# Parameter Matching of Power Systems and Design of Vehicle Control Strategies for Mini-Electric Trucks

^{1}

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## Abstract

**:**

## 1. Introduction

## 2. Power System Parameter Design

#### 2.1. Basic Parameters and Performance Indicators

#### 2.2. Selection of Operation Conditions

#### 2.3. Calculation of Motor Parameters

_{veh}is the power required to drive the whole vehicle, kW; m is mass, kg; g is gravitational acceleration, m/s

^{2}; f is rolling resistance coefficient; u

_{a}is speed, km/h; θ is climbing angle, θ=arctan i; C

_{D}is drag coefficient; A is the windward area, m

^{2}; δ is conversion coefficient of rotating mass; a is acceleration, m/s

^{2}; η

_{T}is mechanical transmission efficiency. The air density is taken as 1.29 kg/m

^{3}, and to convert the units of vehicle speed from km/h to m/s, it needs to be multiplied by the conversion factor of 1/3.6. Through dimensional analysis of the expression, the value 21.15 is obtained.

_{a}is 10 km/h, and climbing gradient is 35%.

_{1}is the acceleration time under full load from 0 to 100 km/h, and u

_{t}is the final speed reached after acceleration.

_{max}is the maximum power of the motor, kW; λ is the overload coefficient of the motor, with its value between 2 and 3. Taking λ as 3, the calculated rated power is 80 kW.

#### 2.4. Determination of Transmission System Parameters

_{0}is taken as 6.058.

#### 2.5. Determination of Power Battery Parameters

## 3. Establishment of the Power Model Based on CRUISE Software

## 4. Vehicle Control Strategy Design

#### 4.1. Vehicle Control Strategy Modeling

#### 4.2. Implementation of Brake Priority

#### 4.3. Implementation of Prohibiting Vehicle Driving on Charging

#### 4.4. Implementation of Vehicle Driving

#### 4.5. Implementation of Fault Handling

#### 4.6. Implementation of Drive Control

#### 4.7. Implementation of Braking Energy Recovery

## 5. Analysis of Simulation Results

## 6. Performance Test

#### 6.1. Maximum Speed Test

_{1}and t

_{2}, respectively. The Formula for the maximum speed is as follows.

#### 6.2. Acceleration Test

## 7. Theoretical Calculation of Dynamic Performance

_{max}is the maximum speed, n is the speed of the drive motor, i

_{0}is the ratio of the main reducer, i

_{g}ratio of the transmission, and i

_{g}= 1. The calculation results show that the maximum speed of the mini−electric trucks is 186.69 km/h, which is close to the simulation results in CRUISE simulation software, which proves that the simulation results are accurate. At the same time, it is proved that the design is reasonable and meets the requirements of dynamic performance indicators.

_{max}is the maximum climbing angle, T

_{tq}is the torque of the motor, i

_{0}is the ratio of the main reducer, i

_{g}ratio of the transmission, i

_{g}= 1, f is the coefficient of rolling resistance, m is mass, g is the acceleration of gravity, and r is Radius of the wheel. The calculated maximum climbable gradient is 39.8%, which is close to 40.34 calculated by simulation software, and meets the requirement that the maximum climb climbable gradient is no less than 35%.

## 8. Analysis of Dynamic Performance

## 9. Analysis of Economy

## 10. Conclusions

- (1)
- According to the power demand of the whole vehicle, to enhance the accuracy of dynamic parameters of mini−electric trucks, combining the characteristics of mini trucks, the parameters of the driving motor, power battery, and the transmission ratio of the main reducer are designed using the automobile theory and the types of drive motors and power batteries are selected, the dynamic model of mini−electric trucks is established.
- (2)
- The vehicle control strategy including how working condition switching is developed according to the work conditions of mini−electric trucks, and the control strategy model is built using the simulation software, and the realization method of each function of the control strategy and the realization process of each function are analyzed.
- (3)
- According to the matched parameters of the power system, the power performance is calculated theoretically.
- (4)
- The dynamic performance is analyzed through the simulation results were compared with theoretical calculation and performance tests results respectively, the comparison results show an error rate of maximum speed, acceleration time and maximum gradient between simulation results and test results are 0.641% and 5.63% (15.328%), respectively, the dynamic index have reached the expected value, it proves that the design method of dynamic parameters is reasonable, and the dynamic performance is analyzed through co−simulation, endurance mileage is up to 295 Km under UDC conditions, increased by 5% after the vehicle control strategy adopted. The economy is improved. It has practical guiding significance for the power system design of mini−electric trucks.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

