Energy-Optimal Braking Control Using a Double-Layer Scheme for Trajectory Planning and Tracking of Connected Electric Vehicles

Dong, Husheng; Zhuang, Weichao; Yin, Guodong; Xu, Liwei; Wang, Yan; Wang, Faan; Lu, Yingjie

doi:10.1186/s10033-021-00601-3

Cited by 23 publications

(12 citation statements)

References 27 publications

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“…The system works at 24 V DC. The potentiometers present the variable nature of renewable sources as analog inputs, which is acceptable as per [6].…”

Section: Microgrid Configurationmentioning

confidence: 99%

WITHDRAWN: Control Algorithm Validation Using PLC based Hardware Test-Bench for Stable Microgrid Automation

Mehta

Basak

2023

Preprint

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With the advancements in automation-based technology in engineering applications, programmable logic controller (PLC) has gained much attention. These PLCs possess real-time on-field applicability, reliable operation, long life, robust behavior and quick action in response to various inputs, multiple decision-making variables, harsh environmental conditions, etc. However, unlike the simulation software, tool boxes, and other setups, a robust and reconfigurable PLC can be employed to validate control algorithms for microgrids considering any geographical and environmental conditions. Thus, this paper proposes a PLC-based hardware test bench prototype as an effective solution for control algorithm validation aiming at power management problems and stable microgrid automation. The critical contribution of this paper includes the design and fabrication of the controller test bench prototype, abstracting it as the microgrid central controller (MGCC). The energy management system for the microgrid model with the fuzzy logic-based controller has been developed using MATLAB Simulink software. The performance of the proposed controller test bench is analyzed considering perturbations due to reduced renewable resource contribution. The results validate the proposed controller’s control algorithms, which can ensure the stable operation of the microgrid.

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“…The system works at 24 V DC. The potentiometers present the variable nature of renewable sources as analog inputs, which is acceptable as per [6].…”

Section: Microgrid Configurationmentioning

confidence: 99%

WITHDRAWN: Control Algorithm Validation Using PLC based Hardware Test-Bench for Stable Microgrid Automation

Mehta

Basak

2023

Preprint

View full text Add to dashboard Cite

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“…Therefore, the model parameter R 0 is defined as a constant, while that of R p and C p are timevarying. The OCV is obtained by the OCV-SOC relationship as in (1). The sampling frequency of voltage and current is set to 1Hz.…”

Section: Simulation Validationmentioning

confidence: 99%

“…Lithium-ion (Li-ion) batteries are widely used in Electric Vehicles (EVs) and stationary energy storage because of their high charge/discharge efficiency, low self-discharge rate, and long lifespan [1]- [3]. To extend the service life of the batteries and ensure their safe operation, a well-designed Battery Management System (BMS) is required to monitor the State of Health (SOH) and State of Charge (SOC) [4]- [6].…”

Section: Introductionmentioning

confidence: 99%

Online Identification of Lithium-ion Battery Model Parameters with Initial Value Uncertainty and Measurement Noise

Meng

Liu

et al. 2023

Chin. J. Mech. Eng.

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Online parameter identification is essential for the accuracy of the battery equivalent circuit model (ECM). The traditional recursive least squares (RLS) method is easily biased with the noise disturbances from sensors, which degrades the modeling accuracy in practice. Meanwhile, the recursive total least squares (RTLS) method can deal with the noise interferences, but the parameter slowly converges to the reference with initial value uncertainty. To alleviate the above issues, this paper proposes a co-estimation framework utilizing the advantages of RLS and RTLS for a higher parameter identification performance of the battery ECM. RLS converges quickly by updating the parameters along the gradient of the cost function. RTLS is applied to attenuate the noise effect once the parameters have converged. Both simulation and experimental results prove that the proposed method has good accuracy, a fast convergence rate, and also robustness against noise corruption.

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“…Model Predictive control (MPC), which can predict the future of the system while dealing with its constraints, has been applied to the field of robot control [6][7][8][9][10][11][12][13][14] . In [6], the authors designed a model based predictive control method for autonomous vehicle trajectory tracking, in which a successive linearization approach was using to obtain the linear error model of vehicle kinematics.…”

Section: Introductionmentioning

confidence: 99%

Design of ET-MPC-based Parameters Self-tuning Controller for Mobile Robot Based on Machine Learning

Liu

et al. 2022

Preprint

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In this paper, a model predictive control (MPC) algorithm framework based on machine learning (ML) is proposed for mobile robot system, which integrates event-triggered mechanism (ETM) and parameters self-tuning mechanism (PSM). Firstly, the kinematic model of the mobile robot is established, and the MPC-based path tracking controller is designed. Secondly, the PSM for MPC is designed based on the kernel extreme learning machine optimized by sparrow search algorithm (SSA-KELM). Then, two event-triggered mechanisms are designed to determine whether to perform optimization solution and parameters tuning, respectively. In addition, a state deviation compensation mechanism (SDCM) based on event-triggered MPC (ET-MPC) is designed. Finally, the theoretical result is applied to the actual mobile robot system. Compared to the basic MPC-based trajectory tracking controller, the controller designed in this study can obtain better control performance with less computing resources.

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Energy-Optimal Braking Control Using a Double-Layer Scheme for Trajectory Planning and Tracking of Connected Electric Vehicles

Cited by 23 publications

References 27 publications

WITHDRAWN: Control Algorithm Validation Using PLC based Hardware Test-Bench for Stable Microgrid Automation

WITHDRAWN: Control Algorithm Validation Using PLC based Hardware Test-Bench for Stable Microgrid Automation

Online Identification of Lithium-ion Battery Model Parameters with Initial Value Uncertainty and Measurement Noise

Design of ET-MPC-based Parameters Self-tuning Controller for Mobile Robot Based on Machine Learning

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