Auto-encoder LSTM for Li-ion SOH prediction: a comparative study on various benchmark datasets

Audin, Paul; Jorge, Inès; Mesbahi, Tedjani; Samet, Ahmed; Beuvron, François de Bertrand de; Boné, Romuald

doi:10.1109/icmla52953.2021.00246

Cited by 8 publications

(2 citation statements)

References 20 publications

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“…To capture changes in battery trajectory over time, temporal features were extracted to learn the health indices of the batteries. As mentioned earlier, this paper extracted five temporal features during charge cycles and three features during discharge cycles, as they have been shown to be efficient for predicting the SOH of a battery [43]. During the charge cycles, we extracted the time to maximum voltage and temperature, time to minimum current, and time charged under CC and CV modes.…”

Section: Health Indexmentioning

confidence: 99%

Prediction of State of Health of Lithium-Ion Battery Using Health Index Informed Attention Model

Wei

2023

Sensors

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State-of-health (SOH) is a measure of a battery’s capacity in comparison to its rated capacity. Despite numerous data-driven algorithms being developed to estimate battery SOH, they are often ineffective in handling time series data, as they are unable to utilize the most significant portion of a time series while predicting SOH. Furthermore, current data-driven algorithms are often unable to learn a health index, which is a measurement of the battery’s health condition, to capture capacity degradation and regeneration. To address these issues, we first present an optimization model to obtain a health index of a battery, which accurately captures the battery’s degradation trajectory and improves SOH prediction accuracy. Additionally, we introduce an attention-based deep learning algorithm, where an attention matrix, referring to the significance level of a time series, is developed to enable the predictive model to use the most significant portion of a time series for SOH prediction. Our numerical results demonstrate that the presented algorithm provides an effective health index and can precisely predict the SOH of a battery.

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Section: Health Indexmentioning

confidence: 99%

Prediction of State of Health of Lithium-Ion Battery Using Health Index Informed Attention Model

Wei

2023

Sensors

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“…The most common models include feedforward neural networks [18], recurrent neural networks [19], fuzzy logic [20], and support vector machines [21]. However, models such as random forest regression (RFR) [22], transformer NNs [23], LSTM, and autoencoders are considered promising models for precise estimation results [24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study-Based Data-Driven Models for Lithium-Ion Battery State-of-Charge Estimation

Hussein,

Esoofally,

Donekal

et al. 2024

Batteries

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Batteries have been considered a key element in several applications, ranging from grid-scale storage systems through electric vehicles to daily-use small-scale electronic devices. However, excessive charging and discharging will impair their capabilities and could cause their applications to fail catastrophically. Among several diagnostic indices, state-of-charge estimation is essential for evaluating a battery’s capabilities. Various approaches have been introduced to reach this target, including white, gray, and black box or data-driven battery models. The main objective of this work is to provide an extensive comparison of currently highly utilized machine learning-based estimation techniques. The paper thoroughly investigates these models’ architectures, computational burdens, advantages, drawbacks, and robustness validation. The evaluation’s main criteria were based on measurements recorded under various operating conditions at the Energy Systems Research Laboratory (ESRL) at FIU for the eFlex 52.8 V/5.4 kWh lithium iron phosphate battery pack. The primary outcome of this research is that, while the random forest regression (RFR) model emerges as the most effective tool for SoC estimation in lithium-ion batteries, there is potential to enhance the performance of simpler models through strategic adjustments and optimizations. Additionally, the choice of model ultimately depends on the specific requirements of the task at hand, balancing the need for accuracy with the complexity and computational resources available and how it can be merged with other SoC estimation approaches to achieve high precision.

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Dual-model approach for one-shot lithium-ion battery state of health sequence prediction

Arbaoui,

Samet,

Ayadi

et al. 2024

Array

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Auto-encoder LSTM for Li-ion SOH prediction: a comparative study on various benchmark datasets

Cited by 8 publications

References 20 publications

Prediction of State of Health of Lithium-Ion Battery Using Health Index Informed Attention Model

Prediction of State of Health of Lithium-Ion Battery Using Health Index Informed Attention Model

Comparative Study-Based Data-Driven Models for Lithium-Ion Battery State-of-Charge Estimation

Dual-model approach for one-shot lithium-ion battery state of health sequence prediction

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