- Yang, G. Development trend of mini truck products. Automob. Parts
**2016**, 21, 52–53. [Google Scholar] - Lu, Y.; Liu, L. Risk identification and prevention of vertical fresh e-commerce logistics supply chain operation. J. Commer. Econ.
**2023**, 6, 88–89. [Google Scholar] - Ma, R.C. Study on the construction of cross-border e-commerce logistics service system in the new retail era. Trade Fair Econ.
**2023**, 4, 58–60. [Google Scholar] - Jiang, D.L.; Song, L.Y.; Wang, L. Analysis on Impact of Energy Consumption and Carbon Emission of New Energy Vehicles Based on GREET Software. Automob. Appl. Technol.
**2022**, 47, 15–21. [Google Scholar] - Han, J. China’s new energy vehicle development present situation and explored. Auto Time
**2019**, 15, 75–76. [Google Scholar] - Zhu, J. Development prospects of new energy vehicles. Auto Time
**2021**, 17, 75–76. [Google Scholar] - Zhai, J.X.; Liu, X.M.; Li, C.D.; Yang, X.J. Matching design of power system of pure electric truck. Heavy Truck
**2022**, 5, 15–17. [Google Scholar] - Li, X. Debate on development route of pure electric vehicle. Commer. Automob. News
**2018**, 9, 26–29. [Google Scholar] - Guo, D.; Zhang, H.H.; Zheng, C.Y. Future development forecast of Chinese automobile and benefits analysis of energy saving and emission reduction. Syst. Eng. Theory Pract.
**2016**, 36, 7–11. [Google Scholar] - Lin, X.; Feng, Q.; Mo, L.; Li, H. Optimal adaptation equivalent factor of energy management strategy for plug-in CVT HEV. Proc. Inst. Mech. Eng. Part D J. Automob. Eng.
**2019**, 233, 877–889. [Google Scholar] [CrossRef] - Ritari, A.; Vepsäläinen, J.; Kivekäs, K.; Tammi, K.; Laitinen, H. Energy consumption and lifecycle cost analysis of electric city buses with multispeed gearboxes. Energies
**2020**, 13, 2117–2124. [Google Scholar] [CrossRef] - Qu, J.Y.; Liu, D.C. Parameter Matching and Simulation of Pure Electric Bus Power System. Bull. Sci. Technol.
**2022**, 38, 93–97. [Google Scholar] - Prochazka, P.; Pazdera, I.; Vorel, P. Design of Small Electric Car. In Proceedings of the International Symposium on Power Electrical Drives, Automation and Motion, Anacapri, Capri Island, Italy, 11–13 June 2018; pp. 359–364. [Google Scholar]
- Kim, J. Optimal power distribution of front and rear motors for minimizing energy consumption of 4-wheel-drive electric vehicles. Int. J. Automot. Technol.
**2016**, 17, 319–326. [Google Scholar] [CrossRef] - Yang, G.; Zhang, A.; Li, S.; Wang, Y.; Wang, Y.; Xie, Q.; He, L. Multi-objective evolutionary algorithm based on decision space partition and its application in hybrid power system optimization. Appl. Intell.
**2017**, 46, 827–844. [Google Scholar] [CrossRef] - Qin, D. Dynamic Drive Control Strategy for Pure Electric Vehicle Based on Driver Intention Recognition. Automot. Eng.
**2015**, 37, 26–32. [Google Scholar] - Lian, J.; Li, L.H.; Liu, X.Z.; Huang, H.; Zhou, Y.; Han, H. Research on adaptive control strategy optimization of hybrid electric vehicle. J. Intell. Fuzzy Syst.
**2016**, 30, 2581–2592. [Google Scholar] [CrossRef] - Boukehili, A.; Zhang, Y.T.; Zhao, Q.; Ni, C.Q.; Su, H.F.; Huang, G.J. Hybrid Vehicle Power Management Modeling and Refinement. Int. J. Automot. Technol.
