An integrated control strategy for the composite braking system of an electric vehicle with independently driven axles

Sun, Fuchun; Liu, Wei; He, Haibo; Guo, Hongwei

doi:10.1080/00423114.2016.1180404

Cited by 21 publications

(12 citation statements)

References 10 publications

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“…where S α , S γ , and S λ are the relative errors of α 0 , γ 0 , and λ 0 , respectively. Figure 8 shows that compared to conventional methods [18,19], a smaller relative error can be found between the control parameters of the deep learning control algorithm and the online optimization values. The maximum value of the relative error was only 3.89%, which was within the expected error by 5%.…”

Section: Simulation Results and Discussionmentioning

confidence: 98%

“…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]. However, a problem of inconsistent braking response exists because regenerative braking and friction braking are two independent systems.…”

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confidence: 99%

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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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Section: Simulation Results and Discussionmentioning

confidence: 98%

mentioning

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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show abstract

“…In order to ensure the stability and safety of the brake, the pure electric mining truck brake control system studied in this paper keeps the mechanical brake system [19] and adds the regenerative brake system on the front and rear axles, as shown in Figure 1. During braking, the brake ECU calculates the braking force demand according to the brake pedal signal and transmits it to the vehicle ECU.…”

Section: Principle Analysis Of Regenerative Braking Control Systemmentioning

confidence: 99%

Research on Regenerative Braking of Pure Electric Mining Dump Truck

Zhang¹,

Yang²,

Zhang³

et al. 2019

WEVJ

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When the pure electric mining dump truck is working, it mainly ascends the slope at full load and descends the slope at no load. The loading state of the vehicle and the slope of the road will directly affect its axle load distribution and braking force distribution. In this paper, the slope dynamics analysis of the pure electric double-axle four-wheel drive mining dump truck was carried out. Based on the regenerative braking priority strategy, four regenerative braking control methods were developed based on the Matlab/Simulink platform and ADVISOR 2002 vehicle simulation software to study the ability of regenerative braking energy recovery and its impact on vehicle economic performance. The simulation results show that the regenerative braking priority control strategy used can maximize the regenerative braking force of the vehicle; the regenerative energy recovery capability of pure electric mining dump truck is proportional to the regenerative braking force that can be provided during braking; the two-axis braking strategy based on the I curve and the β line can make full use of the front and rear axle regenerative braking force when the braking intensity is large, and recover more braking energy; under road drive cycle, the single-axis braking force required to the braking strategy based on the maximized front axle braking force is the largest among all strategies, the motor braking efficiency is the highest, and the recovered braking energy is the most. For the studied drive cycle, the regenerative braking technology can reduce the vehicle energy consumption by 1.06%–1.56%. If appropriate measures are taken to improve the road surface condition and reduce the rolling resistance coefficient from f = 0.04 to f = 0.02, the regenerative braking technology can further reduce the vehicle energy consumption to 4.76%–5.73%. The economic performance of the vehicle is improved compared to no regenerative braking. In addition, the vehicle loading state and the driving motor working efficiency also directly affect the regenerative braking energy recovery capability of the pure electric mining truck.

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“…Smartization is one of the primary technological features of next-generation electric cars, and is an important technological tool for improving overall vehicle performance. The key technologies for vehicle smartization include drive-by-wire chassis systems [13], vehicle network-controlled electrical-electronic architectures, network vehicle controllers and their associated information security systems, cloud-based multi-source information fusion, decision-making, and control calibration, and intelligent driving assistance. The Hebei venue of the Beijing Winter Olympics is a mountainous area, complicating winter driving conditions due to steep gradients and ice-and snow-covered surfaces of the roads, and thus placing stringent demands on intelligent driving assistance.…”

Section: Increasing Vehicle Smartization: Research On Network Vehiclementioning

confidence: 99%

Key Technologies for System Engineering of Electric Commercial Vehicles

Sun¹,

He²

2018

Chinese Journal of Engineering Science

Self Cite

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The development of vehicles powered by novel energy sources has reached a worldwide consensus. In particular, purely electric vehicles have been identified as a national strategic emerging industry in China and a key field of the "China Manufacture 2025" program. This study reviews the history, technical roadmaps, and characteristics of the development of electric commercial vehicles in China. Based on the requirements of "zero-emission" and "24-hour uninterrupted and safe operation" borne from the Beijing Olympics, this paper proposes the concept of a systems engineering framework for electric commercial vehicles with three core components-the vehicular platform, battery charging/swapping stations, and operations monitoring. This study also introduces and investigates key technologies for these three components. Finally, this study discusses the main technical measures for improving the systems engineering of electric commercial vehicles to achieve a target of 5 million new energy vehicles by 2020 and operate at-25 °C during the Beijing Winter Olympics in 2022. The study aims to realize an electric commercial vehicle with superior performance and no technical defects that can be used without environmental restrictions.

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An integrated control strategy for the composite braking system of an electric vehicle with independently driven axles

Cited by 21 publications

References 10 publications

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

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

Research on Regenerative Braking of Pure Electric Mining Dump Truck

Key Technologies for System Engineering of Electric Commercial Vehicles

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