Integration of On-Line Control in Optimal Design of Multimode Power-Split Hybrid Electric Vehicle Powertrains

Anselma, Pier Giuseppe; Huo, Yi; Roeleveld, Joel; Belingardi, Giovanni; Emadi, Ali

doi:10.1109/tvt.2019.2901901

Cited by 27 publications

(11 citation statements)

References 41 publications

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“…With predetermined design indexes, the increase (decrease) in vehicle curb mass will result in the increase (decrease) in the total mass of motors and battery. The first formula in (8) clearly reflects this rule. Therefore, the total mass change of the front and rear motors is less than the change in the designed vehicle curb mass during the iteration.…”

Section: Mass Closed-loop Algorithm Modelmentioning

confidence: 73%

“…(1)-(7), a nonlinear system of Eqs. (8) involving m v (k+1) and m m (k) can be derived. This system of equations is difficult to solve analytically.…”

Section: Mass Closed-loop Algorithm Modelmentioning

confidence: 99%

“…So far, plenty of works have been focused on optimization of power system parameters and control strategy parameters [7][8][9][10]. In terms of power system parameter optimization, Lei et al [11] adopted annealing particle swarm optimization for the parameter optimization of hybrid electric vehicles power system, and improved fuel economy.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Parameter closed-loop optimization for pure electric vehicles: unified design of power system and control parameters

Zhang

Yang

Zhou³

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

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To solve the problem of unreasonable vehicle parameters caused by unknown curb mass and open-loop power system and control strategy optimization in the development of pure electric vehicles, this paper presents a parameter closed-loop optimization algorithm (COA) via unified design of system and control parameters. First, a mass closed-loop algorithm (MCA) was adopted to optimize the parameters of power systems under an unknown curb mass and its convergence was proven. Next, with energy consumption being the index function, the torque distributions of the front and rear motors were optimized by dynamic programming (DP). Additionally, vehicle power system and control strategy parameter optimization were realized by combining the MCA, DP, and genetic algorithm. Finally, two comparative optimization algorithms which are the assumed curb mass optimization algorithm (AOA) and the torque equal ratio distribution optimization algorithm (TOA) were implemented to validate the proposed algorithm. The simulation results under China light-duty vehicle test cycle-passenger car (CLTC-P) and New European Driving Cycle (NEDC) operating conditions indicate that the proposed algorithm achieves minimum energy consumption compared with the competing optimization algorithms. The energy consumption resulting from the COA is reduced by 18.98% and 6.36%, compared with those of the TOA and AOA, respectively, under CLTC-P conditions. The energy consumption of the COA is 16.57% and 6.91% lower than those of the TOA and AOA, respectively, under NEDC conditions. This algorithm can optimize the curb mass, peak powers of the motors, mass of the motors, battery energy, battery mass, and torque distribution coefficient simultaneously, and can be applied to different operating conditions.

show abstract

Section: Mass Closed-loop Algorithm Modelmentioning

confidence: 73%

“…(1)-(7), a nonlinear system of Eqs. (8) involving m v (k+1) and m m (k) can be derived. This system of equations is difficult to solve analytically.…”

Section: Mass Closed-loop Algorithm Modelmentioning

confidence: 99%

See 1 more Smart Citation

Parameter closed-loop optimization for pure electric vehicles: unified design of power system and control parameters

Zhang

Yang

Zhou³

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

View full text Add to dashboard Cite

show abstract

“…Due to the low dependency on fossil fuels [14], electric vehicles (EVs), especially hybrid electric vehicles, are recognized as alternatives for conventional vehicles. Hybrid electric vehicles can be commonly classified into three types: series hybrid electric vehicles (SHEVs), parallel hybrid electric vehicles (PHEVs), and series parallel hybrid electric vehicles (SPHEVs) [15].…”

Section: Power System Configurationmentioning

confidence: 99%

Advanced Power Management and Control for Hybrid Electric Vehicles: A Survey

Jiang

Chen

et al. 2021

Wireless Communications and Mobile Computing

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With the trend of low emissions and sustainable development, the demand for hybrid electric vehicles (HEVs) has increased rapidly. By combining a conventional internal combustion engine with one or more electric motors powered by a battery, HEVs have the advantages over traditional vehicles in better fuel economy and lower tailpipe emissions. Nevertheless, the power management strategies (PMSs) for conventional vehicles which mainly focus on the efficiency of internal combustion engine are no longer applicable due to the complex internal structure of HEVs. Hence, a large number of novel strategies appropriate for HEVs have been surveyed, but most of the researches concentrate on discussing the classifications of PMSs and comparing their cons and pros. This paper presents a comprehensive review of power management strategies adopted in HEVs aiming at specific challenges for the first time. The categories of the existing PMSs are presented based on the different algorithms, followed by a brief study of each type including the analysis of its pros and cons. Afterwards, the implementation and optimization of power management strategies aiming at proposed challenges are introduced in detail with the description of their optimization objectives and optimized results. Finally, future directions and open issues of PMSs in HEVs are discussed.

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“…For example, many control methods are proposed to optimize the power distribution [7,8]. Anselma et al [9] developed a machine learning logic based on supervised learning to get the optimal power-split. Fan et al [10] proposed an efficiency-based evaluation real-time control strategy to obtain near-optimal fuel economy, which is validated using a mull-mode power-split hybrid track-type dozer.…”

Section: Introductionmentioning

confidence: 99%

Model-Based Double Closed-Loop Coordinated Control Strategy for the Electro-Mechanical Transmission System of Heavy Power-Split HEVs

Wang

Zhang

Sun³

et al. 2021

Automot. Innov.

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The dual-mode electro-mechanical transmission (EMT) system is a crucial part of power-split hybrid electric vehicles (HEVs), especially for the heavy HEVs. To improve the precision of the system power distribution and the response speed of the electric power supply, a model-based double closed-loop coordinated control strategy is proposed. As the basis of the proposed control strategy, an EMT system model, particularly of an electrical system, is established first. The proposed control strategy includes the power distribution strategy, battery power closed-loop feedback control strategy, and motor coordinated control strategy. To verify the feasibility of the proposed control strategy, simulation and experiment are performed. The results indicate that the proposed control strategy can realize the expected power distribution by coordinating generators and motors and achieve rapid and stable electric power supply.

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Integration of On-Line Control in Optimal Design of Multimode Power-Split Hybrid Electric Vehicle Powertrains

Cited by 27 publications

References 41 publications

Parameter closed-loop optimization for pure electric vehicles: unified design of power system and control parameters

Parameter closed-loop optimization for pure electric vehicles: unified design of power system and control parameters

Advanced Power Management and Control for Hybrid Electric Vehicles: A Survey

Model-Based Double Closed-Loop Coordinated Control Strategy for the Electro-Mechanical Transmission System of Heavy Power-Split HEVs

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