**2012**, 13, 987–998. [Google Scholar] - Niu, J.G.; Xu, C.H.; Niu, D.T.; Pei, F.L. Comparison of regular energy management strategies for extended range electric vehicles. J. Jiangsu Univ. (Nat. Sci. Ed.)
**2018**, 39, 266–272+295. [Google Scholar] - Liu, H.W.; Lei, Y.L.; Fu, Y.; Li, X.Z. Parameter matching and optimization for power system of range-extended electric vehicle based on requirements. Proc. Inst. Mech. Eng. Part D–J. Automot. Eng.
**2020**, 234, 3316–3328. [Google Scholar] [CrossRef] - Li, Z.Y. Energy Recovery of Electric Vehicle Base on NEDC Cycle. Automob. Appl. Technol.
**2021**, 46, 1–4. [Google Scholar] - Ma, J.A. Parameter matching study for power system of electric sweeping vehicle based on AVL CRUISE. Int. J. Electr. Hybrid Veh.
**2020**, 12, 144–157. [Google Scholar] - Yu, Z. Automobile Theory; Machinery Industry Press: Beijing, China, 2009. [Google Scholar]
- Zhao, Y.Q.; Lin, F. Automotive System Dynamics; Science Press: Beijing, China, 2017. [Google Scholar]
- Xu, Z. Driving scheme analysis and parameter matching of a pure electric vehicle based on AVL CRUISE. Automob. Pract. Technol.
**2019**, 23, 41–45. [Google Scholar] - You, Y.; Sun, D.Y.; Qin, D.T.; Wu, B.Z.; Feng, J.H. A new continuously variable transmission system parameters matching and optimization based on wheel loader. Mach. Mach. Theory
**2020**, 150, 103876. [Google Scholar] - Kui, H.L.; Wang, Z.Z.; Zhang, J.Z.; Liu, Y. Transmission ratio and energy management strategy of fuel cell vehicle based on AVL-Cruise. J. Jilin Univ. (Eng. Technol. Ed.)
**2022**, 52, 2119–2129. [Google Scholar] - Guo, H.L.; Kong, W.S.; Yan, Y.C.; Chen, F.; Zhang, C.Z. Parameter Matching Analysis of Energy-Saving Vehicle Power System Based on AVL-Cruise. Agric. Equip. Veh. Eng.
**2022**, 60, 44–49. [Google Scholar] - Tian, Y.; Yao, Q.; Hang, P.; Wang, S. Adaptive Coordinated Path Tracking Control Strategy for Autonomous Vehicles with Direct Yaw Moment Control. Chin. J. Mech. Eng.
**2022**, 35, 1. [Google Scholar] [CrossRef] - Gong, C.Z.; Wu, D.; Yu, Y.; Zhang, Y. Research on Power Performance Test and Data Analysis Method of Electric Vehicle. China Auto
**2022**, 12, 44–52. [Google Scholar] - Jiang, Z. Control System Modeling and Simulation-Analysis and Implementation Based on MATLAB/Simulink; Tsinghua University Press: Beijing, China, 2020. [Google Scholar]
- Wang, Z.; Zang, L.G.; Jiao, J.; Mao, Y.L. Research on Hierarchical Control Strategy of Automatic Emergency Braking System. World Electr. Veh. J.
**2023**, 14, 97. [Google Scholar] [CrossRef] - Agyeman, P.K.; Tan, G.F.; Alex, F.J.; Valiev, F.J.; Owosu-Ansah, P.; Olayode, I.O.; Hassan, M.A. Parameter Matching, Optimization, and Classification of Hybrid Electric Emergency Rescue Vehicles Based on Support Vector Machines. Energies
**2022**, 15, 7071. [Google Scholar] [CrossRef] - Liao, Y.T.; Wu, L.; Hou, T.T. Comparative Study on Battery Electric Vehicles and Fuel Vehicles. Automot. Eng.
**2021**, 28, 40–44. [Google Scholar] - Zhai, X.Z.; Han, T.Q. Analysis of Economic and Power Performance of Electric Vehicle Based on Cruise–Carsim Joint Simulation. J. Hubei Univ. Automot.
**2021**, 35, 40–43+80. [Google Scholar]

