A Data-driven Online Identification and Control Optimization Approach applied to a Hybrid Electric Powertrain System

Marx, Matthias; Shen, Xi; Söffker, Dirk

doi:10.3182/20120215-3-at-3016.00027

Cited by 11 publications

(4 citation statements)

References 21 publications

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“…(3) m i , c i and k i are the mass, damping coefficient, and stiffness coefficient of the elastic element on the i-th path, respectively; j is the imaginary part of the complex number. MB is suitable for various elastic and rigid connections [8]. The approximate value of dynamic stiffness within a certain frequency band in MB is:…”

Section: Load Model Determinationmentioning

confidence: 99%

Noise analysis of interior structure under powertrain excitation

Bai,

Wei,

Chen

2024

J. Phys.: Conf. Ser.

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To accurately identify the structural noise problem caused by vibration in the cockpit, an experimental study was conducted on a certain type of SUV under uniform acceleration conditions. Based on the planning of the multi-body system dynamics model, a transfer path model with secondary contributions is constructed, and the load is identified using the single degree of freedom model and multi-frequency bandwidth model under the OPAX method based on this path. The results indicate that the second-order vibration of the powertrain is transmitted to the entire suspension system through the X direction of the right suspension. The second-order vibration of the suspension system is then transmitted to the Z direction of the right rear bolt of the subframe, causing the entire subframe vibration and ultimately reaching the target point. This structural path has the greatest impact on the interior noise of the vehicle. This path research method can more accurately determine the main excitation points that cause interior noise problems on a certain path and better understand the overall energy transfer characteristics along the transmission path.

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Section: Load Model Determinationmentioning

confidence: 99%

Noise analysis of interior structure under powertrain excitation

Bai,

Wei,

Chen

2024

J. Phys.: Conf. Ser.

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“…Application of neural network models in predictive control for automotive application has been explored frequently in literature [6] [7] [8]. For HEV applications, neural network based model predictive control has primarily been used for design of energy management controllers [9] [10]. The application of 10/31/2017 neural network hybrid powertrain models in vehicle dynamics MPC has not been previously studied.…”

Section: Introductionmentioning

confidence: 99%

Powertrain Modeling and Model Predictive Longitudinal Dynamics Control for Hybrid Electric Vehicles

Hosking

McPhee

2018

SAE Technical Paper Series

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This paper discusses modeling of a power-split hybrid electric vehicle and the design of a longitudinal dynamics controller for the University of Waterloo's self-driving vehicle project. The powertrain of Waterloo's vehicle platform, a Lincoln MKZ Hybrid, is controlled only by accelerator pedal actuation. The vehicle's power management strategy cannot be altered, so a novel approach to greybox modeling of the OEM powertrain control architecture and dynamics was developed. The model uses a system of multiple neural networks to mimic the response of the vehicle's torque control module and estimate the distribution of torque between the powertrain's internal combustion engine and electric motors. The vehicle's power-split drivetrain and longitudinal dynamics were modeled in MapleSim, a modeling and simulation software, using a physics-based analytical approach. All model parameters were identified using Controller Area Network (CAN) data and measurements of wheel torque data that were gathered during vehicle road testing. Using the grey-box powertrain model as a framework, a look-ahead linear time-varying (LTV) model predictive controller (MPC) for reference velocity tracking is proposed. Using some simplifying assumptions about the powertrain dynamics, the controloriented model was reformulated in a pseudo-Hammerstein form. Inversion of the nonlinearities allows linear MPC algorithms to be applied directly to the linear portion of the system. The performance of the MPC was tested using multiple model in the loop (MIL) reference velocity tracking scenarios, and benchmarked against a tuned proportional-integral (PI) controller. Using the novel controloriented model of the OEM powertrain, the MPC was found to track the desired velocity trajectory and reject measurable disturbance inputs, such as road slope, better than the PI controller.

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“…Without the information available from telemetry, prediction strategies based on neural networks and stochastic Markov chain have been considered in [49] within an MPC framework. Past trajectories have been used for prediction, as considered in [48,50,51]. Here, a prediction algorithm considers certain features of the past trajectory measured over predefined time horizons.…”

Section: Introductionmentioning

confidence: 99%

“…Use of GPS for the knowledge of obstacles to come and the assumption of drive cycle being provided as a reference were one of the key aspects here. Apart from GPS, track-based prediction [47] and dynamic recurrent neural networks (DRNN) [48] have also been applied. Without the information available from telemetry, prediction strategies based on neural networks and stochastic Markov chain have been considered in [49] within an MPC framework.…”

Section: Introductionmentioning

confidence: 99%

Optimal Rule-Based Power Management for Online, Real-Time Applications in HEVs with Multiple Sources and Objectives: A Review

Moulik

Söffker

2015

Energies

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The field of hybrid vehicles has undergone intensive research and development, primarily due to the increasing concern of depleting resources and increasing pollution. In order to investigate further options to optimize the performance of hybrid vehicles with regards to different criteria, such as fuel economy, battery aging, etc., a detailed state-of-the-art review is presented in this contribution. Different power management and optimization techniques are discussed focusing on rule-based power management and multi-objective optimization techniques. The extent of rule-based power management and optimization in solving battery aging issues is investigated along with an implementation in real-time driving scenarios where no pre-defined drive cycle is followed. The goal of this paper is to illustrate the significance and applications of rule-based power management optimization based on previous contributions.

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A Data-driven Online Identification and Control Optimization Approach applied to a Hybrid Electric Powertrain System

Cited by 11 publications

References 21 publications

Noise analysis of interior structure under powertrain excitation

Noise analysis of interior structure under powertrain excitation

Powertrain Modeling and Model Predictive Longitudinal Dynamics Control for Hybrid Electric Vehicles

Optimal Rule-Based Power Management for Online, Real-Time Applications in HEVs with Multiple Sources and Objectives: A Review

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