Journal ArticleDOI
Vehicle dynamics control with energy recuperation based on control allocation for independent wheel motors and brake system
TLDR
In this article, an optimal control allocation method for the brake system and the wheel motors of an electric vehicle is proposed for vehicle dynamics control and energy recuperation, which takes into account temperature of electric motors, SOC and voltage of battery, vehicle velocity, fault situations, wheel slip, and vehicle subsystem prioritisation depending on parameters of vehicle dynamics.Abstract:
This work proposes an optimal control allocation method for the brake system and the wheel motors of an electric vehicle. The realisation of the method is proposed for vehicle dynamics control and energy recuperation. The control allocation takes into account temperature of electric motors, SOC and voltage of battery, vehicle velocity, fault situations, wheel slip, and vehicle subsystem prioritisation depending on parameters of vehicle dynamics. To illustrate the functional properties of the control allocation method, the corresponding simulation study is performed for the straight-line braking and ‘sine with dwell’ cornering. The simulations use a number of mathematical models including the 14 DoF model of vehicle motion and the models of electric vehicle systems. The simulation results confirmed effectiveness of the proposed approach both for regenerative braking and vehicle stability.read more
Citations
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Journal ArticleDOI
A Survey of Traction Control and Antilock Braking Systems of Full Electric Vehicles With Individually Controlled Electric Motors
TL;DR: This paper provides a review of state-of-the-art technology and recent developments in TC and ABSs using the actuation of electric motors, with particular attention paid to the realization of slip estimators, the formalization of torque demand, and the control methods applied.
Journal ArticleDOI
Identification of Road-Surface Type Using Deep Neural Networks for Friction Coefficient Estimation
TL;DR: The research has shown that the proposed solution increases the performance of ABS with a rule-based control strategy, and testing of a deep neural networks (DNN)-based road-surface and conditions classification algorithm revealed that this is the most promising approach for this task.
Journal ArticleDOI
Vehicle motion control with subsystem prioritization
TL;DR: In this article, a new approach for integrated vehicle motion control, coordinating multiple vehicle subsystems of a passenger car including friction brake system, near-wheel drive electric motors, wheel steer actuators, camber angle actuators and actuators generating additional normal forces is presented.
Journal ArticleDOI
Multi-Objective Optimal Control Allocation for an Over-Actuated Electric Vehicle
TL;DR: A unified control allocation law composed of two-step optimization is developed to trade off energy optimization and driving stability in actual complicated conditions and to improve energy recovery in routine stable driving conditions.
Journal ArticleDOI
Design and analysis of a novel power management approach, applied on a connected vehicle as V2V, V2B/I, and V2N
Aymen Flah,Chokri Mahmoudi +1 more
TL;DR: A new power management approach applicable for three kinds of connected vehicles based on the principle of vehicle data sharing is exposed, which is designed for implementing all of these three communication cases.
References
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Book
Vehicle dynamics and control
TL;DR: In this paper, the authors present a mean value model of SI and Diesel engines, and design and analysis of passive and active automotive suspension components, as well as semi-active and active suspensions.
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Tyre and vehicle dynamics
TL;DR: In this article, the wheel-shimmy phenomenon is considered in the context of dynamic tire testing and tire characteristics and vehicle handling and stability, and a variety of models are proposed.
Journal ArticleDOI
Energy-management system for a hybrid electric vehicle, using ultracapacitors and neural networks
TL;DR: A very efficient energy-management system for hybrid electric vehicles (HEVs), using neural networks (NNs), was developed and tested, and the increase in range was around 5.3% in city tests, however, when optimal control with NN was used, this figure increased to 8.9%.
Journal ArticleDOI
Automotive Control Systems: For Engine, Driveline and Vehicle
Uwe Kiencke,Lars Nielsen +1 more
TL;DR: In this paper, the authors have large experiences in industrial development (Bosch) as well as in academic research and introduce mechanical engineers into vehicle-specific signal processing and automatic control.
Automotive Control Systems: For Engine, Driveline, and Vehicle
Uwe Kiencke,Lars Nielsen +1 more
TL;DR: In this paper, the authors have large experiences in industrial development (Bosch) as well as in academic research and introduce mechanical engineers into vehicle-specific signal processing and automatic control.