Fuzzy controller design for parallel hybrid vehicle analysis using forward simulation

Naderi, Peyman; Mirsalim, Mojtaba; Bathaee, M.; Chini, Reza

doi:10.1109/vppc.2009.5289847

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…The performance of an HEV is largely dependent on a balance between the gasoline engine and the electric motor, optimized with respect to fuel consumption based on vehicle conditions [3]. Many different types of control methods have been applied to balance the power and energy requirements of HEVs, including fuzzy logic [4][5][6], genetic algorithms [7], dynamic programming [8,9], Pareto optimization [10], and intelligent mechanism designs [11]. These control strategies rely heavily on the availability of battery power to balance the operation of the gasoline engine versus the electric motor.…”

Section: Introductionmentioning

confidence: 99%

Estimation Strategies for the Condition Monitoring of a Battery System in a Hybrid Electric Vehicle

Gadsden

Al-Shabi

Habibi

2011

ISRN Signal Processing

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This paper discusses the application of condition monitoring to a battery system used in a hybrid electric vehicle (HEV). Battery condition management systems (BCMSs) are employed to ensure the safe, efficient, and reliable operation of a battery, ultimately to guarantee the availability of electric power. This is critical for the case of the HEV to ensure greater overall energy efficiency and the availability of reliable electrical supply. This paper considers the use of state and parameter estimation techniques for the condition monitoring of batteries. A comparative study is presented in which the Kalman and the extended Kalman filters (KF/EKF), the particle filter (PF), the quadrature Kalman filter (QKF), and the smooth variable structure filter (SVSF) are used for battery condition monitoring. These comparisons are made based on estimation error, robustness, sensitivity to noise, and computational time.

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Section: Introductionmentioning

confidence: 99%

Estimation Strategies for the Condition Monitoring of a Battery System in a Hybrid Electric Vehicle

Gadsden

Al-Shabi

Habibi

2011

ISRN Signal Processing

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Study on the braking force allocation dynamic control strategy based on the fuzzy control logic

Liang

Yao

Yin

et al. 2011

2011 IEEE 18th International Conference on Industrial Engineering and Engineering Management

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The regenerative braking in the electric vehicle is a double-edge sword, which betters the energy saving effect but increase the unsafe hidden dangerous during braking. A braking force allocation dynamic control strategy has been studied in this paper to solve the contradiction between the energy saving and brake security. There are multiple factors to be considered simultaneously, such as the displacement of the brake pedal, the variation rate of the brake pedal displacement, the vehicle speed, the wheel slip rate error and the variation rate of which. So the fuzzy logic controllers are used to solve this problem based on the expert experiences. The results simulated in the Matlab/Simulink model certify that the energy is recovered as much as possible under guaranteeing the wheels out of locking.Keywords -Braking force allocation, dynamic control, fuzzy logic ___________________________________ 978-1-61284-449-7/11/$26.00 ©2011 IEEE

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Fuzzy controller design for parallel hybrid vehicle analysis using forward simulation

Cited by 2 publications

References 6 publications

Estimation Strategies for the Condition Monitoring of a Battery System in a Hybrid Electric Vehicle

Estimation Strategies for the Condition Monitoring of a Battery System in a Hybrid Electric Vehicle

Study on the braking force allocation dynamic control strategy based on the fuzzy control logic

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