**Figure 7.**Braking priority part model: (

**a**) braking priority processing; (

**b**) torque required calculation model.

Items | Length/Width/Height (mm) | Curb Weight (kg) | Max Total Mass (kg) | Air Resistance Coefficient | Windward Area (m^{2}) | Rolling Radius (m) | Rolling Resistance Coefficient | Transmission Efficiency (%) |
---|---|---|---|---|---|---|---|---|

Index | 4995/2050/1900 | 1185 | 2505 | 0.8 | 2.5 | 0.301 | 0.016 | 94 |

Performance Indicators | Maximum Speed | Maximum Climbable Gradient | Acceleration Time | Endurance Mileage | |
---|---|---|---|---|---|

0–50 km/h | 0–100 km/h | ||||

Value of parameter | 180 km/h | 35% | 6 s | 14 s | 280 |

Parameters | Rated Power (kW) | Peak Power (kW) | Rated Torque (N∙m) | Peak Torque (N∙m) | Rated Speed (r/min) | Peak Speed (r/min) | Rated Voltage (V) | Efficiency (%) |
---|---|---|---|---|---|---|---|---|

Value | 80 | 240 | 90 | 240 | 3000 | 10,000 | 320 | 0.9 |

Items | Battery Type | Nominal Voltage | Capacity | Max Continuous Charging/Discharge Current | Max Transient Charging/Discharge Current | Monomer Mass | Cyclic-Life (80%) |
---|---|---|---|---|---|---|---|

Index | Lithium iron phosphate | 3.2 V | 50 Ah | 15 A/50 A | 50 A/50 A | 1.5 kg | 2000 times |

Serial Number | Direction | Driving Distance (m) | Time (s) | Average Speed (km/h) |
---|---|---|---|---|

1 | Positive | 1000 | 19.37 | 185.86 |

2 | Reverse | 1000 | 19.37 |

Serial Number | Direction | Time (s) | Average Time (s) |
---|---|---|---|

1 | Positive | 4.95 | 4.965 |

2 | Reverse | 4.98 |

Serial Number | Direction | Time (s) | Average Time (s) |
---|---|---|---|

1 | Positive | 13.692 | 13.688 |

2 | Reverse | 13.685 |

Maximum Speed (km/h) | Acceleration Time from 0 to 50 km/h (s) | Acceleration Time from 0 to 100 km/h (s) | Maximum Gradient (%) | |
---|---|---|---|---|

Simulation results | 187 | 4.7 | 11.59 | 40.34 |

Theoretical calculation results | 186.69 | — | — | 39.8 |

Test results | 185.8 | 4.965 | 13.688 | — |

Error rate (%) between simulation results and theoretical calculation results | 0.166 | — | — | 1.35 |

Error rate (%) between simulation results and test results | 0.641 | 5.63 | 15.328 | — |

Time (s) | Signal status | Mode |
---|---|---|

0–12 | err_flg = 0; BPS_Brake_Enble = 1; APS_Brake_Enble = 0 | Neutral |

13–28 | err_flg = 0; APS_Brake_Enble = 0; Gear_position = 1; Acc_pedal = 1 | Motor drive |

29–32 | BPS_Brake_Enble = 1; Gear_position = 0; Acc_pedal = 0 | Brake energy recovery |

32–45 | err_flg = 0; BPS_Brake_Enble = 1; APS_Brake_Enble = 0 | Neutral mode |

45–74 | err_flg = 0; APS_Brake_Enble = 0; Gear_position = 1; Acc_pedal = 1 | Motor drive mode |

74–82 | err_flg = 0; BPS_Brake_Enble = 1; Gear_position = 0; Acc_pedal = 0 | Brake energy recovery |

82–117 | err_flg = 0; BPS_Brake_Enble = 1; APS_Brake_Enble = 0 | Neutral |

117–154 | err_flg = 0; APS_Brake_Enble = 0; Gear_position = 1; Acc_pedal = 1 | Motor drive |

160–165 | err_flg = 0; BPS_Brake_Enble = 1; APS_Brake_Enble = 0 | Neutral |

165–175 | err_flg = 0; APS_Brake_Enble = 0; Gear_position = 1; Acc_pedal = 1 | Motor drive |

175–188 | err_flg = 0; BPS_Brake_Enble = 1; Gear_position = 0; Acc_pedal = 0 | Brake energy recovery |

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**MDPI and ACS Style**

Ma, J.; Gu, F.; Feng, Z.; Zhang, S.
Parameter Matching of Power Systems and Design of Vehicle Control Strategies for Mini-Electric Trucks. *World Electr. Veh. J.* **2023**, *14*, 207.
https://doi.org/10.3390/wevj14080207

**AMA Style**

Ma J, Gu F, Feng Z, Zhang S.
Parameter Matching of Power Systems and Design of Vehicle Control Strategies for Mini-Electric Trucks. *World Electric Vehicle Journal*. 2023; 14(8):207.
https://doi.org/10.3390/wevj14080207

**Chicago/Turabian Style**

Ma, Jianwei, Fengyi Gu, Ziliang Feng, and Shaohang Zhang.
2023. "Parameter Matching of Power Systems and Design of Vehicle Control Strategies for Mini-Electric Trucks" *World Electric Vehicle Journal* 14, no. 8: 207.
https://doi.org/10.3390/wevj14080207