Battery state of health estimation method based on sparse auto‐encoder and backward propagation fading diversity among battery cells

Sun, Yening; Zhang, Jinlong; Zhang, Kaifei; Qi, Hanhong; Zhang, Chunjiang

doi:10.1002/er.6346

Cited by 16 publications

(5 citation statements)

References 48 publications

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“…However, the performance of the autoencoder hinges on the careful choice of hyperparameters, which influences how different inputs activate specific nodes. While enforcing sparsity enhances feature discernment, it also increases computational complexity, underscoring the need for a balanced approach to hyperparameter tuning for optimal efficiency [95][96][97]. Denoising autoencoders (DAEs) are another set of popular AE models that use partially damaged input and training to recover the original, undistorted image.…”

Section: Autoencoders (Aes)mentioning

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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Section: Autoencoders (Aes)mentioning

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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show abstract

“…The model typically consists of two components: the encoder and the decoder. AE is a useful tool for recognizing and tracking characteristics of battery aging and is capable of handling the varied and inconsistent nature of battery decay and capacity loss [209,210]. Wu et al developed a combined convolutional auto-encoder (CAE) and recursive auto-encoder (RAE) framework to extract health features from voltage and temperature profiles undercharging and used the AdaBoost, an Ensemble learning method, to construct SOH estimation models [211].…”

Section: Un-supervised Learningmentioning

confidence: 99%

Battery State of Health Estimate Strategies: From Data Analysis to End-Cloud Collaborative Framework

Yang

Zhang

et al. 2023

Batteries

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Lithium-ion batteries have become the primary electrical energy storage device in commercial and industrial applications due to their high energy/power density, high reliability, and long service life. It is essential to estimate the state of health (SOH) of batteries to ensure safety, optimize better energy efficiency and enhance the battery life-cycle management. This paper presents a comprehensive review of SOH estimation methods, including experimental approaches, model-based methods, and machine learning algorithms. A critical and in-depth analysis of the advantages and limitations of each method is presented. The various techniques are systematically classified and compared for the purpose of facilitating understanding and further research. Furthermore, the paper emphasizes the prospect of using a knowledge graph-based framework for battery data management, multi-model fusion, and cooperative edge-cloud platform for intelligent battery management systems (BMS).

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“…Sun et al [80] developed a sparse autoencoder and BPNN-based SOH prediction technique. The input data framework for the model training was conducted by using the battery voltage extracted during the later phase of the charging path.…”

Section: Autoencoder-based Soh Estimation Approachesmentioning

confidence: 99%

Intelligent SOX Estimation for Automotive Battery Management Systems: State-of-the-Art Deep Learning Approaches, Open Issues, and Future Research Opportunities

et al. 2022

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Real-time battery SOX estimation including the state of charge (SOC), state of energy (SOE), and state of health (SOH) is the crucial evaluation indicator to assess the performance of automotive battery management systems (BMSs). Recently, intelligent models in terms of deep learning (DL) have received massive attention in electric vehicle (EV) BMS applications due to their improved generalization performance and strong computation capability to work under different conditions. However, estimation of accurate and robust SOC, SOH, and SOE in real-time is challenging since they are internal battery parameters and depend on the battery’s materials, chemical reactions, and aging as well as environmental temperature settings. Therefore, the goal of this review is to present a comprehensive explanation of various DL approaches for battery SOX estimation, highlighting features, configurations, datasets, battery chemistries, targets, results, and contributions. Various DL methods are critically discussed, outlining advantages, disadvantages, and research gaps. In addition, various open challenges, issues, and concerns are investigated to identify existing concerns, limitations, and challenges. Finally, future suggestions and guidelines are delivered toward accurate and robust SOX estimation for sustainable operation and management in EV operation.

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Battery state of health estimation method based on sparse auto‐encoder and backward propagation fading diversity among battery cells

Cited by 16 publications

References 48 publications

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

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

Battery State of Health Estimate Strategies: From Data Analysis to End-Cloud Collaborative Framework

Intelligent SOX Estimation for Automotive Battery Management Systems: State-of-the-Art Deep Learning Approaches, Open Issues, and Future Research Opportunities

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