Integrated framework for SOH estimation of lithium-ion batteries using multiphysics features

Son, Seho; Jeong, Siheon; Kwak, Eunji; Kim, Jun Hyeong; Oh, Ki‐Yong

doi:10.1016/j.energy.2021.121712

Cited by 70 publications

(12 citation statements)

References 52 publications

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“…Thelen et al, 28 however, used less but more detailed data, including a DVA of half cells, and hence, reported a lower RMSE of 0.74% at estimating the SOH at 37 °C or 55 °C. Similarly, Son et al 60 demonstrated that incorporating more detailed features, such as mechanical and electrochemical responses, in the form of health indicators optimized a PINN to estimate the SOH with a lower RMSE of 0.49%. In contrast, the current study utilizes easily derivable signals during operation and does not require complex measurements to initialize the method, thereby competing with the PIML model developed by Kohtz et al 30 The latter model processes charging voltage segments and estimates the SEI thickness, which is directly mapped to the SOH, with an RMSE of 8.97% at an unseen test case at 30 °C.…”

Section: Resultsmentioning

confidence: 98%

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Physics-Informed Neural Networks for State of Health Estimation in Lithium-Ion Batteries

Hofmann,

Hamar,

Rogge

et al. 2023

J. Electrochem. Soc.

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One of the most challenging tasks of modern battery management systems is the accurate state of health estimation. While physico-chemical models are accurate, they have high computational cost. Neural networks lack physical interpretability but are efficient. Physics-informed neural networks tackle the aforementioned shortcomings by combining the efficiency of neural networks with the accuracy of physico-chemical models. A physics-informed neural network is developed and evaluated against three different datasets: A pseudo-two-dimensional Newman model generates data at various state of health points. This dataset is fused with experimental data from laboratory measurements and vehicle field data to train a neural network in which it exploits correlation from internal modeled states to the measurable state of health. The resulting physics-informed neural network performs best with the synthetic dataset and achieves a root mean squared error below 2% at estimating the state of health. The root mean squared error stays within 3% for laboratory test data, with the lowest error observed for constant current discharge samples. The physics-informed neural network outperforms several other purely data-driven methods and proves its advantage. The inclusion of physico-chemical information from simulation increases accuracy and further enables broader application ranges.

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Section: Resultsmentioning

confidence: 98%

“…61 The additional information from higher sampling rates positively contributes to the final performance. In other works, 28,30,42,43,60,62 the sample rate is not explicitly stated.…”

Section: Resultsmentioning

confidence: 99%

Physics-Informed Neural Networks for State of Health Estimation in Lithium-Ion Batteries

Hofmann,

Hamar,

Rogge

et al. 2023

J. Electrochem. Soc.

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“…However, further improvements based on the appropriate selection of the model hyperparameters should be undertaken for better estimation accuracy. Son et al [81] proposed a SOH estimation technique by applying an autoencoder model. The SOH estimation technique consists of three parts namely feature extraction, feature manipulation, and SOH estimation.…”

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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“…Ma et al [30] used the battery capacity in a specific window (the minimum embedding dimensions of the capacity data) as input features, and created a hybrid neural network that integrated a convolutional neural network and long short-term memory to predict battery lifetime. Son et al [31] employed a Gaussian process regression using multiphysics features including mechanical and impedance evolutionary responses to estimate the SOH of batteries. Even though these present methods provide satisfactory results in terms of battery life-time prediction, they often require data corresponding to at least 25 % aging in order to accurately estimate the target value.…”

Section: Introductionmentioning

confidence: 99%

“…used the battery capacity in a specific window (the minimum embedding dimensions of the capacity data) as input features, and created a hybrid neural network that integrated a convolutional neural network and long short‐term memory to predict battery life‐time. Son et al [31] . employed a Gaussian process regression using multiphysics features including mechanical and impedance evolutionary responses to estimate the SOH of batteries.…”

Section: Introductionmentioning

confidence: 99%

Improved Battery Cycle Life Prediction Using a Hybrid Data‐Driven Model Incorporating Linear Support Vector Regression and Gaussian

et al. 2022

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The ability to accurately predict lithium-ion battery life-time already at an early stage of battery usage is critical for ensuring safe operation, accelerating technology development, and enabling battery second-life applications. Many models are unable to effectively predict battery life-time at early cycles due to the complex and nonlinear degrading behavior of lithiumion batteries. In this study, two hybrid data-driven models, incorporating a traditional linear support vector regression (LSVR) and a Gaussian process regression (GPR), were developed to estimate battery life-time at an early stage, before more severe capacity fading, utilizing a data set of 124 battery cells with lifetimes ranging from 150 to 2300 cycles. Two type of hybrid models, here denoted as A and B, were proposed. For each of the models, we achieved 1.1 % (A) and 1.4 % (B) training error, and similarly, 8.3 % (A) and 8.2 % (B) test error. The two key advantages are that the error percentage is kept below 10 % and that very low error values for the training and test sets were observed when utilizing data from only the first 100 cycles.The proposed method thus appears highly promising for predicting battery life during early cycles.

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Integrated framework for SOH estimation of lithium-ion batteries using multiphysics features

Cited by 70 publications

References 52 publications

Physics-Informed Neural Networks for State of Health Estimation in Lithium-Ion Batteries

Physics-Informed Neural Networks for State of Health Estimation in Lithium-Ion Batteries

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

Improved Battery Cycle Life Prediction Using a Hybrid Data‐Driven Model Incorporating Linear Support Vector Regression and Gaussian

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