A NARX network optimized with an adaptive weighted square-root cubature Kalman filter for the dynamic state of charge estimation of lithium-ion batteries

Takyi‐Aninakwa, Paul; Wang, Shunli; Zhang, Hongying; Xiao, Yang; Fernandez, Carlos

doi:10.1016/j.est.2023.107728

Cited by 14 publications

(2 citation statements)

References 66 publications

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“…RNN is one type of NN with a loop structure, which can model the patterns with time-dependent behavior and approximate the transient response of the system. One type of RNN, the nonlinear autoregressive network with exogenous inputs (NARX), is used in this paper for the comparison study since it shows outstanding performance in modeling different system dynamics [28][29][30]. In this paper, the NARX model is constructed and trained by the Neural Net Time Series toolbox in MATLAB.…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

A Hybrid Neural Ordinary Differential Equation Based Digital Twin Modeling and Online Diagnosis for an Industrial Cooling Fan

Peng,

Chen

2023

Future Internet

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Digital twins can reflect the dynamical behavior of the identified system, enabling self-diagnosis and prediction in the digital world to optimize the intelligent manufacturing process. One of the key benefits of digital twins is the ability to provide real-time data analysis during operation, which can monitor the condition of the system and prognose the failure. This allows manufacturers to resolve the problem before it happens. However, most digital twins are constructed using discrete-time models, which are not able to describe the dynamics of the system across different sampling frequencies. In addition, the high computational complexity due to significant memory storage and large model sizes makes digital twins challenging for online diagnosis. To overcome these issues, this paper proposes a novel structure for creating the digital twins of cooling fan systems by combining with neural ordinary differential equations and physical dynamical differential equations. Evaluated using the simulation data, the proposed structure not only shows accurate modeling results compared to other digital twins methods but also requires fewer parameters and smaller model sizes. The proposed approach has also been demonstrated using experimental data and is robust in terms of measurement noise, and it has proven to be an effective solution for online diagnosis in the intelligent manufacturing process.

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Section: Recurrent Neural Networkmentioning

confidence: 99%

A Hybrid Neural Ordinary Differential Equation Based Digital Twin Modeling and Online Diagnosis for an Industrial Cooling Fan

Peng,

Chen

2023

Future Internet

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“…The integration of ML and MPC has experienced consistent growth in recent years, resulting in various categories of applications [45]- [47], e.g., offline modeling utilizes measurement data to create ML models for MPC [2], [48], [49]; online learning adjusts MPC model coefficients in real-time [26], [50], [51]; ML in imitation of MPC replicates MPC behavior in realtime, with successful applications in various industries [52]- [54] and improvements of computational efficiency [52], [55], [56]; ML in control structure of MPC involves ML as an add-on or embedded controller [57]- [59]; finally, MPC can also work as a safe learning controller in learning algorithms to address control constraints [60], [61]. Complex nonlinear interactions that may be difficult for conventional mathematical and statistical models to capture, particularly in complex systems, can be captured by ML-based models such as nonlinear autoregressive models with exogenous inputs (NARX) [62], [63], feed-forward neural networks (FNNs) [64], deep neural networks (DNNs) [65], and recurrent neural networks (RNNs) [66], can be used as process models, have the potential to effectively represent complicated physical systems, have demonstrated the ability to successfully simulate dynamic processes inside the MPC framework, giving precise approximations and quicker convergence in MPC. In this regard, it would be beneficial to place a stronger emphasis on MPC controllers utilizing NARX models, as this approach aligns with the methodology outlined in our paper.…”

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confidence: 99%

Neural-Network Based MPC for Enhanced Lateral Stability in Electric Vehicles

Hassan,

Ruiz-Moreno,

Frejo

et al. 2024

IEEE Access

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Distributed electric drive vehicles offer maneuver-ability but face stability challenges under different driving conditions. Model Predictive Control (MPC) algorithms can improve lateral stability, but their high computational demands hinder real-time implementation. To address this, the proposed strategy combines Nonlinear Autoregressive Exogenous (NARX) neural networks with MPC in two ways, namely, Nonlinear Prediction-Nonlinear Optimization (NMPC-NO) and Nonlinear Prediction-Linearization (MPC-NPL). While NMPC-NO involves online nonlinear optimization, MPC-NPL uses local linearization, reducing both the computational load significantly to about 40% of the computation time of MPC and 0.05% of that of nonlinear model predictive control (NMPC). The neural networks are trained and validated on 20 different datasets, with alternative training methods investigated. MATLAB/Simulink simulations under various standardized tests demonstrate the effectiveness of the proposed techniques, highlighting improved handling performance, reduced computation time, and real-time deployment capabilities. Recent research underscores Direct Yaw Moment Control (DYC) as effective for improving handling stability [6]-[8]. DYC comprises two control levels, with the upper controller

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A comprehensive review of state-of-charge and state-of-health estimation for lithium-ion battery energy storage systems

Tao,

Wang,

Cao

et al. 2024

Ionics

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A NARX network optimized with an adaptive weighted square-root cubature Kalman filter for the dynamic state of charge estimation of lithium-ion batteries

Cited by 14 publications

References 66 publications

A Hybrid Neural Ordinary Differential Equation Based Digital Twin Modeling and Online Diagnosis for an Industrial Cooling Fan

A Hybrid Neural Ordinary Differential Equation Based Digital Twin Modeling and Online Diagnosis for an Industrial Cooling Fan

Neural-Network Based MPC for Enhanced Lateral Stability in Electric Vehicles

A comprehensive review of state-of-charge and state-of-health estimation for lithium-ion battery energy storage systems

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