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Advances in Vehicle Dynamics and Motion Control for Electric Vehicles

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "E: Electric Vehicles".

Deadline for manuscript submissions: closed (5 November 2021) | Viewed by 2761

Special Issue Editor


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Guest Editor
Automotive Engineering Group, Technische Universität Ilmenau, 98693 Ilmenau, Germany
Interests: vehicle dynamics; automotive control systems; electric vehicles; automated vehicles; chassis design; alternative powertrains; vehicle testing; motion control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

You are invited to submit papers to a Special Issue of Energies on “Advances in Vehicle Dynamics and Motion Control for Electric Vehicles.” Recent trends in automotive engineering point out that influence of an electric powertrain, especially in the case of individual on-board and in-wheel motors, on electric vehicle dynamics in terms of stability, handling, agility, and comfort should be carefully considered on various development and designing stages. As compared to conventional vehicles, electric cars have different loading modes, another balance between sprung and unsprung masses, as well as weight distribution. On the other hand, on-board and in-wheel motors can not only be used in traditional vehicle dynamics control systems as ABS or ESC but also can cause emerging new motion control functions through blended operation of various powertrain and chassis actuators (e.g., brake and ride blending). All these factors should be properly considered in studies on vehicle dynamics and motion control system design for electric vehicles.    

This Special Issue will focus on new results in the following topics related to electric vehicles, in particular with on-board and electric motors:

- Vehicle dynamics

- Motion control

- Brake control and brake blending

- Vehicle stability control

- Ride comfort and active suspension

- Vehicle state and parameter estimation

- Off-road electric vehicles

- Torque vectoring

- Wheel slip control

- Integrated vehicle dynamics control

- New chassis concepts and systems for electric vehicles (active front and rear steering, active camber, active toe, active stabilisator)

Dr. Valentin Ivanov
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • electric vehicle
  • vehicle dynamics
  • motion control
  • automotive control systems
  • automotive chassis
  • in-wheel motor
  • on-board motor

Published Papers (1 paper)

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Research

23 pages, 4215 KiB  
Article
Optimization-Based Tuning of a Hybrid UKF State Estimator with Tire Model Adaption for an All Wheel Drive Electric Vehicle
by Hannes Heidfeld and Martin Schünemann
Energies 2021, 14(5), 1396; https://doi.org/10.3390/en14051396 - 03 Mar 2021
Cited by 6 | Viewed by 1699
Abstract
Novel drivetrain concepts such as electric direct drives can improve vehicle dynamic control due to faster, more accurate, and more flexible generation of wheel individual propulsion and braking torques. Exact and robust estimation of vehicle state of motion in the presence of unknown [...] Read more.
Novel drivetrain concepts such as electric direct drives can improve vehicle dynamic control due to faster, more accurate, and more flexible generation of wheel individual propulsion and braking torques. Exact and robust estimation of vehicle state of motion in the presence of unknown disturbances, such as changes in road conditions, is crucial for realization of such control systems. This article shows the design, tuning, implementation, and test of a state estimator with individual tire model adaption for direct drive electric vehicles. The vehicle dynamics are modeled using a double-track model with an adaptive tire model. State-of-the-art sensors, an inertial measurement unit, steering angle, wheel speed, and motor current sensors are used as measurements. Due to the nonlinearity of the vehicle model, an Unscented Kalman Filter (UKF) is used for simultaneous state and parameter estimation. To simplify the difficult task of UKF tuning, an optimization-based method using real-vehicle data is utilized. The UKF is implemented on an electronic control unit and tested with real-vehicle data in a hardware-in-the-loop simulation. High precision even in severe driving maneuvers under various road conditions is achieved. Nonlinear state and parameter estimation for all wheel drive electric vehicles using UKF and optimization-based tuning is shown to provide high precision with minimal manual tuning effort. Full article
(This article belongs to the Special Issue Advances in Vehicle Dynamics and Motion Control for Electric Vehicles)
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