# An Adaptive Adjustment Method of Equivalent Factor Considering Speed Predict Information

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Literature Backgrounds

## 3. Powertrain Model

## 4. Speed Prediction Model

#### 4.1. RBF-NN Speed Prediction Model

#### 4.2. RBF-NN Speed Prediction Model Based on WPT

## 5. EF Adaptive Adjustment Method for ECMS

#### 5.1. ECMS

#### 5.2. EF Adaptive Adjustment Method Based on SOC Feedback

#### 5.3. EF Adaptive Adjustment Method Based on Speed Prediction

## 6. Validation and Results

## 7. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

- Zhao, X.; Guo, G. Review on energy management strategy of hybrid electric vehicle. JAS
**2016**, 42, 321–334. (In Chinese) [Google Scholar] - Su, L.; Zeng, Y.; Qin, D. Research status and development trend of energy management strategy for plug-in hybrid electric vehicle. J. Chongqing Univ.
**2017**, 40, 10–15. (In Chinese) [Google Scholar] - Wang, W.; Liu, K.; Yang, C.; Xu, B.; Ma, M. Cyber Physical Energy Optimization Control Design for PHEVs Based on Enhanced Firework Algorithm. IEEE Trans. Veh. Technol.
**2021**, 70, 282–291. [Google Scholar] [CrossRef] - Plötz, P.; Moll, C.; Bieker, G.; Mock, P. From lab-to-road: Real-world fuel consumption and CO2 emissions of plug-in hybrid electric vehicles. Environ. Res. Lett.
**2021**, 16, 054078. [Google Scholar] [CrossRef] - Pam, A.; Bouscayrol, A.; Fiani, P.; Noth, F. Rule-Based Energy Management Strategy for a Parallel Hybrid Electric Vehicle Deduced from Dynamic Programming. In Proceedings of the 2017 IEEE Vehicle Power and Propulsion Conference, Belfort, France, 11–14 December 2017; pp. 1–6. [Google Scholar]
- Peng, J.; He, H.; Xiong, R. Rule based energy management strategyfor a series–parallel plug-in hybrid electric bus optimized by dynamic programming. Appl. Energy
**2017**, 185, 1633–1643. [Google Scholar] [CrossRef] - Li, S.; Sharkh, S.; Walsh, F.; Zhang, C. Energy and battery management of a plug-in series hybrid electric vehicle using fuzzy logic. IEEE Trans. Veh. Technol.
**2011**, 60, 3571–3585. [Google Scholar] [CrossRef] - Wang, Y.; Sun, Z.; Chen, Z. Development of energy management system based on a rule-based power distribution strategy for hybrid power sources. Energy
**2017**, 175, 1055–1066. [Google Scholar] [CrossRef] - Padmarajan, B.; McGordon, A.; Jennings, P. Blended Rule-Based Energy Management for PHEV: System Structure and Strategy. IEEE Trans. Veh. Technol.
**2016**, 65, 8757–8762. [Google Scholar] [CrossRef] - Fang, S.; Gou, B.; Wang, Y.; Xu, Y.; Shang, C.; Wang, H. Optimal hierarchical management of shipboard multibattery energy storage system using a data-driven degradation model. IEEE Trans. Transp. Electrif.
**2019**, 5, 1306–1318. [Google Scholar] [CrossRef] - Musardo, C.; Rizzoni, G.; Guezennec, Y.; Staccia, B. A-ecms: An adaptive algorithm for hybrid electric vehicle energy management. Eur. J. Control.
**2005**, 11, 509–524. [Google Scholar] [CrossRef] - Li, J.; Liu, Y.; Qin, D.; Li, G.; Chen, Z. Research on Equivalent Factor Boundary of Equivalent Consumption Minimization Strategy for PHEVs. IEEE Trans. Veh. Technol.
**2020**, 69, 6011–6024. [Google Scholar] [CrossRef] - Yang, C.; Du, S.; Li, L.; You, S.; Yang, Y.; Zhao, Y. Adaptive real-time optimal energy management strategy based on equivalent factors optimization for plug-in hybrid electric vehicle. Appl. Energy
**2017**, 203, 883–896. [Google Scholar] [CrossRef] - Yang, S.; Wang, J.; Zhang, F.; Xi, J. Self-adaptive equivalent consumption minimization strategy for hybrid electric vehicles. IEEE Trans. Veh. Technol.
**2020**, 70, 189–202. [Google Scholar] [CrossRef] - Qin, D.; Zhan, S.; Zeng, Y.; Su, L. Management strategy of hybrid electrical vehicle based on driving style recognition. J. Mech. Eng.
**2016**, 52, 162–169. (In Chinese) [Google Scholar] [CrossRef] - Liu, L.; Zhang, B.; Jiang, T. Equivalent consumption minimization strategy for PHEV based on driving condition adaptation. J. Automot. Saf. Energy
**2020**, 11, 371–378. (In Chinese) [Google Scholar] - Rezaei, A.; Burl, J.B.; Zhou, B. Estimation of the ECMS equivalent factor bounds for hybrid electric vehicles. IEEE Trans. Control. Syst. Technol.
**2018**, 26, 2198–2205. [Google Scholar] [CrossRef] - Sun, C.; He, H.; Sun, F. Investigating adaptive-ECMS with velocity forecast ability for hybrid electric vehicles. Appl. Energy
**2017**, 185, 1644–1653. [Google Scholar] [CrossRef] - Zhang, Y.; Chu, L.; Ding, Y.; Xu, N.; Guo, C.; Fu, Z.; Xu, L.; Xin, T.; Liu, Y. A hierarchical energy management strategy based on model predictive control for plug-in hybrid electric vehicles. IEEE Access
**2019**, 7, 81612–81629. [Google Scholar] [CrossRef] - Zhang, F.; Hu, X.; Liu, T.; Xu, K.; Duan, Z.; Pang, H. Computationally efficient energy management for hybrid electric vehicles using model predictive control and vehicle-to-vehicle communication. IEEE Trans. Veh. Technol.
**2021**, 70, 237–250. [Google Scholar] [CrossRef] - Sun, C.; Hu, X.; Moura, S.J.; Sun, F. Velocity predictors for predictive energy management in hybrid electric vehicles. IEEE Trans. Control. Syst. Technol.
**2015**, 23, 1197–1204. [Google Scholar] - Chen, Z.; Guo, N.; Shen, J.; Xiao, R.; Dong, P. A hierarchical energy management strategy for power-split plug-in hybrid electric vehicles considering velocity prediction. IEEE Access
**2018**, 6, 33261–33274. [Google Scholar] [CrossRef] - Li, Y.; Jiao, X.; Jing, Y. A real-time energy management strategy combining rule-based control and ECMS with optimization equivalent factor for HEVs. In Proceedings of the 2017 Chinese Automation Congress, Jinan, China, 20–22 October 2017; pp. 5988–5992. [Google Scholar]

