Electrochemical cell prognostics using online impedance measurements and model-based data fusion techniques

Kozlowski, J.D.

doi:10.1109/aero.2003.1234169

Cited by 97 publications

(66 citation statements)

References 8 publications

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“…SOL is referred to in the literature as the time when the battery must be replaced [12]. It is similar to SOH, but quantifies the remaining time until the battery will be unable to perform.…”

Section: Battery State Evaluationmentioning

confidence: 99%

“…SOC is similar to the fuel usage indication in gasoline cars, but the battery is inaccessible for measuring and experiences aging, varying environmental conditions, and charge-discharge cycles, which will makes it difficult for a BMS to provide an accurate SOC estimation. According to [12], SOH describes the percentage of battery life remaining. However, there is no consensus on the definition of SOH because it does not correspond to the measurement of a specific physical quality.…”

Section: Battery State Evaluationmentioning

confidence: 99%

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Battery Management Systems in Electric and Hybrid Vehicles

et al. 2011

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Abstract:The battery management system (BMS) is a critical component of electric and hybrid electric vehicles. The purpose of the BMS is to guarantee safe and reliable battery operation. To maintain the safety and reliability of the battery, state monitoring and evaluation, charge control, and cell balancing are functionalities that have been implemented in BMS. As an electrochemical product, a battery acts differently under different operational and environmental conditions. The uncertainty of a battery's performance poses a challenge to the implementation of these functions. This paper addresses concerns for current BMSs. State evaluation of a battery, including state of charge, state of health, and state of life, is a critical task for a BMS. Through reviewing the latest methodologies for the state evaluation of batteries, the future challenges for BMSs are presented and possible solutions are proposed as well.

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Section: Battery State Evaluationmentioning

confidence: 99%

Section: Battery State Evaluationmentioning

confidence: 99%

Battery Management Systems in Electric and Hybrid Vehicles

et al. 2011

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show abstract

“…In some studies the focus is directed towards hybrid EVs where state estimation techniques such as the extended Kalman filter are used [12,13]. Autoregressive integrated moving average and artificial neural networks are also used for both clustering of * Correspondence: turev@gtu.edu.tr measured data and estimation [14]. Dynamical models representing nonlinear potentials of lithium-ion batteries such as temperature changes, heat effects, and transient response are proposed in [15].…”

Section: Introductionmentioning

confidence: 99%

A parametric battery state of health estimation method for electric vehicle applications

Sarıkurt¹,

Ceylan²,

Baliikçi³

2017

Turk J Elec Eng & Comp Sci

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Lithium-ion batteries are commonly preferred in electric vehicle applications. The relative capacity and state of health of a battery decrease with age. Therefore, accurate estimation of these parameters is essential. In this study a parametrical approach for estimation of battery state of health is proposed. A hybrid battery model that has a maximum error less than 3% is used. The relative capacity of the battery is estimated by using performance decrement with age.The method is validated by two different set of experiments. The first set is conducted with batteries that were aged by a controlled process and the second set is conducted with randomly aged batteries. The proposed method works successfully in both conditions with maximum error less than 5%.

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“…Therefore, data-driven prognostics algorithm is relatively flexible for the SOH prediction of lithium battery. Kozlowski [9] proposed a data-driven approach that combines three predictors, auto-regressive moving average (ARMA), neural networks, and fuzzy-logic to predict the battery SOH. Guo [10] applied an improved nonlinear degradation Autoregressive to perform batteries SOH prognostics.…”

Section: Introductionmentioning

confidence: 99%

Gravitational search algorithm with Gaussian process for lithium-ion batteries state of health (SOH) estimation

Ye¹,

Zhang²,

Yang³

2015

Proceedings of the 4th International Conference on Information Technology and Management Innovation

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Keywords: Gaussian process regression, gravitational search algorithm optimization, lithium-ion battery, state of health. Abstract. State of health (SOH) estimation plays a significant role in battery prognostics. In this paper, Gaussian Process Regression (GPR) is used as a data-driven approach to perform SOH estimation, which supports uncertainty representation and management. At present, the hyper-parameters of GPR are optimized by conjugate gradient algorithm. However, the conjugate gradient algorithm has the shortcomings of too strong dependence on initial value and easily falling into local optimum. In order to improve the prediction precision and generalization ability of GPR, we utilized Gravitational Search Algorithm (GSA) replace of conjugate gradient to search the optimal hyper-parameters during the training process automatically then formed the GSA-GPR algorithm. Experimental results confirm that the proposed method can be effectively applied to lithium-ion batteries SOH estimation by quantitative comparison with the standard GPR algorithms, Genetic Algorithm (GA)-GPR and Particle Swarm Optimization (PSO)-GPR algorithms.

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Electrochemical cell prognostics using online impedance measurements and model-based data fusion techniques

Cited by 97 publications

References 8 publications

Battery Management Systems in Electric and Hybrid Vehicles

Battery Management Systems in Electric and Hybrid Vehicles

A parametric battery state of health estimation method for electric vehicle applications

Gravitational search algorithm with Gaussian process for lithium-ion batteries state of health (SOH) estimation

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