mass | 7000 kg | Wheel radius | 0.60 m |

Aerodynamic drag coefficient | 0.6 | Front area | 9 m${}^{2}$ | |

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 |

$\mathit{hp}$ | Multi-Signal LSTM | SVM | ||
---|---|---|---|---|

$\mathit{T}\_\mathit{pre}$ (s) | RMSE | $\mathit{T}\_\mathit{pre}$ (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 ($s=$ 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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## Share and Cite

**MDPI and ACS Style**

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

**AMA Style**

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 Style**

Lu, 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