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ABSTRACT
The electric-hydraulic hybrid vehicle (EHHV) is an important research area of hybrid electric vehicles (HEV), which provides a competitive project compared to other hybrid technologies. This paper conducts comprehensive research on a master-slave electric-hydraulic hybrid vehicle (MSEHHV). After an integrated driving cycle, the battery state of charge (SOC) values for MSEHHV and electric vehicle (EV) are 44.65% and 38.27%. The economy of the MSEHHV is verified, which is obviously superior to the EV. To further explore the energy conservation potential of the MSEHHV, the research proposes a cooperative optimization method of powertrain parameters and control strategy. Specifically, the optimization objective is to improve SOC. The response surface method (RSM) fits the functional relation between design variables and optimization objective. An optimization model is constructed based on the response surface model. Ultimately, the particle swarm optimization (PSO) algorithm is used for the optimal solution to obtain the optimal parameter combination. To evaluate the adaptability of the method, the performance of three models in the actual driving cycle is compared. Simulation results suggest that the energy consumption of the optimized MSEHHV is 33.41% and 6.33% lower than that of EV and initial MSEHHV. The research provides a valuable reference for the optimal design of electric-hydraulic hybrid technology.
Nomenclature
| EV | = | HPA |
| HEV | = | Hybrid electric vehicle |
| EHHV | = | Electric-hydraulic hybrid vehicle |
| MSEHHV | = | Master-slave electric-hydraulic hybrid vehicle |
| SOC | = | State of charge |
| RSM | = | Response surface method |
| PSO | = | Particle swarm optimization algorithm |
| LHS | = | Latin hypercube sampling |
| HD | = | Hydraulic drive mode |
| ED | = | Electric drive mode |
| E-HD | = | Electric-hydraulic drive mode |
| HRB | = | Hydraulic regenerative braking mode |
| ERB | = | Electric regenerative braking mode |
| VCU | = | Vehicle control unit |
| HPA | = | The high-pressure accumulator |
| LPA | = | The low-pressure accumulator |
| HP/M | = | Hydraulic pump/motor |
| u | = | The velocity threshold |
| 10-15 | = | Japanese 10-15 mode cycle |
| HWFET | = | Highway fuel economy test |
| US06 | = | The US06 supplemental federal test procedure |
| SC03 | = | The SC03 supplemental federal test procedure |
| NEDC | = | New European driving cycle |
| u1 | = | The low-velocity threshold |
| u2 | = | The high-velocity threshold |
| pL | = | The lowest working pressure of the LPA |
| pH | = | The highest working pressure of the HPA |
Acknowledgements
The project is supported partly by the National Natural Science Foundation of China (No. 52075278), and the Municipal Livelihood Science and Technology Project of Qingdao (No. 19-6-1-92-nsh).
Disclosure statement
No potential conflict of interest was reported by the authors.
Additional information
Funding
Notes on contributors
Qingxiao Jia
Qingxiao Jia is a degree graduate student at the College of Mechanical and Electrical Engineering, Qingdao University. His main research interests are parameter matching, energy management strategy design and system optimization for new hybrid vehicles.
Caihong Zhang
Caihong Zhang is a lecturer at the College of Automation, Qingdao University. She specializes in control theory and control engineering.
Hongxin Zhang
Hongxin Zhang is the deputy dean and professor of the College of Mechanical and Electrical Engineering, Qingdao University. He mainly conducts the design and simulation of new power transmission technology for vehicles.
Zhen Zhang
Zhen Zhang is a degree graduate student at the College of Mechanical and Electrical Engineering, Qingdao University. His research focus on vehicle control systems and vehicle energy management strategy.
Hao Chen
Hao Chen is a degree graduate student at the College of Mechanical and Electrical Engineering, Qingdao University. His research direction is the performance prediction of a new type of electro-hydraulic hybrid electric vehicle.