Evaluating the impact of driveability requirements on the performance of an energy management control architecture for a hybrid electric vehicle

Cacciatori, Enrico

doi:10.1049/ic:20060590

Cited by 5 publications

(3 citation statements)

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“…• It is essential to meet end-user demands for increasingly complex new vehicles towards improving vehicle performance and driveability (Cacciatori et al, 2006), while continuing to reduce costs and meeting new emission standards.…”

Section: Wwwintechopencom Fast Model Predictive Control and Its Appmentioning

confidence: 99%

Fast Model Predictive Control and its Application to Energy Management of Hybrid Electric Vehicles

Assadian¹

2011

Advanced Model Predictive Control

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Section: Wwwintechopencom Fast Model Predictive Control and Its Appmentioning

confidence: 99%

Fast Model Predictive Control and its Application to Energy Management of Hybrid Electric Vehicles

Assadian¹

2011

Advanced Model Predictive Control

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“…In the field of HEV research, existing studies mostly specified fuel economy as the primary research objective [3,4], and several have focused on the emission characteristics of HEVs [5,6]. However, both the fuel economy and emission characteristic of HEVs are associated with relatively low-bandwidth nature, while the higher bandwidth nature, vehicle riding comfort, is often ignored by most existing HEV strategies [7]. As an essential vehicle performance index, the riding comfort of HEVs also deserves special consideration [8].…”

Section: Introductionmentioning

confidence: 99%

Jerk Analysis of a Power-Split Hybrid Electric Vehicle Based on a Data-Driven Vehicle Dynamics Model

et al. 2018

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Given its highly coupled multi-power sources with diverse dynamic response characteristics, the mode transition process of a power-split Hybrid Electric Vehicle (HEV) can easily lead to unanticipated passenger-felt jerks. Moreover, difficulties in parameter estimation, especially power-source dynamic torque estimation, result in new challenges for jerk reduction. These two aspects entangle with each other and constitute a complicated coupling problem which obstructs the realization of a valid anti-jerk method. In this study, a vehicle dynamics model with reference to a data-driven modeling method is first established, integrating a full-time artificial neural network engine dynamic model that can accurately predict engine dynamic torque. Then the essential reason for the occurrence of vehicle jerks in real driving conditions is analyzed. Finally, to smooth the mode transition process, a more practical anti-jerk strategy based on power-source torque changing rate limitation (TCRL) is proposed. Verification studies indicate that the data-driven vehicle dynamics model has enough accuracy to reflect the vehicle dynamic characteristics, and the proposed TCRL strategy could reduce the vehicle jerk by up to 85.8%, without any sacrifice of vehicle performance. This research provides a feasible method for precise modeling of vehicle dynamics and a reference for improving the riding comfort of hybrid electric vehicles.

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“…the vehicle jerk) into consideration. 6 As one of the essential indexes of the vehicle performance, the HEV driveability, whose dynamics are usually disregarded in the energy management strategy, also needs to be carefully controlled. 7 To achieve acceptable driveability for HEVs, transient control during the mode transition is rather challenging as it requires coordinated control of the engine, the electric machine (EM) and the transmission system.…”

Section: Introductionmentioning

confidence: 99%

Dynamic modelling and systematic control during the mode transition for a multi-mode hybrid electric vehicle

Zhu

Chen

Yin

et al. 2013

Proceedings of the Institution of Mechanical Engineers, Part D:

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A smooth mode transition is critical for the driveability of hybrid electric vehicles and is difficult to achieve owing to transient intervention of torques from the engine, the electric machine and the transmission. This paper presents a systematic control strategy during transition from the motor-only mode to the compound driving mode of a multi-mode hybrid electric vehicle using one electric machine. The proposed strategy divides the mode transition process into four consecutive operating phases and employs a fuzzy gain-scheduling proportional-integral-derivative controller as feedback to adapt various non-linearities. Dynamic modelling of the vehicle system emphasizing the dynamics of the engine, the transmission, the clutches and the drive axle is conducted for validation simulation. Simulation results show that the mode transition controlled by the designed fuzzy gain-scheduling proportional-integral-derivative controller is completed in 700 ms with the vehicle jerk below 3.5 m/s 3 and where the produced clutch frictional losses are only 56 J. The performance is better than those controlled by a conventional proportional-integral-derivative controller. A sensitivity study shows the proposed controller has good adaptability to disturbances caused by the changing road conditions, the uncertainties in engine system and the noise from clutch actuation.

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Evaluating the impact of driveability requirements on the performance of an energy management control architecture for a hybrid electric vehicle

Cited by 5 publications

References 0 publications

Fast Model Predictive Control and its Application to Energy Management of Hybrid Electric Vehicles

Fast Model Predictive Control and its Application to Energy Management of Hybrid Electric Vehicles

Jerk Analysis of a Power-Split Hybrid Electric Vehicle Based on a Data-Driven Vehicle Dynamics Model

Dynamic modelling and systematic control during the mode transition for a multi-mode hybrid electric vehicle

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