Road motion control electric vehicle with speed and torque observer

Foito, Daniel; Gaspar, Manuel; Pires, V. Fernão

doi:10.1109/smartmile.2013.6708210

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…This includes the selection of the DC-DC converter topology and its benefits and the selection of suitable power sources for the HEV. a) Literature Review: In [18], a control technique has been applied on the HEV to design observers for estimating the values of speed and torque. Several control strategies have been developed for fast charging of electric vehicles in [19].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Control of PV-FC Electric Vehicle using Lyapunov based Theory

Hayat¹,

Ahmed²,

Tanveer-ul-Haq³

et al. 2019

IJACSA

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Lyapunov based control is used to test whether a dynamical system is asymptotically stable or not. The control strategy is based on linearization of system. A Lyapunov function is constructed to obtain a stabilizing feedback controller. This paper deals with Lyapunov based control of multiple input single output system for hybrid electric vehicles (HEVs). Generally, an electric vehicle has an energy management system (EMS), an inverter, a DC-DC converter and a traction motor for the operation of its wheels. The control action is applied on the DC-DC converter, which works side-by-side with the EMS of the electric vehicle. The input sources considered in this study are: photo-voltaic (PV) panel, fuel cell and high voltage lithiumion (Li-ion) battery. PV cell and fuel cell are considered as the primary sources of energy and the battery is considered as the secondary source. The converter used is a DC-DC boost converter which is connected with all the three sources. The idea follows the basic HEV principle in which multiple sources are incorporated to satisfy the power demands of the vehicle, using a DC-DC converter and an inverter, to operate its traction motor. The target is to achieve necessary tracking of all input source currents and output voltage, and fulfill the power demand of the HEV under severe load transients. The operations of the DC-DC converter are divided into three stages, each representing a different combination of the input sources. The analysis and proof of the stability of the HEV system is done using the Lyapunov stability theory.The results are discussed in the conclusion.

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Section: Introductionmentioning

confidence: 99%

Hybrid Control of PV-FC Electric Vehicle using Lyapunov based Theory

Hayat¹,

Ahmed²,

Tanveer-ul-Haq³

et al. 2019

IJACSA

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“…The simulation results are tested by two 37-kW IMs. In another paper [12], an EDS for two rear wheels of EV is presented and analyzed versus the speed and torque observed for the DC motor.…”

Section: Introductionmentioning

confidence: 99%

Computation of the Speed of Four In-Wheel Motors of an Electric Vehicle Using a Radial Basis Neural Network

Yıldırım

Çatalbaş

Gülten

et al. 2016

Eng. Technol. Appl. Sci. Res.

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This paper presents design and speed estimation for an Electric Vehicle (EV) with four in-wheel motors using Radial Basis Neural Network (RBNN). According to the steering angle and the speed of EV, the speeds of all wheels are calculated by equations derived from the Ackermann-Jeantand model using CoDeSys Software Package. The Electronic Differential System (EDS) is also simulated by Matlab/Simulink using the mathematical equations. RBNN is used for the estimation of the wheel speeds based on the steering angle and EV speed. Further, different levels of noise are added to the steering angle and the EV speed. The speeds of front wheels calculated by CoDeSys are sent to two Induction Motor (IM) drives via a Controller Area Network-Bus (CAN-Bus). These speed values are measured experimentally by a tachometer changing the steering angle and EV speed. RBNN results are verified by CoDeSys, Simulink, and experimental results. As a result, it is observed that RBNN is a good estimator for EDS of an EV with in-wheel motor due to its robustness to different levels of sensor noise.

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“…Designed EDS is verified by Matlab/Simulink results. In [5], an electric differential system for two independent rear wheels of EV is presented and analysis of speed and torque observer for DC motor is carried out. Rear wheel torque vectoring algorithm based on Ackermann Jeantand model is designed for dual motor electric drive system in [6].…”

Section: Introductionmentioning

confidence: 99%

Design of Electronic Differential System for an Electric Vehicle with in-wheel motor

Yıldırım

Öksüztepe

Tanyeri

et al. 2016

2016 IEEE Power and Energy Conference at Illinois (PECI)

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This paper presents design of Electronic Differential System (EDS) for an Electric Vehicle (EV) with inwheel motor. EDS is generally used in EVs due to some drawbacks of mechanical differential such as being heavy systems and mechanical losses caused by the powertrains. According to the turning angle of the wheel, task of EDS for front wheels is to adjust rpm of the wheels. On the contrary for rear wheels, only rpm control is realized due to not steering. Hence, there are less studies on EDS for front wheels of EV in the literature. In this study, an EDS for front wheels of EV is designed. According to steering angle and speed of EV, the speeds of the front wheels are estimated by equations derived from Ackermann-Jeantand model using Codesys Software Package. The estimated speeds are sent to Induction Motor (IM) Drives via Controller Area Network-Bus (CAN-Bus). EDS is also simulated by Matlab/Simulink. Then, the speeds of the front wheels are experimentally measured by a tachometer. Codesys results are verified by both Simulink and experimental results. It is observed that the designed EDS is convenient for EVs with inwheel motor.

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Road motion control electric vehicle with speed and torque observer

Cited by 6 publications

References 13 publications

Hybrid Control of PV-FC Electric Vehicle using Lyapunov based Theory

Hybrid Control of PV-FC Electric Vehicle using Lyapunov based Theory

Computation of the Speed of Four In-Wheel Motors of an Electric Vehicle Using a Radial Basis Neural Network

Design of Electronic Differential System for an Electric Vehicle with in-wheel motor

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