A Novel Neural Networks Ensemble Approach for Modeling Electrochemical Cells

Luzi, Massimiliano; Paschero, Maurizio; Rizzi, Antonello; Maiorino, Enrico; Mascioli, Fabio Massimo Frattale

doi:10.1109/tnnls.2018.2827307

Cited by 22 publications

(7 citation statements)

References 33 publications

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“…For the sake of completeness, in Table 6, the best PI-ENNC result on the NASA test set has been compared with the NN-based models discussed in [68] and the ECM optimized through an Hybrid-Genetic-PSO in [46]. The models selected for comparing the PI-ENNC are defined as follows:…”

Section: Resultsmentioning

confidence: 99%

“…The ELM has been set up counting ten hidden neurons with hyperbolic tangent activation function and one output neuron with linear activation. In accordance with the ELM technique, the weights of the hidden layer have been randomly initialized and the training of the output weights has been performed by solving a linear least square problem [68]. • WNN: a Wavelet Neural Network (WNN) (black box type) where the Morlet wavelet has been used as an activation function of the wavelons.…”

Section: Resultsmentioning

confidence: 99%

“…The WNN has been configured with ten hidden wavelons and one output neuron with the linear activation function. The training procedure has been performed with the Nadam optimizer and trained for 10,020 epochs [68].…”

Section: Resultsmentioning

confidence: 99%

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A Physically Inspired Equivalent Neural Network Circuit Model for SoC Estimation of Electrochemical Cells

et al. 2021

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Battery Management System (BMS) design for Lithium-ion batteries State of Charge (SoC) prediction has a crucial role in Electric Vehicles (EVs) and smart grids development. The need to design compact, light and fast devices requires finding a suitable trade-off between effectiveness and efficiency. In the literature, it is well emphasized that the application of electrochemical-based methods such as the Pseudo-Two-Dimensional (P2D) model is computationally prohibitive and requires significant simplifications. Conversely, plain Equivalent Circuit Models (ECM) are too simple and unable to represent the cell dynamics. The application of an Ensemble Neural Network (ENN) as Equivalent Neural Network Circuit (ENNC) emerged as a promising solution able to synthesize expressive and computationally efficient models. Indeed, with the support of a suitable dataset, an ENN can be configured to represent a given ECM, modeling each lumped parameter through an assigned Neural Network (NN). Accordingly, the ENNC system is able to keep a physical description of the battery cell while approximating the non-linear dynamic of each component. The paper proposes a novel ENNC battery named Physical Inspired-Equivalent Neural Network Circuit (PI-ENNC) whose ensemble architecture relies on a fractional-order Extended Single Particle (ESP) Lithium-ion cell formulation. The PI-ENNC is designed to approximate the ESP transfer functions referred to the ohmic effects, the electrolyte diffusion and the non-uniform charge distribution in the cell. The proposed model has been tested with three publicly available datasets, investigating the model behavior according to two different training strategies and with different input configurations. In order to prove its effectiveness, results have been compared with a simpler version proposed in a previous work. Results highlight the effectiveness of PI-ENNC in SoC prediction, underlining the importance of designing an ENN architecture that leverages on equations and constraints that reflect the physical phenomena of the cell.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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A Physically Inspired Equivalent Neural Network Circuit Model for SoC Estimation of Electrochemical Cells

et al. 2021

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“…The dataset is publicly available on IEEE DataPort [33]. Detailed description can be found in [24], [25], [26], [27], [28], [29], and [30]. [1].…”

Section: A Dataset Descriptionmentioning

confidence: 99%

Improving Stability of Adversarial Li-ion Cell Usage Data Generation using Generative Latent Space Modelling

Chattoraj

Pratiher

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The quality and quantity of cell usage data (CUD) availability are crucial for reliable lithium-ion (Li-ion) battery modeling. Further, the model needs to encompass the non-linear and complex system dynamics, such as diverse aging mechanisms and dynamic operating characteristics. In general, the CUD acquisition from the electrochemical energy storage systems is a time-dependent, tedious, and lengthy, expensive process, which is often noise-corrupted with spurious outliers. Outliers' robust, realistic synthetic CUD generation is essential for accelerating domain-specific technological developments. Time-series generative adversarial networks (TimeGAN) have been the state-ofthe-art for latent space sequential data modeling by optimizing both the adversarial and supervised objectives while preserving the multivariate sequences' temporal correlation dynamics [1]. The original TimeGAN formulation adopts the binary crossentropy loss function, leading to vanishing gradient stability problems during the training process [2], [3]. Least-squares based formulation overcome such an issue without considering outliers influence [4]. In this treatise, some robust loss-functions for the TimeGAN architecture are explored for generating realistic Liion CUD. Extensive experimental validation on publicly available datasets illustrates the amended TimeGAN framework's improved stability w.r.t generator and discriminator scores.

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“…The process of these approaches can be divided into three procedures: model building, identification of model parameters, and SOC estimation [6]. Many battery models have been reported to date, e.g., the electrochemical model [16], equivalent circuit models (ECMs) [17,18], and neural network models [19][20][21][22]. The electrochemical model is rather complex and requires a large number of parameters for SOC estimation [16].…”

Section: Introductionmentioning

confidence: 99%

Online Parameters Identification and State of Charge Estimation for Lithium-Ion Battery Using Adaptive Cubature Kalman Filter

Luo

Tan

et al. 2021

WEVJ

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The state of charge (SOC) of a lithium-ion battery plays a key role in ensuring the charge and discharge energy control strategy, and SOC estimation is the core part of the battery management system for safe and efficient driving of electric vehicles. In this paper, a model-based SOC estimation strategy based on the Adaptive Cubature Kalman filter (ACKF) is studied for lithium-ion batteries. In the present study, the dual polarization (DP) model is employed for SOC estimation and the vector forgetting factor recursive least squares (VRLS) method is utilized for model parameter online identification. The ACKF is then designed to estimate the battery’s SOC. Finally, the Urban Dynamometer Driving Schedule and Dynamic Stress Test are utilized to evaluate the performance of the proposed method by comparing with results obtained using the extended Kalman filter (EKF) and the cubature Kalman filter (CKF) algorithms. The simulation and experimental results show that the proposed ACKF algorithm combined with VRLS-based model identification is a promising SOC estimation approach. The proposed algorithm is found to provide more accurate SOC estimation with satisfying stability than the extended EKF and CKF algorithms.

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A Novel Neural Networks Ensemble Approach for Modeling Electrochemical Cells

Cited by 22 publications

References 33 publications

A Physically Inspired Equivalent Neural Network Circuit Model for SoC Estimation of Electrochemical Cells

A Physically Inspired Equivalent Neural Network Circuit Model for SoC Estimation of Electrochemical Cells

Improving Stability of Adversarial Li-ion Cell Usage Data Generation using Generative Latent Space Modelling

Online Parameters Identification and State of Charge Estimation for Lithium-Ion Battery Using Adaptive Cubature Kalman Filter

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