Online Capacity Estimation for Lithium-Ion Battery Cells via an Electrochemical Model-Based Adaptive Interconnected Observer

Allam, Anirudh; Onori, Simona

doi:10.1109/tcst.2020.3017566

Cited by 66 publications

(62 citation statements)

References 17 publications

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“…The anode observer estimates the lithium concentration in the anode ðb x 2 Þ and the anode diffusion coefficient ð b q 1 Þ. Further, the practical stability of the adaptive interconnected observer's estimation error dynamics has been proved analytically (Allam and Onori, 2020b), which ensures that the estimated variables converge around the respective true values within a bounded error ball of radius defined by the uncertainties in the aging-enhanced SPM.…”

Section: Model-based Observermentioning

confidence: 99%

“…It has to be pointed out that the continuous-time adaptive interconnected observer formulation (Allam and Onori, 2020b) cannot be directly implemented on an embedded controller. Since the sensor measurements in a real system are available at discrete sample times, the continuous-time system needs to be sampled at particular time intervals to obtain a discrete-time system.…”

Section: Model-based Observermentioning

confidence: 99%

“…By exploiting the relationship between capacity and power fade due to SEI layer growth at the anode, an aging-enhanced expression for the cell terminal voltage is derived as (Allam and Onori, 2020…”

Section: Electrochemical Modelingmentioning

confidence: 99%

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Pushing the Envelope in Battery Estimation Algorithms

2020

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Accurate estimation of lithium-ion battery health will (a) improve the performance and lifespan of battery packs in electric vehicles, spurring higher adoption rates, (b) determine the actual extent of battery degradation during usage, enabling a health-conscious control, and (c) assess the available battery life upon retiring of the vehicle to re-purpose the batteries for ''second-use'' applications. In this paper, the real-time validation of an advanced battery health estimation algorithm is demonstrated via electrochemistry, control theory, and batteryin-the-loop (BIL) experiments. The algorithm is an adaptive interconnected sliding mode observer, based on a battery electrochemical model, which simultaneously estimates the critical variables such as the state of charge (SOC) and state of health (SOH). The BIL experimental results demonstrate that the SOC/SOH estimates from the observer converge to an error of 2% with respect to their true values, in the face of incorrect initialization and sensor signal corruption.

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Section: Model-based Observermentioning

confidence: 99%

Section: Model-based Observermentioning

confidence: 99%

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Pushing the Envelope in Battery Estimation Algorithms

2020

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“…Their mean and covariance are w(k);N(q k ,Q k ) and v(k);N(r k ,R k ). Considering the variation in A k , B k , C k , and D k as time progresses and the error of the system linearization and discretization, an adaptive KF is used to estimate the state of the augmented system (24). This method is denoted as the AKFAS algorithm and shown in Figure 7.…”

Section: Collaborative Estimation Of Soc and Sohmentioning

confidence: 99%

“…This model uses the growth of the solid electrolyte interphase layer to estimate the SOC and aging-sensitive transport parameters. Allam and Onori 24 analyzes the battery electrochemical impedance spectroscopy (EIS) and designs an H∞ observer to estimate SOC by introducing the constant phase element into the traditional time domain circuit model. The online application of these methods in vehicles needs further research due to the need to measure battery chemistry for safety and convenience.…”

Section: Introductionmentioning

confidence: 99%

Augmented system model-based online collaborative determination of lead–acid battery states for energy management of vehicles

Wang

Huang

Pan

et al. 2021

Measurement and Control

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State of charge (SOC) and state of health (SOH) of batteries are the indispensable control decision variables for online energy management system (EMS) in modern internal combustion engine vehicles. The real-time and accurate determination of SOC and SOH is essential to the reliability and safety of EMS operation. Obtaining good accuracy for the SOC estimation is difficult without considering SOH because of their coupling relationship. Although several works on the joint estimation of SOC and SOH of lithium–ion batteries are available, these studies cannot be applied to lead–acid batteries because of the differences in physical structure and characteristics. This study handles the problem of modeling the relationship between SOC and SOH of lead–acid battery and their online collaborative estimation. First, the structure and control strategy of a bus-based EMS is discussed, and the improper energy control actions of EMS due to the inaccurate SOC estimation are analyzed. Second, an instantaneous correlation factor β for SOC and SOH is defined as a new state estimating variable, and the simplified linear relationship model between β and open circuit voltage is established through the battery experiments. Third, a discretized augmented system equation of β is deduced according to the relationship model and the Randles circuit model. The least square circuit parameter identification (LSCPI) algorithm is presented to identify the time-varying circuit model parameters, while the adaptive Kalman filter for augmented system (AKFAS) algorithm is employed to estimate β online. A collaborative estimation algorithm is proposed on the basis of the LSCPI and AKFAS to determine SOC and SOH of lead–acid battery in real time, and a demo intelligent battery sensor is developed for its implementation. The results of battery charging and discharging experiments indicate that the proposed method has high accuracy. The estimation accuracy of SOC of this method reaches 3.13%, which is 7% higher than that of the existing method.

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