A novel electro-hydraulic compound braking system coordinated control strategy for a four-wheel-drive pure electric vehicle driven by dual motors

Tang, Qingsong; Yang, Yang; Luo, Chang; Zhong, Yang; Fu, Chunyun

doi:10.1016/j.energy.2021.122750

Cited by 40 publications

(22 citation statements)

References 26 publications

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“…In‐vehicle operation, the driver behaviour can be represented as a stochastic system since the driving conditions are random and unpredictable. The stochastic MPC modelling approach is used in vehicle energy management where the driver model is used to predict future power requests 92 . Since stochastic MPC is based on online numerical optimization, the driver model can be learned online, hence allowing the control algorithm to adapt to different driver's behaviours 93 .…”

Section: Regenerative Braking Control Strategies In Electric Vehiclesmentioning

confidence: 99%

Critical review on optimal regenerative braking control system architecture, calibration parameters and development challenges for EVs

Saiteja

Ashok

Wagh

et al. 2022

Intl J of Energy Research

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Summary Vehicle technology advances and the world shifts towards E‐mobility, improving the performance of electric vehicles (EVs) and HEVs, which are becoming the prominent focus. One of the prime topics of EV development is increasing the driving range, which is the fundamental requirement. Apart from increasing the battery capacity, retrieving the wasted energy during conventional braking, also called as regenerative braking, is a hot topic. In this context, numerous control architectures and major braking approaches are considered to examine in this study in order to create an efficient regenerative braking system (RBS). This review article intends to provide an overview of major subsystems in the RBS such as motor control system and hydraulic braking system and how it affects the braking performance is also well discussed. Additionally, it complies with some of the recent research applications and systematically reviews the several braking control strategies implied. The prominent ones are fuzzy logic control, neural network, MPC, sliding mode, and adaptive control modelling approaches. These control strategies are used to enhance energy regeneration without affecting vehicle performance. Further, this article discusses the RBS design process and its calibration variables, such as speed of the vehicle and brake force estimation, which can be used to improve braking performance. Moreover, challenges on RBS improvements are effectively addressed, coupled with brief suggestions and discussions for the growth of future RBS development. Finally, this article will hopefully help the reader to critically analyse the working of RBS and encourage to design of an efficient RBS for electro‐mobility application. Highlights A holistic overview of the RBS control system architecture and its various control systems is reviewed. Various brake energy control strategies like Fuzzy, MPC, NN, SMC, Adaptive and learning‐based controls are critically evaluated. The discussion of necessary calibration process and its associated parameters are elucidated. Representation of real‐time design and development process of an efficient RBS in EV. Some of the prominent challenges faced during design and development of RBS and scope of future improvement is suggested.

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Section: Regenerative Braking Control Strategies In Electric Vehiclesmentioning

confidence: 99%

Critical review on optimal regenerative braking control system architecture, calibration parameters and development challenges for EVs

Saiteja

Ashok

Wagh

et al. 2022

Intl J of Energy Research

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“…Furthermore, vehicle stability in braking situations needs to be taken into account. In Tang et al. (2022) , an optimal balance method between braking safety and efficiency was developed.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, vehicle stability in braking situations needs to be taken into account. In Tang et al (2022), an optimal balance method between braking safety and efficiency was developed. This method consisted of two components: one was a force distribution controller, and the other was an optimization object that was based on the wheel slip rate.…”

mentioning

confidence: 99%

Switchable MPC-based multi-objective regenerative brake control via flow regulation for electric vehicles

Mei

Cheng²,

Mu³

et al. 2023

Front. Robot. AI

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Recent investigations of the electric braking booster (E-Booster) focus on its potential to enhance brake energy regeneration. A vehicle’s hydraulic system is composed of the E-Booster and electric stability control to control the master cylinder and wheel cylinders. This paper aims to address the independent closed-loop control of the position and pressure as well as the maintenance of the pedal feel. To track both the reference signals related to piston displacement and the wheel cylinder pressure, an explicit model predictive control (MPC) is developed. First, the new flow model is introduced as the foundation for controller design and simulation. Next, in accordance with the operational conditions, the entire system is divided into three switchable subsystems. The three distributed MPCs are constructed based on the linearized subsystems, and a state machine is used to perform the state jump across the controllers. A linear piecewise affine control law can then be obtained by solving the quadratic program (QP) of explicit MPC. Afterwards, the non-linear extended Kalman filter including the recorded time-variant process noise is used to estimate all the state variables. The effectiveness of the explicit MPC is evidenced by the simulations compared with a single MPC in regenerative and dead-zone conditions. The proposed controller decreases the latency significantly by 85 milliseconds, which also helps to improve accuracy by 22.6%. Furthermore, the pedal feel remains consistent, even when factoring in the number of vibrations caused by the inherent hydraulic characteristic of pressure versus volume.

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“…[ 23 ] Tang took the four‐wheel‐drive pure EV driven by dual motors as the research object and proposed a coordinated control strategy for an electrohydraulic hybrid brake system with efficient energy recovery. [ 24 ] Yang proposed an electromechanical–hydraulic coupled vehicle, a system that enables the interconversion of three different power sources. The cosimulation model developed in the article could show that the vehicle has advantages in terms of economy and dynamics.…”

Section: Introductionmentioning

confidence: 99%

Energy Management Optimization of Master–Slave Hybrid Electric Vehicle under Rule‐Based Control Strategy

Zhang

Hong

et al. 2022

Energy Tech

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In response to the imperfections and issues with conventional fuel vehicles and electric vehicles (EVs), this article proposes a master–slave hybrid electric vehicle (MSHEV) with multiple energy sources. The research team establishes the rule‐based control strategy for MSHEV with the aid of reviewing existing theories. The control strategy enables the MSHEV to transition between several working modes. The simulation consequence verifies that the MSHEV has lower power consumption and energy loss than the EV under actual vehicle driving cycle. One of most critical initial steps in ameliorating a vehicle's performance is parameter optimization. This article selects the battery state of charge of MSHEV as the optimization objective and optimizes applicable parameters. Thereafter, an approximate model is constructed based on the Response Surface Model, and an optimization model is built based on the Multi‐Island Genetic Algorithm. The energy management of the optimized MSHEV is more reasonable and the state of charge is further enhanced. The accomplishment of this article is of considerable significance and reference value in the optimization of energy management of hybrid electric vehicles.

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A novel electro-hydraulic compound braking system coordinated control strategy for a four-wheel-drive pure electric vehicle driven by dual motors

Cited by 40 publications

References 26 publications

Critical review on optimal regenerative braking control system architecture, calibration parameters and development challenges for EVs

Critical review on optimal regenerative braking control system architecture, calibration parameters and development challenges for EVs

Switchable MPC-based multi-objective regenerative brake control via flow regulation for electric vehicles

Energy Management Optimization of Master–Slave Hybrid Electric Vehicle under Rule‐Based Control Strategy

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