Equivalent consumption minimization strategy of hybrid electric vehicle considering the impact of driving style

Gong, Changchao; Hu, Minghui; Li, Shuxian; Zhan, Sen; Qin, Datong

doi:10.1177/0954407018803768

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…An instantaneous cost function can be evaluated as the sum of the two energy contributions (fuel and electricity), weighted by means of a dedicated equivalence factor s. 39 The control decision is achieved at each time instant by setting values of control variables corresponding to the minimum of the cost function. 40 The equivalence factor s is kept as constant in the traditional version of ECMS, while it is varied over time in the A-ECMS. An A-ECMS approach for hybrid electric powertrains was initially proposed by Musardo et al 41 by adapting s over time according to the current value of battery SOC, paving the way for copious similar approaches developed in the following decade.…”

Section: A-ecms Race Strategy For Formula-ementioning

confidence: 99%

Optimal adaptive race strategy for a Formula-E car

Anselma

2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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Appropriately managing battery state-of-charge and temperature while ensuring minimized lap time represents a crucial issue in Formula-E competitions. An open research question might relate to simultaneously guarantee near-optimality in the race strategy solution, computational light-weighting, and effective adaptability with respect to varying and unpredictable race conditions. In this paper, a novel near-optimal real-time capable Formula-E race controller is introduced that takes inspiration from the adaptive equivalent consumption minimization strategy (A-ECMS) approach. A reduced-order Formula-E car plant model is detailed first. The optimal Formula-E race problem subsequently discussed involves controlling at each lap the depletable battery energy, the thermal management mode, and the race mode in order to minimize the overall race time. Moreover, avoiding excessively depleting the battery energy and overheating the battery are considered as constraints for the race optimization problem. Dynamic programing (DP) is implemented first to obtain the global optimal Formula-E race strategy solution in an off-line control approach. The proposed real-time capable A-ECMS based race controller finds then detailed illustration. The flexibility of the introduced A-ECMS Formula-E race controller is guaranteed by optimally calibrating the related equivalence factors to adapt to the current vehicle states (i.e. battery state-of-charge, battery temperature, and lap number). Simulation results for the Marrakesh e-prix considering different race scenarios in terms of battery initial temperature and Safety car entry demonstrate that the estimated race time achieved by the A-ECMS race controller is always near-optimal being 1.7% higher at most compared with the corresponding global optimal benchmark provided by DP.

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Section: A-ecms Race Strategy For Formula-ementioning

confidence: 99%

Optimal adaptive race strategy for a Formula-E car

Anselma

2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…In recent years, hybrid electric vehicles (HEVs) have been recognized as promising technologies to replace the conventional internal-combustion engine (ICE)-drive road vehicles, improving fuel economy, and reducing harmful tailpipe emissions [1][2][3][4]. The HEV has become a worldwide research hotspot.…”

Section: Introductionmentioning

confidence: 99%

Development of Test-Bed Controller for Powertrain of HEV

et al. 2020

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The dynamic test-bed is a powerful tool for the powertrain integration and control strategy development of a hybrid electric vehicle (HEV). This paper focuses on developing a test-bed controller with driver simulation, road load simulation (RLS), and engine simulation. The main factors that influence the RLS accuracy are analyzed, especially inertia of test-bed and torque signal sampling frequency, and the RLS algorithm with penalty function based on a forward model is proposed. The engine model is developed and permanent magnet synchronous motor (PMSM) is adopted to realize engine start-up/stop, torque control, and inertia simulation. The simulation platform was built in MATLAB/Simulink to verify its accuracy. The simulation results present the developed RLS with a penalty function based on a forward model that can reduce the speed and torque error. The developed controller is applied to the single-axis parallel HEV test-bed. The experiment results show that the developed test-bed controller can precisely emulate the road load and improve the efficiency of the development of HEV powertrain.

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Optimization of energy management strategy for fuel cell/battery/ultracapacitor hybrid electric vehicle using distributed interior point

Tao,

Chen,

et al. 2024

Electric Power Systems Research

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Equivalent consumption minimization strategy of hybrid electric vehicle considering the impact of driving style

Cited by 5 publications

References 21 publications

Optimal adaptive race strategy for a Formula-E car

Optimal adaptive race strategy for a Formula-E car

Development of Test-Bed Controller for Powertrain of HEV

Optimization of energy management strategy for fuel cell/battery/ultracapacitor hybrid electric vehicle using distributed interior point

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