An electrochemical model-based particle filter approach for Lithium-ion battery estimation

Samadi, M. Foad; Alavi, Seyed Mohammad Mahdi; Saif, Mehrdad

doi:10.1109/cdc.2012.6426009

Cited by 35 publications

(18 citation statements)

References 14 publications

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“…It is worth mentioning that other advanced methods for SoC estimation such as Kalman Filtering [14], [16] and Particle Filtering [17] can readily be adopted within the proposed framework.…”

Section: Battery Modelmentioning

confidence: 99%

Nonlinear model predictive control for cell balancing in Li-ion battery packs

Samadi¹,

Saif

2014

2014 American Control Conference

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A major issue with Li-ion batteries hindering their widespread application in transportation industry is safety related concerns that should be effectively addressed within the battery management system design. Overcharge/discharge of the cells within a battery pack is one undesirable outcome that can affect the battery's health. Differences between the cells within a battery pack can lead to cell over-charge/discharge that is not detectable from battery pack's voltage. Thus, a cell balancing circuit is usually employed in battery packs in order to keep all the cells in balance. The control of cell-balancing circuits is mostly addressed by logical-based algorithms where the dynamical model of the system is not taken into account. This work considers the control problem of a cell-balancing circuit in a model-based framework by proposing a nonlinear model predictive control (NMPC). The NMPC problem is solved using a genetic algorithm and simulation studies show the effectiveness of the proposed algorithm.

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“…It is worth mentioning that other advanced methods for SoC estimation such as Kalman Filtering [14], [16] and Particle Filtering [17] can readily be adopted within the proposed framework.…”

Section: Battery Modelmentioning

confidence: 99%

Nonlinear model predictive control for cell balancing in Li-ion battery packs

Samadi¹,

Saif

2014

2014 American Control Conference

Self Cite

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“…In this paper, according to the results in Table 4, we notice that the order of the variables in the state transition is of 0.1. Therefore, the process noise is set as u i,k = 0.0001∀i ∈ [1,5]; and the observation noise is set as v k = 0.0001. Using the PF, we are able update the value of {a k , b k , c k , d k } given the latest measured SOH S k .…”

Section: The Methodsmentioning

confidence: 99%

“…In the model, the battery's SOH is linked to the battery's electrochemical parameters. For example, Samadi et al [5] developed an electrochemical-based aging model to estimate (State-of-charge) SOC and SOH of the battery. These model-based approaches can achieve high estimation accuracy, but they also require heavy work in the model development.…”

Section: Introductionmentioning

confidence: 99%

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Indirect State-of-Health Estimation for Lithium-Ion Batteries under Randomized Use

Tang

et al. 2017

Energies

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Lithium-ion batteries are widely used in many systems. Because they provide a power source to the whole system, their state-of-health (SOH) is very important for a system's proper operation. A direct way to estimate the SOH is through the measurement of the battery's capacity; however, this measurement during the battery's operation is not that easy in practice. Moreover, the battery is always running under randomized loading conditions, which makes the SOH estimation even more difficult. Therefore, this paper proposes an indirect SOH estimation method that relies on indirect health indicators (HIs) that can be measured easily during the battery's operation. These indicators are extracted from the battery's voltage and current and the number of cycles the battery has been through, which are far easier to measure than the battery's capacity. An empirical model based on an elastic net is developed to build the quantitative relationship between the SOH and these indirect HIs, considering the possible multi-collinearity between these HIs. To further improve the accuracy of SOH estimation, we introduce a particle filter to automatically update the model when capacity data are obtained occasionally. We use a real dataset to demonstrate our proposed method, showing quite a good performance of the SOH estimation. The results of the SOH estimation in the experiment are quite satisfactory, which indicates that the method is effective and accurate enough to be used in real practice.

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“…In [17], [18], [19] and [20], the Li ions concentration and the battery Statement of Charge (SOC) are estimated by using (extended) Kalman filters and observers. In [21] and [22], particle filters have been employed to estimate the Li ions transportation rate and the battery SOC. There are also some literature, utilizing the battery Equivalent Circuit Model (ECM) for diagnostics, [23], [13] and [24].…”

Section: Introductionmentioning

confidence: 99%

Plating Mechanism Detection in Lithium-ion batteries, by using a particle-filtering based estimation technique

Alavi

Samadi

Saif

2013

2013 American Control Conference

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Plating of metallic Lithium (Li) is an important battery degradation, failure mechanism, and a safety concern of Lithium-ion (Li-ion) batteries during charging process, which can short circuit the battery and lead to uncontrollable energetic chemical reactions. It happens when the transport rate of Li ions to the negative electrode exceeds the rate that Li ions can be inserted (intercalated) into the graphite. This paper presents a two-step Plating Mechanism Detection (PMD) algorithm which is based on the model-based fault diagnosis theory. In the first step, an estimation of the Li ions' transport rate in both positive and negative electrodes is obtained from an electrochemical model of the battery through a particle filtering approach. Estimated data are then compared with their boundary values in order to generate an appropriate fault alarm. Simulation results are provided to show the effectiveness of the proposed PMD algorithm.

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An electrochemical model-based particle filter approach for Lithium-ion battery estimation

Cited by 35 publications

References 14 publications

Nonlinear model predictive control for cell balancing in Li-ion battery packs

Nonlinear model predictive control for cell balancing in Li-ion battery packs

Indirect State-of-Health Estimation for Lithium-Ion Batteries under Randomized Use

Plating Mechanism Detection in Lithium-ion batteries, by using a particle-filtering based estimation technique

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