Predictive Brake Control for Electric Vehicles

Satzger, Clemens; Castro, Ricardo de

doi:10.1109/tvt.2017.2751104

Cited by 48 publications

(26 citation statements)

References 43 publications

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“…In this study a multi-parametric quadratic programming (mp-QP) formulation is adopted, as suggested in [21] and implemented in [24]. The mp-NLP in (13) and (14) is linearized around a predefined point ( 0 , ,0 ) by means of Taylor series expansion, such that the cost function is approximated with a quadratic function (15)- (16) and the constraints assume a linear formulation (17):…”

Section: B Off-line Solutionmentioning

confidence: 99%

“…A linear MPC strategy is developed in [10], where the ABS slip regulation is achieved through torque blending between the friction brakes and in-wheel motors. Similarly, [11], [12] and the very recent research [13] combine ABS control and torque blending, by using a linear MPC formulation. [14] presents an MPC-based ABS, with test results on a hardware-in-the-loop rig.…”

Section: Introductionmentioning

confidence: 99%

“…[20] partially covers this knowledge gap, but limits the analysis to on-board electric drivetrains, characterized by significant torsional dynamics. [13] includes also an MPC-PI experimental comparison, but for an ABS application combined with torque blending.…”

Section: Introductionmentioning

confidence: 99%

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Explicit Nonlinear Model Predictive Control for Electric Vehicle Traction Control

Tavernini

Metzler

Gruber

et al. 2019

IEEE Trans. Contr. Syst. Technol.

108

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Abstract-This study presents a traction control system for electric vehicles with in-wheel motors, based on explicit non-linear model predictive control. The feedback law, available beforehand, is described in detail, together with its variation for different plant conditions. The explicit controller is implemented on a rapid control prototyping unit, which proves the real-time capability of the strategy, with computing times in the order of microseconds. These are significantly lower than the required sampling time for a traction control application. Hence, the explicit model predictive controller can run at the same frequency as a simple traction control system based on Proportional Integral (PI) technology. High-fidelity model simulations provide: i) a performance comparison of the proposed explicit non-linear model predictive controller with a benchmark PI-based traction controller with gain scheduling and anti-windup features; and ii) a performance comparison among two explicit and one implicit non-linear model predictive controllers based on different internal models, with and without consideration of transient tire behavior and load transfers. Experimental test results on an electric vehicle demonstrator are shown for one of the explicit non-linear model predictive controller formulations.Index Terms-traction control, wheel slip, model predictive control, PI control, electric vehicle, in-wheel motors.Sorniotti (email: a.sorniotti@surrey.ac.uk) are with the

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Section: B Off-line Solutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Explicit Nonlinear Model Predictive Control for Electric Vehicle Traction Control

Tavernini

Metzler

Gruber

et al. 2019

IEEE Trans. Contr. Syst. Technol.

108

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“…A control algorithm based on a model predictive control framework was proposed to recover more braking energy and maintain the optimal slip value. The framework could reduce the torque tracking error [17].The feasibility of several control strategies is worth studying, and the optimization of control parameters that affect the comprehensive braking performance, including braking stability and energy recuperation efficiency, also needs attention. Sun presented a predictive control strategy using an offline process optimization stream to realize the balance between the maximum regenerative energy recuperation efficiency and the optimum braking stability [18,19].…”

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confidence: 99%

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confidence: 99%

A Control Algorithm for the Novel Regenerative–Mechanical Coupled Brake System with by-Wire Based on Multidisciplinary Design Optimization for an Electric Vehicle

Guoye

Gong

et al. 2018

Energies

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Current regenerative braking systems in electric vehicles have several problems, such as complex structures, too many control parameters, and inconsistent braking responses. To solve these problems, a control algorithm with multidisciplinary design optimization (MDO) is proposed based on the novel regenerative-mechanical coupled brake-by-wire system. A dynamic model of the novel regenerative braking system was established to analyze the mechanism of coupled braking and propose a braking torque distribution strategy. To realize a better balance between the optimum braking stability and the maximum regenerative energy recovery based on the braking torque distribution strategy and sample points, the MDO mathematical model was developed to optimize the control parameters with the collaborative optimization algorithm. The finite sample points comprising the vehicle speed, battery state-of-charge, and braking severity were obtained through an optimal Latin hypercube design and represent the overall design space. A network was established based on the sample points and the optimization results. Using this network, the in-depth characteristics of the sample points and the optimization results were obtained through supervised learning to develop the control algorithm for vehicle braking. A simulation was performed using the normal braking condition, and the simulation results demonstrated that the control algorithm has higher control precision than conventional methods and better real-time performance than online optimization.Energies 2018, 11, 2322 2 of 18 be modulated to control the overall braking torque for meeting the requirements [5]. The regenerative braking torque depends on the motor characteristics [6], the charging power capability of the battery [7], and the available tire-road friction [8]; hence, existing research has focused on improving the braking energy recovery efficiency by distributing the torque between regenerative braking and friction braking based on drivers' demands.In engineering practice, several rule-based regenerative braking strategies have been proposed and used in recent years. For a rear-driven electric truck, a modified control strategy that determines how to distribute the braking force between the front and rear axles was proposed by Zhang to improve the recovery efficiency [9]. Xiao presented an integrated control strategy to coordinate regenerative and friction braking forces to deal with the braking stability and recovery efficiency when a vehicle performed normal deceleration and emergency braking [3]. To regenerate more braking energy and move closer to the ideal braking force distribution curve, a combined braking control strategy was developed for the rear wheel-driven series hybrid electric EV to adjust the proportions of regenerative braking and friction braking [10]. A regenerative braking cooperative control strategy was proposed by Jiweon for hybrid EVs equipped with a hydraulic brake on the rear wheels and an electronic wedge brake on the front wheels [11].Optimization and...

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A Novel Bilevel Electromechanical Compound Braking Coordinated Control Strategy for Electric Vehicles

Zhao,

Li,

Yang

et al. 2024

Energy Tech

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Due to the difference of response time and braking type between the motor and the pneumatic braking system, it is still difficult to coordinate the motor braking and the pneumatic braking to ensure the vehicle stability and maximal energy regeneration. To address this challenge, a bilevel electromechanical compound braking coordinated control strategy for electric vehicles is proposed considering general and emergency braking state. First, in general braking state, considering the delay characteristics of the pneumatic braking system, a Lagrange quadratic interpolation prediction algorithm is designed to start the pneumatic braking system in advance. Second, in emergency braking state, a model predictive control method is proposed to optimize the braking torque distribution while controlling the wheel slip ratio in a stable range. In order to obtain the optimal control effect, a modified adaptive cuckoo search algorithm is put forward, in which three adaptive impact factors are added. Finally, the proposed control strategy is verified under three road conditions and compared with the conventional control strategy. The results demonstrate significant improvements under gravel road condition, including a 7% increase in energy recovery efficiency, a 92.1% enhancement in the following effect of pneumatic braking torque, and a 43.5% reduction in wheel fluctuation.

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Predictive Brake Control for Electric Vehicles

Cited by 48 publications

References 43 publications

Explicit Nonlinear Model Predictive Control for Electric Vehicle Traction Control

Explicit Nonlinear Model Predictive Control for Electric Vehicle Traction Control

A Control Algorithm for the Novel Regenerative–Mechanical Coupled Brake System with by-Wire Based on Multidisciplinary Design Optimization for an Electric Vehicle

A Novel Bilevel Electromechanical Compound Braking Coordinated Control Strategy for Electric Vehicles

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