A New Torque Distribution Strategy for Blended Anti-Lock Braking Systems of Electric Vehicles Based on Road Conditions and Driver's Intentions

Li, Weihua; Du, Haiping

doi:10.4271/2016-01-0461

Cited by 22 publications

(13 citation statements)

References 13 publications

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“…The ABS hydraulic system model can describe the mechanism leading to the pressure increasing, holding, and decreasing process, and its principle is shown in Figure 4. According to [24], the dynamic model of ABS hydraulic system can be established as:…”

Section: Abs Hydraulic System Modelmentioning

confidence: 99%

Dynamic Coordinated Control for Regenerative Braking System and Anti-Lock Braking System for Electrified Vehicles Under Emergency Braking Conditions

Yang

Tang

et al. 2020

IEEE Access

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The economy of electrified vehicles can be improved by using the motor to recover the energy released during braking. However, the vehicle's regenerative braking system (RBS) and anti-lock braking system (ABS) are not compatible, so the energy dissipated during braking cannot be recovered under emergency braking conditions. This paper employs the method of logic threshold control combined with phase plane theory to analyze the relationship between the slip rate and the braking torque during the ABS braking process and to obtain the composition rule of the braking torque required for ABS braking. Based on this rule, a control strategy to coordinate RBS and ABS when triggering ABS is proposed to improve the efficiency of braking energy recovery. Furthermore, a comparative simulation is conducted to analyze the braking performance of electrified vehicle on roads with different adhesion coefficients by adopting the proposed control strategy and the traditional control strategy. The results show that, compared with the traditional coordinated control strategy, the braking energy recovery efficiency of the proposed coordinated control strategy is improved by 23.08%-38.54%, and can effectively shorten the braking distance and braking time, with better braking performance. Therefore, this paper offers a useful theoretical reference to the design of RBS and ABS coordinated control strategies for electrified vehicles. INDEX TERMS Anti-lock braking system, Coordinated control strategy, Energy recovery, Regenerative braking.

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Section: Abs Hydraulic System Modelmentioning

confidence: 99%

Dynamic Coordinated Control for Regenerative Braking System and Anti-Lock Braking System for Electrified Vehicles Under Emergency Braking Conditions

Yang

Tang

et al. 2020

IEEE Access

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“…Zhang et al [14] use statistics of fifteen sets of data from different drivers to 2 Mathematical Problems in Engineering evaluate the threshold value of emergency braking intention for electronic braking system (EBS). In [15], TS-fuzzy system was built to identify driver's normal braking and emergency braking intention, which adopts pedal stroke and the change rate of the pedal stroke as identification parameters. Yang et al [16] used unsupervised machine learning algorithms, such as K-means and Gaussian mixture model (GMM), to identify driver's braking intention.…”

Section: Literaturementioning

confidence: 99%

“…Model predictive control is used to achieve different braking intentions to make braking performance better. In [15], the braking torque distribution strategy is proposed based on the road conditions and driver's braking intentions for electric vehicles. Driver's braking intentions are classified as the emergency braking and the normal braking, which were identified by TS-fuzzy system.…”

Section: Literaturementioning

confidence: 99%

A Braking Intention Identification Method Based on Data Mining for Electric Vehicles

Wang

Tang

et al. 2019

Mathematical Problems in Engineering

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A braking intention identification method based on empirical mode decomposition (EMD) algorithm and entropy theory for electric vehicles is proposed. EMD algorithm is given to decompose nonstationary brake pedal signal to stationary intrinsic mode function (IMF), which is the base of data mining. After that, entropy theory is used to extract brake pedal signal features. A braking intention identification model is built based on fuzzy c-means clustering algorithm. The hardware and software for braking intention identification system based on this method is set up to do offline and real-time experiments. The results show that the identification method proposed in this paper has good real-time quality and can distinguish moderate braking intention and gentle braking intention better.

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“…When emergency braking is recognized, ABS and the motor regenerative braking system work in coordination to achieve braking safety. When the normal braking is recognized, the motor regenerative braking system and the mechanical hydraulic braking system work in coordination [12][13][14][15]. Therefore, in order to maximize the safety and energy utilization of electric vehicles, the braking intention also needs to be identified accurately and timely.…”

Section: Introductionmentioning

confidence: 99%

Identification of Driver Braking Intention Based on Long Short-Term Memory (LSTM) Network

Wang

Zhao

et al. 2020

IEEE Access

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Driving intention identification is a key technology which can improve the adaptability of the intelligent driver assistance systems and the energy efficiency of electric vehicles. This article proposes a novel method for identifying the driver braking intention. In order to improve the identification accuracy of driving intention, a braking intention identification model based on Long Short-Term Memory (LSTM) Network is constructed. The data of slight braking, normal braking and hard braking that can use for offline training are obtained through tests on real vehicle at Chang'an University vehicle performance testing ground. Support vector machine-recursive feature elimination (SVM-RFE) algorithm is used to select the characteristic parameter of braking intention identification model. The random search is subsequently used to optimize the hyper-parameters of LSTM. LSTM-based and General Hidden Markov Model (GHMM)-based model under different time window are used to identify braking intention of slight braking, normal braking and hard braking respectively. The results show that the Precision, Recall, F-measure, Accuracy of the braking intention identification model which propose in this paper based on LSTM are better than that of the braking intention identification model based on GHMM. Moreover, the Recall and Accuracy of the LSTM-based braking intention identification models are above 0.95, indicating the good ability of intention identification.

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A New Torque Distribution Strategy for Blended Anti-Lock Braking Systems of Electric Vehicles Based on Road Conditions and Driver's Intentions

Cited by 22 publications

References 13 publications

Dynamic Coordinated Control for Regenerative Braking System and Anti-Lock Braking System for Electrified Vehicles Under Emergency Braking Conditions

Dynamic Coordinated Control for Regenerative Braking System and Anti-Lock Braking System for Electrified Vehicles Under Emergency Braking Conditions

A Braking Intention Identification Method Based on Data Mining for Electric Vehicles

Identification of Driver Braking Intention Based on Long Short-Term Memory (LSTM) Network

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