Control method of wheel slip rate based on fuzzy algorithm

Zhang, Deping; Sun, Liangbo; Yang, Xuejin; Wang, Beihai; Wang, Luan

doi:10.3233/jifs-179856

Cited by 2 publications

(3 citation statements)

References 7 publications

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“…( 14). d = w + f + s + j (14) where Fd is vehicle driving force, Ff is rolling resistance, Fs denotes slope resistance, Fj indicates acceleration resistance. The above equation is sorted according to whether the variable includes mass or not, as shown in Eq.…”

Section: B Vehicle Mass Estimation Based On Rlsmentioning

confidence: 99%

“…The reference speed is one of the most basic and core input signals in various control systems [11,12]. Because it directly influences the calculation of key parameters such as wheel slip rate [13,14], slip angle [15,16] and road excitation [17], and then has an impact on dynamics control [18,19] and ADAS [20,21]. Especially, for driverless vehicles committed to the development of safer and more intelligent L3 level and above, accurate reference speed estimation can further ensure that the vehicle achieves desired path tracking in any situation [22].…”

Section: Introductionmentioning

confidence: 99%

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Vehicle State Estimation Based on Multidimensional Information Fusion

et al. 2022

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As the core input parameters of various control systems, the real-time and accurate acquisition of reference speed, mass and road slope is the key factor to improve the performance of intelligent vehicle dynamics control. Therefore, the parameter estimation method based on multi-dimensional information fusion is proposed in this paper. A comprehensive evaluation of wheel dynamics state is realized by information fusion, which is quantified in terms of wheel speed credibility. Then the calculated dynamic speed and auxiliary speed are weighted coupled to achieve accurate estimation of reference speed, which avoids the influence of unstable wheels. Similarly, the method to calculate the confidence factor of mass estimation is established in order to screen the vehicle state suitable for estimation. And the online estimation of mass is realized based on recursive least square method. Meanwhile, the road slope estimation algorithm based on interactive multiple model has been designed, which achieves the weighted fusion of Kalman filter observer based on kinematics and unscented Kalman filter observer based on dynamics. Finally, the road tests were carried out on representative working conditions. The maximum error between the actual speed and the reference speed does not exceed 0.68m/s. The relative error of mass estimation is not more than 1.95%, and the absolute error of slope estimation is less than 1.84%, which proves that the proposed estimation algorithm has high comprehensive performance. More importantly, it is not limited to specific working conditions, which means a great significance for the development of intelligent vehicles.

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Section: B Vehicle Mass Estimation Based On Rlsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vehicle State Estimation Based on Multidimensional Information Fusion

et al. 2022

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show abstract

“…A new robust adaptive algorithm based on a state observer is designed to achieve tracking control of wheel slip rates [26]. In view of the long braking time of the existing wheel slip rate control method, the obvious gap between the slip rate and the optimal slip rate, and the poor control effect, the fuzzy algorithm is introduced to design the wheel slip control method to improve the control effect [27].…”

Section: Introductionmentioning

confidence: 99%

Research on Decoupled Optimal Control of Straight-Line Driving Stability of Electric Vehicles Driven by Four-Wheel Hub Motors

2021

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The optimal control strategy for the decoupling of drive torque is proposed for the problems of runaway and driving stability in straight-line driving of electric vehicles driven by four-wheel hub motors. The strategy uses a hierarchical control logic, with the upper control logic layer being responsible for additional transverse moment calculation and driving anti-slip control; the middle control logic layer is responsible for the spatial motion decoupling for the underlying coordinated distribution of the four-wheel drive torque, on the basis of which the drive anti-skid control of a wheel motor-driven electric vehicle that takes into account the transverse motion of the whole vehicle is realized; the lower control logic layer is responsible for the optimal distribution of the driving torque of the vehicle speed following control. Based on the vehicle dynamics software Carsim2019.0 and MATLAB/Simulink, a simulation model of a four-wheel hub motor-driven electric vehicle control system was built and simulated under typical operating conditions such as high coefficient of adhesion, low coefficient of adhesion and opposing road surfaces. The research shows that the wheel motor drive has the ability to control the stability of the whole vehicle with large intensity that the conventional half-axle drive does not have. Using the proposed joint decoupling control of the transverse pendulum motion and slip rate as well as the optimal distribution of the drive force with speed following, the transverse pendulum angular speed and slip rate can be effectively controlled with the premise of ensuring the vehicle speed, thus greatly improving the straight-line driving stability of the vehicle.

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Control method of wheel slip rate based on fuzzy algorithm

Cited by 2 publications

References 7 publications

Vehicle State Estimation Based on Multidimensional Information Fusion

Vehicle State Estimation Based on Multidimensional Information Fusion

Research on Decoupled Optimal Control of Straight-Line Driving Stability of Electric Vehicles Driven by Four-Wheel Hub Motors

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