An efficient vehicle-following predictive energy management strategy for PHEV based on improved sequential quadratic programming algorithm

For the vehicle-following scenario, control design of plug-in hybrid electric vehicle (PHEV) needs to care about not only the efficient energy conversion, but also the driving safety by keeping an appropriate distance. Thus, how to obtain the optimal fuel economy under the premise of maintaining a s...

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Vydané v:Energy (Oxford) Ročník 219; s. 119595
Hlavní autori: Yang, Chao, Wang, Muyao, Wang, Weida, Pu, Zesong, Ma, Mingyue
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford Elsevier Ltd 15.03.2021
Elsevier BV
Predmet:
Algorithms
Driving ability
Electric motors
Electric vehicles
Energy
Energy conversion
Energy efficiency
Energy management
Energy management strategy
Fuel consumption
Fuel economy
Horizon
Hybrid electric vehicles
Model predictive control
Optimization
Plug-in hybrid electric vehicle
Powertrain
prediction
Predictions
Quadratic programming
Real time
SQP optimization
torque
Traffic speed
Vehicle safety
Vehicle-following
vehicles (equipment)
Model predictive control
Energy management strategy
SQP optimization
Plug-in hybrid electric vehicle
Vehicle-following
ISSN:0360-5442, 1873-6785
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Shrnutí:For the vehicle-following scenario, control design of plug-in hybrid electric vehicle (PHEV) needs to care about not only the efficient energy conversion, but also the driving safety by keeping an appropriate distance. Thus, how to obtain the optimal fuel economy under the premise of maintaining a safe following distance, is a challenging and hot issue for researchers, especially in the background of autonomous driving. Aiming at above problem, this paper proposes an efficient vehicle-following energy management strategy (EMS) for PHEVs based on model prediction control (MPC). In this strategy, the values of powertrain torque and vehicle speed are predicted in the given prediction horizon, and an improved sequential quadratic programming (ISQP) algorithm is proposed to solve the receding horizon optimization problem. The real-time efficiency of engine and electric motor are estimated through the calculation from last moment. The proposed EMS is verified by using the parameters of a real-world cargo truck equipped with parallel hybrid powertrain. The results show that the proposed strategy can ensure the vehicle driving safety while obtaining excellent fuel economy. Finally, the real-time capability of proposed strategy is verified in hardware-in-loop test environment. •An efficient vehicle-following predictive EMS for PHEV is presented.•The vehicle-following energy management problem is solved using MPC method.•A novel ISQP algorithm is proposed in the receding horizon.•The control performance is verified both in simulation and HIL system.
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SourceType-Scholarly Journals-1
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ISSN:0360-5442
1873-6785
DOI:10.1016/j.energy.2020.119595