Simulation Research on Regenerative Braking Control Strategy of Hybrid Electric Vehicle
Abstract
:1. Introduction
2. System Analysis and Modelling
2.1. Structure of Braking Energy Recovery System
2.2. Motor Model
2.3. Power Battery Model
2.4. Engine Model
2.5. Vehicle Parameters
2.6. Braking Energy Recovery Control Strategy
3. Control Strategy of Braking Force Distribution for Front and Rear Axles
3.1. Relationship between Braking Strength and Braking Force Distribution Coefficient
- The braking strength should satisfy for the biaxial vehicle with an adhesion coefficient of 0.2~0.8;
- when the braking strength z is 0.15~0.80, the adhesion coefficient utilization curve of the front axle should be above the adhesion coefficient utilization curve of the rear axle under various loads;
- when the braking strength z is 0.30~0.45, if the adhesion coefficient utilization curve of the rear axle does not exceed 0.05 above the straight line determined by the formula , the adhesion coefficient utilization curve of the rear axle can be above the adhesion coefficient utilization curve of the front axle.
3.2. Braking Mode Division and Braking Force Distribution
4. Control Strategy of Electromechanical Braking Force Distribution for Front Axle
4.1. Design of Fuzzy Controller
4.2. Fuzzy Rules
4.3. Defuzzification
5. Simulation Results and Analysis
5.1. Simulation Analysis of Braking Energy Recovery Control Strategy for Hybrid Electric Vehicle
5.2. Comparative Analysis of Simulation Results of Different Braking Energy Recovery Control Strategies
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Parameters | Numerical Value | |
---|---|---|
Windward area (m2) | 2.638 | |
Wheelbase (m) | 2.710 | |
Vehicle fit-up quality (kg) | 1545 | |
Vehicle parameters | Rolling radius of tire (m) | 0.347 |
Height center of mass (m) | 0.650 | |
Drive Mode (FWD/AWD/RWD) | FWD (front drive) | |
Transmission ratio | 3.12 | |
Maximum Motor Power (kw) | 70 | |
Motor parameters | Maximum torque of motor (N·m) | 155 |
Maximum speed of motor (rpm) | 12,000 | |
Battery parameter | Battery capacity (Ah) | 75 |
Voltage Range (V) | 260~420 |
V | SOC | z | K | v | SOC | z | K | ||
---|---|---|---|---|---|---|---|---|---|
1 | L | H | L | VL | 15 | M | M | H | VL |
2 | L | H | M | VL | 16 | H | M | L | VH |
3 | L | H | H | VL | 17 | H | M | M | H |
4 | M | H | L | VL | 18 | H | M | H | VL |
5 | M | H | M | VL | 19 | L | L | L | M |
6 | M | H | H | VL | 20 | L | L | M | M |
7 | H | H | L | VL | 21 | L | L | H | VL |
8 | H | H | M | VL | 22 | M | L | L | H |
9 | H | H | H | VL | 23 | M | L | M | H |
10 | L | M | L | L | 24 | M | L | H | VL |
11 | L | M | M | M | 25 | H | L | L | VH |
12 | L | M | H | VL | 26 | H | L | M | H |
13 | M | M | L | VH | 27 | H | L | H | VL |
14 | M | M | M | VH |
Control Strategy | Control Strategy A | Control Strategy B | Control Strategy Proposed in This Paper | |||
---|---|---|---|---|---|---|
NEDC | WLTC | NEDC | WLTC | NEDC | WLTC | |
Total energy consumption of vehicles (KJ) | 4212.36 | 12,141.2 | 4201.19 | 12,043.53 | 4224.82 | 12,131.13 |
Total braking energy (KJ) | 1640.81 | 3677.4 | 1632.72 | 3646.52 | 1676.80 | 3681.21 |
Energy recovered by braking (KJ) | 603.25 | 1324.61 | 344.51 | 790.20 | 656.90 | 1420.34 |
Effective energy recovery rate (%) | 14.32 | 10.91 | 8.20 | 6.56 | 15.55 | 11.71 |
Braking energy recovery rate (%) | 36.76 | 36.02 | 21.10 | 21.67 | 39.18 | 38.58 |
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Geng, C.; Ning, D.; Guo, L.; Xue, Q.; Mei, S. Simulation Research on Regenerative Braking Control Strategy of Hybrid Electric Vehicle. Energies 2021, 14, 2202. https://doi.org/10.3390/en14082202
Geng C, Ning D, Guo L, Xue Q, Mei S. Simulation Research on Regenerative Braking Control Strategy of Hybrid Electric Vehicle. Energies. 2021; 14(8):2202. https://doi.org/10.3390/en14082202
Chicago/Turabian StyleGeng, Cong, Dawen Ning, Linfu Guo, Qicheng Xue, and Shujian Mei. 2021. "Simulation Research on Regenerative Braking Control Strategy of Hybrid Electric Vehicle" Energies 14, no. 8: 2202. https://doi.org/10.3390/en14082202