Two-Time-Scale Braking Controller Design With Sliding Mode for Electric Vehicles Over CAN

Li, Wei; Zhu, Weiyu; Zhu, Xiaoyuan; Guo, Jianping

doi:10.1109/access.2019.2939412

Cited by 9 publications

(8 citation statements)

References 47 publications

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“…To avoid this, the adaptive SMC method acts as a good solution, which can maintain the braking efficiency without having negative effects on overall vehicle performance. Similar to adaptive SMC, the fuzzy SMC is also a modified SMC control method, which reduces chattering effectively with the parameter optimization FLC 107,108 . Another control strategy for RB in EVs and HEVs is the intelligent sliding mode scheme (ISMS), which has a basic logic torque limiter that maintains significant energy recovery without overcharging the battery pack and produces a good tracking of requested slip during an extreme braking situation with high braking performance 109,110 .…”

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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“…The proposed methodology shown in Figure 5 enhances vehicle stability by preventing skidding during braking operation. 15 The effective utilization of two types of electric braking methods with coordinative control strategy improves vehicle stability and jerk less braking operation due to reduced participation of mechanical brakes. 16 With the application of speed-based decentralized active disturbance rejection control technique, motor is braked with ultra-capacitor for fast speed as sufficient energy generated during braking and for low-speed motor braked by dissipative resistor.…”

Section: Optimization Based Strategiesmentioning

confidence: 99%

A review on braking control and optimization techniques for electric vehicle

Jamadar

Jadhav²

2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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In this modern world, due to limited fossil fuels, increasing cost, and environmental concerns a clean and sustainable transportation system in the form of electric vehicles (EV) is now becoming popular. On the other hand, electric vehicles have an advantage such as zero-emission, less maintenance, comfortable ride, less noise, etc. An electric vehicle is driven by an electric motor so considering driving patterns and road traffic conditions the vehicle often requires braking operation. The most common method is to employ mechanical brakes to reduce the speed of EV. However, mechanical braking mechanism results in friction which in turn, into heating losses and reduces the efficiency of EV. Therefore, regenerative braking system (RBS) and energy management system are employed in addition to mechanical braking for increasing the braking efficiency of the EV system. It is reported in many literatures about different regenerative braking control techniques for EV systems. This paper presents a review of regenerative braking and braking energy management techniques by considering different driving situations and road conditions.

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“…Caruntu et al [25] present the impact of network-induced delays to the oscillation of a vehicle drivetrain proposing a predictive controller to deal with time-varying delays. The impact of network-induced delays was taken into consideration in the lateral motion of vehicles [26] and the braking process of EVs [27]. The network-induced delays can deduct the control performance to the electrified powertrain system and may cause oscillation as well.…”

Section: Introductionmentioning

confidence: 99%

Robust Oscillation Suppression Control of Electrified Powertrain System Considering Mechanical-Electric-Network Effects

Zhu

et al. 2020

IEEE Access

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This paper works on the oscillation of electrified powertrain system in an integrated manner where combined mechanical-electric-network effects are all taking into consideration, and a robust oscillation controller is proposed to suppress the effects-caused oscillation and to maintain the system stability. An integrated model is developed in which driving motor, drivetrain and communication network are all included. Thus, torque ripples in the driving motor, nonlinear gear backlash as well as driveshaft flexibility in the drivetrain and network-induced delays that may cause powertrain system oscillation can be all considered. In order to dealing with the coupling effects of network-induced delays and event-driven manner of the controller nodes, a delay-free discrete model is further built via polytopic inclusion approach and system augmentation technique. An energy-to-peak performance based robust controller is proposed to ensure the torsional oscillation damping as well as vehicle speed tracking performance. During backlash mode, as the driving motor and the load are decoupled, a sliding mode compensator is further adopted to restrain the torsional oscillation. The stability of electrified powertrain control system is ensured by using Lyapunov theory, and the controller gain is obtained by solving a set of linear matrix inequalities (LMIs). Comparative simulation tests are carried out by using Matlab/Simulink in which a delicate controller area network (CAN) model is developed via SimEvent. The combined mechanical-electric-networked effects on torsional oscillation are demonstrated during the simulation tests, while the performance of the proposed controller is well verified. INDEX TERMS Electrified powertrain, nonlinear gear backlash, network-induced delays, robust energy-topeak controller, sliding mode compensator.

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Two-Time-Scale Braking Controller Design With Sliding Mode for Electric Vehicles Over CAN

Cited by 9 publications

References 47 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

A review on braking control and optimization techniques for electric vehicle

Robust Oscillation Suppression Control of Electrified Powertrain System Considering Mechanical-Electric-Network Effects

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