Series Hybrid Electric Vehicle Simultaneous Energy Management and Driving Speed Optimization

Chen, Boli; Evangelou, Simos A.; Lot, Roberto

doi:10.1109/tmech.2019.2943320

Cited by 63 publications

(28 citation statements)

References 26 publications

(34 reference statements)

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“…In the study of serial hybrid powertrains, the combined optimization of an energy management strategy and driving speed was investigated for minimizing fuel consumption [61]. An energy management strategy based on the road condition and the driving time was proposed to find the optimal driving speed and energy power allocation for specified driving tasks.…”

Section: Series Hybrid Powertrainmentioning

confidence: 99%

Review and Development of Electric Motor Systems and Electric Powertrains for New Energy Vehicles

et al. 2021

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This paper presents a review on the recent research and technical progress of electric motor systems and electric powertrains for new energy vehicles. Through the analysis and comparison of direct current motor, induction motor, and synchronous motor, it is found that permanent magnet synchronous motor has better overall performance; by comparison with converters with Si-based IGBTs, it is found converters with SiC MOSFETs show significantly higher efficiency and increase driving mileage per charge. In addition, the pros and cons of different control strategies and algorithms are demonstrated. Next, by comparing series, parallel, and power split hybrid powertrains, the series–parallel compound hybrid powertrains are found to provide better fuel economy. Different electric powertrains, hybrid powertrains, and range-extended electric systems are also detailed, and their advantages and disadvantages are described. Finally, the technology roadmap over the next 15 years is proposed regarding traction motor, power electronic converter and electric powertrain as well as the key materials and components at each time frame.

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Section: Series Hybrid Powertrainmentioning

confidence: 99%

Review and Development of Electric Motor Systems and Electric Powertrains for New Energy Vehicles

et al. 2021

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“…since Φc P b dt ≤ 0. The condition (34) further implies that m f (T ) is minimized if Φ c = ∅, indicating that for an optimal EM the battery is never charged directly by the PS, instead, the battery is only replenished by regenerative braking. In the literature correction schemes have been employed when calculating the SS efficiency, for example by post-multiplication of the SS discharging efficiency with the PS average efficiency, such that future power losses of the PS for replenishing the consumed SOC in the SS are accounted for [40].…”

Section: A Driving Speed Optimization (Ocp-s)mentioning

confidence: 99%

“…However, the solution must be suboptimal as the conventional speed optimization methods do not incorporate the characteristics of the hybrid powertrain and power sharing strategies. To overcome the disadvantages of such a two-step optimization mechanism, simultaneous optimization of both driving speed and EM has also been suggested (see for example [32], [33], [34], [35]), which is expected to achieve better optimal solutions than the two-step counterpart. However, the computation complexity is increased when both aspects are simultaneously solved.…”

mentioning

confidence: 99%

Hybrid Electric Vehicle Two-Step Fuel Efficiency Optimization With Decoupled Energy Management and Speed Control

Chen

Evangelou

Lot

2019

IEEE Trans. Veh. Technol.

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Hybrid electric vehicles (HEVs) offer an effective solution for emissions reduction and fuel energy savings. The pursuit of further improvements in their energy efficiency has led to the two fundamental optimization challenges of vehicle speed and powertrain energy management (EM), which are inherently coupled. This paper examines the vehicle speed and powertrain EM co-optimization problem for fuel economy for a series HEV following a prescribed route with expected traveling time. In order to overcome the computational burden of a large scale optimal control problem (OCP), this work presents a novel twostep optimal control strategy that suitably separates the cooptimization problem on the basis of involving the characteristics of the HEV powertrain power split and losses in the speed optimization step without an explicit use of a powertrain model. A benchmark method that simultaneously solves the optimal driving speed and the energy source power split is introduced, which is used to show the solution quality of the proposed approach. It is illustrated that the proposed method yields a driving speed solution close to the benchmark method, and additionally it outperforms the benchmark fuel economy, with much higher computational efficiency. The simplicity and effectiveness of the proposed two-step approach make it a practical and implementable EM control strategy.

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“…Whereas for PHEV, the fuel consumption cannot be directly calculated according to the driving state (including driving speed, acceleration and SOC), due to the existence of two power sources in powertrain. Thus, the net energy consumed by the motion of PHEV is usually defined as the objective function [25]. In the second step, the energy management is optimized according to the velocity profiles generated in the first step.…”

Section: Introductionmentioning

confidence: 99%

Data-driven based eco-driving control for plug-in hybrid electric vehicles

Liu

Zhang

et al. 2021

Journal of Power Sources

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With the development of connected and automated vehicles, eco-driving control is reckoned to generate unprecedented potential on energy-saving in electrified powertrain. In this paper, a data-driven based eco-driving control strategy with efficient computation capacity is proposed for plug-in hybrid electric vehicles to achieve approximate optimal energy economy. An efficient hierarchical optimal control scheme is designed to mitigate the massive computational cost during velocity optimization and powertrain control. A data-driven optimal energy consumption cost model and an optimal battery current model are respectively constructed via two neural networks and served as the critic model and the system model during velocity optimization. Furthermore, the neural networkbased dynamic programming is exploited to optimize the vehicle velocity by merging the data-driven models and Bellman optimality principle. The simulation results demonstrate that the proposed method can remarkably improve fuel economy by up to 16.7% in complicated driving conditions, compared with conventional sequential optimization methods. Furthermore, the data-driven control scheme can drastically improve the computational efficiency with slight sacrifice on fuel economy, compared with the optimum benchmark.

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Series Hybrid Electric Vehicle Simultaneous Energy Management and Driving Speed Optimization

Cited by 63 publications

References 26 publications

Review and Development of Electric Motor Systems and Electric Powertrains for New Energy Vehicles

Review and Development of Electric Motor Systems and Electric Powertrains for New Energy Vehicles

Hybrid Electric Vehicle Two-Step Fuel Efficiency Optimization With Decoupled Energy Management and Speed Control

Data-driven based eco-driving control for plug-in hybrid electric vehicles

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