A Cooperative Control Strategy for a Hydraulic Regenerative Braking System Based on Chassis Domain Control

Li, Ning; Jiang, Junping; Sun, Fulu; Ye, Mingrui; Ning, Xiaobin; Chen, Pengzhan

doi:10.3390/electronics11244212

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…Recently, with advancements in vehicle control systems, there has been a steady increase in attention to the coordinated control of electro-hydraulic composite braking systems [1,2]. In electric vehicle braking, the real-time monitoring and processing of data from multiple sensors enable the coordinated control of the regenerative braking system (RBS) and anti-lock braking system (ABS), effectively controlling the distribution of braking torque for each wheel based on parameters like battery status and vehicle speed [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

RBS and ABS Coordinated Control Strategy Based on Explicit Model Predictive Control

Chu,

Li,

Guo

et al. 2024

Sensors

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During the braking process of electric vehicles, both the regenerative braking system (RBS) and anti-lock braking system (ABS) modulate the hydraulic braking force, leading to control conflict that impacts the effectiveness and real-time capability of coordinated control. Aiming to enhance the coordinated control effectiveness of RBS and ABS within the electro-hydraulic composite braking system, this paper proposes a coordinated control strategy based on explicit model predictive control (eMPC-CCS). Initially, a comprehensive braking control framework is established, combining offline adaptive control law generation, online optimized control law application, and state compensation to effectively coordinate braking force through the electro-hydraulic system. During offline processing, eMPC generates a real-time-oriented state feedback control law based on real-world micro trip segments, improving the adaptiveness of the braking strategy across different driving conditions. In the online implementation, the developed three-dimensional eMPC control laws, corresponding to current driving conditions, are invoked, thereby enhancing the potential for real-time braking strategy implementation. Moreover, the state error compensator is integrated into eMPC-CCS, yielding a state gain matrix that optimizes the vehicle braking status and ensures robustness across diverse braking conditions. Lastly, simulation evaluation and hardware-in-the-loop (HIL) testing manifest that the proposed eMPC-CCS effectively coordinates the regenerative and hydraulic braking systems, outperforming other CCSs in terms of braking energy recovery and real-time capability.

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Section: Introductionmentioning

confidence: 99%

RBS and ABS Coordinated Control Strategy Based on Explicit Model Predictive Control

Chu,

Li,

Guo

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

Regenerative Braking Algorithm for Parallel Hydraulic Hybrid Vehicles Based on Fuzzy Q-Learning

et al. 2023

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The use of regenerative braking systems is an important approach for improving the travel mileage of electric vehicles, and the use of an auxiliary hydraulic braking energy recovery system can improve the efficiency of the braking energy recovery process. In this paper, we present an algorithm for optimizing the energy recovery efficiency of a hydraulic regenerative braking system (HRBS) based on fuzzy Q-Learning (FQL). First, we built a test bench, which was used to verify the accuracy of the hydraulic regenerative braking simulation model. Second, we combined the HRBS with the electric vehicle in ADVISOR. Third, we modified the regenerative braking control strategy by introducing the FQL algorithm and comparing it with a fuzzy-control-based energy recovery strategy. The simulation results showed that the power savings of the vehicle optimized by the FQL algorithm were improved by about 9.62% and 8.91% after 1015 cycles and under urban dynamometer driving schedule (UDDS) cycle conditions compared with a vehicle based on fuzzy control and the dynamic programming (DP) algorithm. The regenerative braking control strategy optimized by the fuzzy reinforcement learning method is more efficient in terms of energy recovery than the fuzzy control strategy.

show abstract

A Cooperative Control Strategy for a Hydraulic Regenerative Braking System Based on Chassis Domain Control

Cited by 2 publications

References 18 publications

RBS and ABS Coordinated Control Strategy Based on Explicit Model Predictive Control

RBS and ABS Coordinated Control Strategy Based on Explicit Model Predictive Control

Regenerative Braking Algorithm for Parallel Hydraulic Hybrid Vehicles Based on Fuzzy Q-Learning

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