# Online Control Strategy for Plug-In Hybrid Electric Vehicles Based on an Improved Global Optimization Algorithm

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Power System Model of the PHEV

#### 2.1. Vehicle Dynamics Model

#### 2.2. Numerical Model of the Engine and Motor

#### 2.3. CVT Model

#### 2.4. Theoretical Model of the Battery

## 3. Offline Optimization Algorithm

#### 3.1. Approximately Equivalent Fuel Consumption Minimum Strategy

#### 3.2. Improved Dynamic Programming Algorithm

#### 3.3. Offline Optimization Simulation Results

## 4. Online Control Strategy

#### 4.1. Neural Network Training

#### 4.2. Online Controller Verification

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 5.**Dynamic programming (DP)-approximate equivalent fuel consumption minimum control strategy (A-ECMS) algorithm battery state of charge (SOC) change curve, (

**a**) 11-SC03; (

**b**) 9-HWFET.

**Figure 6.**Optimal equivalent factor sequence, (

**a**) 11-SC03, Initial SOC = 0.9; (

**b**) 9-HWFET, Initial SOC = 0.9; (

**c**) 11-SC03, Initial SOC = 0.6; (

**d**) 9-HWFET, Initial SOC = 0.6; (

**e**) 11-SC03, Initial SOC = 0.3; (

**f**) 9-HWFET, Initial SOC = 0.3.

**Figure 11.**Equivalent factors of the neural network strategy, (

**a**) Recognition error of BP neural network; (

**b**) Allowable range of equivalent factor.

Item | Parameter | Symbol | Unit | Value |
---|---|---|---|---|

Vehicle | Mass | $m$ | kg | 1547 |

Windward area | $A$ | m^{2} | 2.28 | |

Wind drag coefficient | $Cd$ | - | 0.357 | |

Wheel radius | $r$ | m | 0.289 | |

Rolling resistance coefficient | $f$ | - | 0.0083 | |

Engine | Maximum power | ${P}_{e,\mathrm{max}}$ | kW | 70 |

Maximum torque | ${T}_{e,\mathrm{max}}$ | Nm | 137/3500 rpm | |

Speed range | ${\omega}_{e}$ | rpm | 800–6000 | |

ISG motor | Maximum power | ${P}_{m,\mathrm{max}}$ | kW | 40 |

Maximum torque | ${P}_{m,\mathrm{max}}$ | Nm | 140 | |

Speed range | ${\omega}_{m}$ | rpm | 0–6000 | |

Battery | Capacity | ${Q}_{0}$ | A·h | 40 |

Rated voltage | ${U}_{0}$ | V | 352 | |

CVT | Speed ratio range | ${i}_{cvt}$ | - | 0.422–2.432 |

Final drive ratio | ${i}_{0}$ | - | 5.297 |

Cycle Name | 11-SC03 | 9-HWFET | |||||
---|---|---|---|---|---|---|---|

Initial SOC | 0.9 | 0.6 | 0.3 | 0.9 | 0.6 | 0.3 | |

Fuel consumption (L) | DP | 1.131 | 2.947 | 4.943 | 5.297 | 5.602 | 6.093 |

DP-A-ECMS | 1.133 | 2.958 | 4.995 | 5.321 | 5.631 | 6.175 | |

% | 0.18 | 0.37 | 1.05 | 0.45 | 0.52 | 1.35 | |

Calculating time (min) | DP | 58.4 | 123.1 | ||||

DP-A-ECMS | 27.7 | 59.6 | |||||

% | −52.57 | −51.58 |

Method | Based on DP | Based on DP-A-ECMS | Fuel Saving Ratio (%) |
---|---|---|---|

Fuel consumption (L) | 4.437 | 4.342 | 2.46 |

Control Strategies | Rule-Based | Optimization-Based | BP Neural Network | |||
---|---|---|---|---|---|---|

CD-CS | Fuzzy Logic | A-ECMS | MPC | Based on DP | Based on DP-A-ECMS | |

Fuel consumption (L) | 5.519 | 5.487 | 4.318 | 4.324 | 4.437 | 4.342 |

Calculating time (ms) | 0.67 | 1.2 | 3.5 | 6.2 | 7.5 | 8.3 |

Robustness | very good | very good | very poor | very poor | relatively poor | relatively good |

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**MDPI and ACS Style**

Wang, S.; Qin, D.
Online Control Strategy for Plug-In Hybrid Electric Vehicles Based on an Improved Global Optimization Algorithm. *Appl. Sci.* **2020**, *10*, 8352.
https://doi.org/10.3390/app10238352

**AMA Style**

Wang S, Qin D.
Online Control Strategy for Plug-In Hybrid Electric Vehicles Based on an Improved Global Optimization Algorithm. *Applied Sciences*. 2020; 10(23):8352.
https://doi.org/10.3390/app10238352

**Chicago/Turabian Style**

Wang, Shaoqian, and Datong Qin.
2020. "Online Control Strategy for Plug-In Hybrid Electric Vehicles Based on an Improved Global Optimization Algorithm" *Applied Sciences* 10, no. 23: 8352.
https://doi.org/10.3390/app10238352