Fuzzy control strategy of pure electric vehicle based on driving intention recognition

Qi, Wanqiang

doi:10.3233/jifs-179998

Cited by 10 publications

(6 citation statements)

References 18 publications

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“…The main factors affecting the efficiency of vehicle braking energy recovery include braking intensity and vehicle speed [27]. Pure electric commercial vehicles usually drive on mountainous terrain, and so road gradient is also an important consideration.…”

Section: Hybrid Brakingmentioning

confidence: 99%

Research on Regenerative Braking Control Strategy for Single-Pedal Pure Electric Commercial Vehicles

Li,

Shi,

Gao

et al. 2023

WEVJ

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In recent years, with the increasing severity of energy and environmental issues, countries have vigorously developed the new energy automotive industry. To reduce the difficulty of driver operation and increase endurance mileage, this article proposes a regenerative braking control strategy for a single-pedal pure electric commercial vehicle. Firstly, the single-pedal control system’s hierarchical approach was designed to contain the driver’s intention analysis and torque calculation layers. After identifying the driver’s intention, a logic threshold method was used to determine the braking pattern. Then, a fuzzy theory was used, with road gradient, braking strength, and speed as input parameters, and the ratio coefficient of braking force as the output parameter. A hybrid regenerative braking strategy was formulated based on the ideal distribution curve. Finally, the proposed strategy was verified through simulation and a constant-speed car-following experiment. The constant-speed car-following experiment results show that the maximum optimization rate of energy consumption provided by the proposed single-pedal regenerative braking control strategy is 5.81%, and the average optimization rate is 4.33%. This strategy can effectively reduce energy consumption and improve the economic performance of single-pedal pure electric commercial vehicles.

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Section: Hybrid Brakingmentioning

confidence: 99%

Research on Regenerative Braking Control Strategy for Single-Pedal Pure Electric Commercial Vehicles

Li,

Shi,

Gao

et al. 2023

WEVJ

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show abstract

“…The previous works on driver behavior modeling are generally based on Neural Networks [ 7 , 8 ], Hidden Markov Models [ 9 , 10 ], Fuzzy Control Theory [ 11 , 12 ], and Gaussian Mixture Models [ 13 , 14 ]. Regarding classification, it is necessary to group the different features that comprise driver behavior.…”

Section: State-of-the-art On Driver Behavior Feature Identificationmentioning

confidence: 99%

Fuzzy Ontology-Based System for Driver Behavior Classification

Fernández

Ito

Cruz-Piris

et al. 2022

Sensors

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Intelligent transportation systems encompass a series of technologies and applications that exchange information to improve road traffic and avoid accidents. According to statistics, some studies argue that human mistakes cause most road accidents worldwide. For this reason, it is essential to model driver behavior to improve road safety. This paper presents a Fuzzy Rule-Based System for driver classification into different profiles considering their behavior. The system’s knowledge base includes an ontology and a set of driving rules. The ontology models the main entities related to driver behavior and their relationships with the traffic environment. The driving rules help the inference system to make decisions in different situations according to traffic regulations. The classification system has been integrated on an intelligent transportation architecture. Considering the user’s driving style, the driving assistance system sends them recommendations, such as adjusting speed or choosing alternative routes, allowing them to prevent or mitigate negative transportation events, such as road crashes or traffic jams. We carry out a set of experiments in order to test the expressiveness of the ontology along with the effectiveness of the overall classification system in different simulated traffic situations. The results of the experiments show that the ontology is expressive enough to model the knowledge of the proposed traffic scenarios, with an F1 score of 0.9. In addition, the system allows proper classification of the drivers’ behavior, with an F1 score of 0.84, outperforming Random Forest and Naive Bayes classifiers. In the simulation experiments, we observe that most of the drivers who are recommended an alternative route experience an average time gain of 66.4%, showing the utility of the proposal.

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“…Qi et al proposed a control strategy that can not only meet the requirements of road conditions and the driver's driving intention, but also consider the vehicle's operating state. Using the fuzzy control algorithm, a fuzzy controller is designed, which takes the motor demand variable rate and battery charging state as the input and the motor demand torque compensation coefficient as the output [27].…”

Section: Introductionmentioning

confidence: 99%

Yaw Stability Research of the Distributed Drive Electric Bus by Adaptive Fuzzy Sliding Mode Control

et al. 2022

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The direct yaw moment control can effectively enhance the yaw stability of the vehicle under extreme conditions, which has become one of the essential technologies for the distributed driving electric bus. Due to the features of a large mass and high center of gravity of the bus, lateral instability is more likely to occur under extreme driving conditions. To reduce the uncertainty and interference in the yaw movement process of the bus, this paper targets the instability caused by the coupling problem between the sideslip angle and yaw rate. An adaptive fuzzy sliding mode control is proposed to execute direct yaw moment control. The weight coefficient of the sideslip angle and the yaw rate is adjusted via fuzzy control in real time. The optimal direct yaw moment is finally obtained. A distribution method based on the vertical load proportion is adopted for the allocation of four motors’ torque. Under three typical working conditions, a joint simulation test was carried out. The simulation results demonstrate that the raised method decreases the amplitude of the sideslip angle by 20.90%, 12.75%, and 23.67% and the yaw rate is 8.62%, 6.89%, and 9.28%, respectively. The chattering and sudden changes in the additional yaw moment are also lessened. The control strategy can realize the control target, which effectively strengthens the yaw stability of the bus.

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Fuzzy control strategy of pure electric vehicle based on driving intention recognition

Cited by 10 publications

References 18 publications

Research on Regenerative Braking Control Strategy for Single-Pedal Pure Electric Commercial Vehicles

Research on Regenerative Braking Control Strategy for Single-Pedal Pure Electric Commercial Vehicles

Fuzzy Ontology-Based System for Driver Behavior Classification

Yaw Stability Research of the Distributed Drive Electric Bus by Adaptive Fuzzy Sliding Mode Control

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