Part | Specification | Value |
---|---|---|

Vehicle | Vehicle loaded mass | 2065 kg |

Roll resistance coefficient | 0.014 | |

Windward area | 2.4 m^{2} | |

Wheel radius | 0.334 | |

Engine | Maximum power | 118 kW |

Maximum speed | 5500 rpm | |

Capacity | 1.5 L | |

Motor | Peak power | 110 kW |

Peak torque | 250 Nm | |

Peak speed | 12,000 rpm | |

Battery | Maximum capacity | 33 Ah |

Open-circuit voltage | 502 V |

Predict Time Length (s) | Predict Error under CLTC Condition (km/h) |
---|---|

1 | 7.99 |

3 | 2.3586 |

5 | 3.7088 |

10 | 6.9010 |

15 | 9.3926 |

20 | 11.3781 |

EF Adjustment Method | Initial/Final SOC | Equivalent Fuel Consumption/(L/100 km) |
---|---|---|

Speed-known | 0.3/0.3 | 7.99 |

Speed-predict | 0.3/0.2986 | 8.23 |

Speed-unknown | 0.3/0.3115 | 8.57 |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Zhang, B.; Meng, Q.; Wu, J.; Ni, Y.
An Adaptive Adjustment Method of Equivalent Factor Considering Speed Predict Information. *World Electr. Veh. J.* **2021**, *12*, 211.
https://doi.org/10.3390/wevj12040211

**AMA Style**

Zhang B, Meng Q, Wu J, Ni Y.
An Adaptive Adjustment Method of Equivalent Factor Considering Speed Predict Information. *World Electric Vehicle Journal*. 2021; 12(4):211.
https://doi.org/10.3390/wevj12040211

**Chicago/Turabian Style**

Zhang, Bingzhan, Qinglong Meng, Juncheng Wu, and Yaoyao Ni.
2021. "An Adaptive Adjustment Method of Equivalent Factor Considering Speed Predict Information" *World Electric Vehicle Journal* 12, no. 4: 211.
https://doi.org/10.3390/wevj12040211