Comparison between two nonlinear Kalman Filters for reliable SoC estimation on a prototypal BMS

Luzi, Massimiliano; Paschero, Maurizio; Rossini, Angelo; Rizzi, Antonello; Mascioli, Fabio Massimo Frattale

doi:10.1109/iecon.2016.7794054

Cited by 17 publications

(8 citation statements)

References 17 publications

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“…As reported in the literature, KF is an efficient method for SoC estimation [25,52,53]. In particular, non-linear versions of KF, such as UKF and EKF have been largely used for this purpose as reviewed in [28], where the authors highlighted the improvements given by non-linear KF with a special emphasis on UKF.…”

Section: Soc Approximation With Non-linear Kalman Filtermentioning

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: Soc Approximation With Non-linear Kalman Filtermentioning

confidence: 99%

“…In order to design more effective BMSs, non-linear KFs are usually supported with accurate prediction models impacting on the computational costs of the procedure as discussed in [24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

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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“…In engineering practice, many algorithms such as open circuit voltage, Coulomb integral [9], neural network [10] and the Kalman filter [11,12] based methods have been proposed for SOC estimation, and the model-based Kalman filter and its extension algorithm such as the EKF and the UKF [13][14][15] are commonly used algorithms for SOC estimation. The core idea of the Kalman filter is to make an optimal estimate of the state of the dynamic system in terms of minimum variance, but the Kalman filter requires an accurate system model and complete knowledge of noise statistics.…”

Section: Introductionmentioning

confidence: 99%

State of Charge Estimation for Li-Ion Batteries Based on an Unscented H-Infinity Filter

Liu

Cai

Liu

et al. 2020

J. Electr. Eng. Technol.

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The state of charge (SOC) of lithium-ion batteries reflects their remaining capacity. Accurate estimation of SOC helps battery safety and is beneficial to the efficient management of batteries. The charging and discharging processes of lithium-Ion batteries are very complicated, and it is difficult to obtain accurate SOC estimation results. Therefore, it is important to study improved algorithms for SOC estimation for this nonlinear non-Gaussian battery system. In this paper, we propose an unscented H-infinity filter (UHF) based SOC estimation method, which combines the advantages of both the unscented Kalman filter (UKF) and the H-infinity filter (HF). The UKF propagates the sigma points through the nonlinear system and does not need the first-order linear approximation of the system equation, while the HF can suppress the non-Gaussian noise in the system to the greatest extent. The proposed UHF based SOC estimation algorithm is verified and evaluated in the battery management system, and further optimized in practical problems. Experimental results show that the proposed UHF based algorithm can perform accurate SOC estimation for lithium-ion batteries, and is superior to the UKF based SOC estimation. Keywords Index terms-unscented kalman filterer • H-infinity filter • State of charge* Zhiwei He

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Comparison between two nonlinear Kalman Filters for reliable SoC estimation on a prototypal BMS

Cited by 17 publications

References 17 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

State of Charge Estimation for Li-Ion Batteries Based on an Unscented H-Infinity Filter

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