Optimal Slip Ratio Based Fuzzy Control of Acceleration Slip Regulation for Four-Wheel Independent Driving Electric Vehicles

Yin, Guodong; Wang, Shanbao; Jin, Xin

doi:10.1155/2013/410864

Cited by 32 publications

(28 citation statements)

References 12 publications

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“…The phenomenon of vehicle speed is corrected based on slip rate control. [4][5][6][26][27][28][29] In this study, we design the control target of TCS based on speed control. As shown in equation (9), slip rate is calculated based on wheel and vehicle speeds.…”

Section: Discussionmentioning

confidence: 99%

Development of a new traction control system using ant colony optimization

Jin

Ling

2018

Advances in Mechanical Engineering

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An advanced traction control system can help limit wheel rotation and enhance vehicle stability. This article presents a new traction control system under complicated situations, including the low slippery road surface and split-m road surface. First, a 15-degree-of-freedom nonlinear vehicle dynamics simulation model is established. Then, the driving wheel speed is regulated by adjusting the engine torque and the wheel brake pressure. The engine torque regulation is based on a proportional-integral-derivative plus ant colony optimization controller, and the wheel brake pressure regulation is based on a proportional-integral plus ant colony optimization controller. Finally, the proposed strategies are applied to simulation and road tests. Results indicate that the algorithm exhibits high control accuracy and robust performance. Compared with the traditional proportional-integral-derivative controller, the proposed strategies improve vehicle acceleration performance and stability.

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Section: Discussionmentioning

confidence: 99%

Development of a new traction control system using ant colony optimization

Jin

Ling

2018

Advances in Mechanical Engineering

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“…Now, we will define angular acceleration (̇,̇) of both acceleration/braking modes. Ignoring the rolling resistance and wind resistance, the relationship between the wheel angular acceleration, motor torque , and slip ratio can be described by the following formulas [41].…”

Section: Asr/abs Control Designmentioning

confidence: 99%

Electric Vehicle Longitudinal Stability Control Based on a New Multimachine Nonlinear Model Predictive Direct Torque Control

Sekour

Hartani

Merah

2017

Journal of Advanced Transportation

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In order to improve the driving performance and the stability of electric vehicles (EVs), a new multimachine robust control, which realizes the acceleration slip regulation (ASR) and antilock braking system (ABS) functions, based on nonlinear model predictive (NMP) direct torque control (DTC), is proposed for four permanent magnet synchronous in-wheel motors. The in-wheel motor provides more possibilities of wheel control. One of its advantages is that it has low response time and almost instantaneous torque generation. Moreover, it can be independently controlled, enhancing the limits of vehicular control. For an EV equipped with four in-wheel electric motors, an advanced control may be envisaged. Taking advantage of the fast and accurate torque of in-wheel electric motors which is directly transmitted to the wheels, a new approach for longitudinal control realized by ASR and ABS is presented in this paper. In order to achieve a high-performance torque control for EVs, the NMP-DTC strategy is proposed. It uses the fuzzy logic control technique that determines online the accurate values of the weighting factors and generates the optimal switching states that optimize the EV drives' decision. The simulation results built in Matlab/Simulink indicate that the EV can achieve high-performance vehicle longitudinal stability control.

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“…According to the characteristics of the distributed driving system, the fuzzy control rules are designed according to the following [20], and the fuzzy control rule table is shown in Table 1: (1) When the torque demand of front wheel drive motor is less than the total torque demand req , the average distribution drive mode is adopted.…”

Section: Fuzzy Control Strategymentioning

confidence: 99%

Contrastive Study on Torque Distribution of Distributed Drive Electric Vehicle under Different Control Methods

Zheng

2017

Journal of Control Science and Engineering

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This paper uses certain hub motor distributed electric vehicle driving system as the research object, using several control strategies, such as dynamic programming global optimization algorithm, fuzzy control, and torque equal distribution and realizing the distribution control of the distributed power of the electric drive system. The simulation results show that, under the NEDC road condition, using the dynamic programming algorithm to optimize the torque distribution, the energy consumption of the electric drive system is 8041 kJ, decreased by 4.77% compared to the average torque distribution control and decreased by 3.5% compared to the fuzzy control strategy. The power consumption of the electric vehicle is 20.25 kWh per 100 km, decreased by 1.01 kWh compared to the average torque distribution control strategy and decreased by 0.72 kWh compared to the fuzzy control strategy. Under the fixed working condition, the energy efficiency of power system can be improved effectively when the distributed dynamic system torque is optimized by the dynamic programming algorithm. Without considering the global optimization, the fuzzy control can effectively improve the energy efficiency of the power system compared to the torque average distribution strategy.

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Optimal Slip Ratio Based Fuzzy Control of Acceleration Slip Regulation for Four-Wheel Independent Driving Electric Vehicles

Cited by 32 publications

References 12 publications

Development of a new traction control system using ant colony optimization

Development of a new traction control system using ant colony optimization

Electric Vehicle Longitudinal Stability Control Based on a New Multimachine Nonlinear Model Predictive Direct Torque Control

Contrastive Study on Torque Distribution of Distributed Drive Electric Vehicle under Different Control Methods

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