899 research outputs found
Lithium-ion battery digitalization: Combining physics-based models and machine learning
Digitalization of lithium-ion batteries can significantly advance the performance improvement of lithium-ion
batteries by enabling smarter controlling strategies during operation and reducing risk and expenses in the
design and development phase. Accurate physics-based models play a crucial role in the digitalization of lithium-ion batteries by providing an in-depth understanding of the system. Unfortunately, the high accuracy comes at
the cost of increased computational cost preventing the employment of these models in real-time applications
and for parametric design. Machine learning models have emerged as powerful tools that are increasingly being
used in lithium-ion battery studies. Hybrid models can be developed by integrating physics-based models and
machine learning algorithms providing high accuracy as well as computational efficiency. Therefore, this paper
presents a comprehensive review of the current trends in integration of physics-based models and machine
learning algorithms to accelerate the digitalization of lithium-ion batteries. Firstly, the current direction in
explicit modeling methods and machine learning algorithms used in battery research are reviewed. Then a
thorough investigation of contemporary hybrid models is presented addressing both battery design and development as well as real-time monitoring and control. The objective of this work is to provide details of hybrid
methods including the various applications, type of employed models and machine learning algorithms, the
architecture of hybrid models, and the outcome of the proposed models. The challenges and research gaps are
discussed aiming to provide inspiration for future works in this field
A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries.
As widely used for secondary energy storage, lithium-ion batteries have become the core component of the power supply system and accurate remaining useful life prediction is the key to ensure its reliability. Because of the complex working characteristics of lithium-ion batteries as well as the model parameter changing along with the aging process, the accuracy of the online remaining useful life prediction is difficult but urgent to be improved for the reliable power supply application. The deep learning algorithm improves the accuracy of the remaining useful life prediction, which also reduces the characteristic testing time requirement, providing the possibility to improve the power profitability of predictive energy management. This article analyzes, reviews, classifies, and compares different adaptive mathematical models on deep learning algorithms for the remaining useful life prediction. The features are identified for the modeling ability, according to which the adaptive prediction methods are classified. The specific criteria are defined to evaluate different modeling accuracy in the deep learning calculation procedure. The key features of effective life prediction are used to draw relevant conclusions and suggestions are provided, in which the high-accuracy deep convolutional neural network — extreme learning machine algorithm is chosen to be utilized for the stable remaining useful life prediction of lithium-ion batteries
Critical review on improved electrochemical impedance spectroscopy-cuckoo search-elman neural network modeling methods for whole-life-cycle health state estimation of lithium-ion battery energy storage systems.
Efficient and accurate health state estimation is crucial for lithium-ion battery (LIB) performance monitoring and economic evaluation. Effectively estimating the health state of LIBs online is the key but is also the most difficult task for energy storage systems. With high adaptability and applicability advantages, battery health state estimation based on data-driven techniques has attracted extensive attention from researchers around the world. Artificial neural network (ANN)-based methods are often used for state estimations of LIBs. As one of the ANN methods, the Elman neural network (ENN) model has been improved to estimate the battery state more efficiently and accurately. In this paper, an improved ENN estimation method based on electrochemical impedance spectroscopy (EIS) and cuckoo search (CS) is established as the EIS-CS-ENN model to estimate the health state of LIBs. Also, the paper conducts a critical review of various ANN models against the EIS-CS-ENN model. This demonstrates that the EIS-CS-ENN model outperforms other models. The review also proves that, under the same conditions, selecting appropriate health indicators (HIs) according to the mathematical modeling ability and state requirements are the keys in estimating the health state efficiently. In the calculation process, several evaluation indicators are adopted to analyze and compare the modeling accuracy with other existing methods. Through the analysis of the evaluation results and the selection of HIs, conclusions and suggestions are put forward. Also, the robustness of the EIS-CS-ENN model for the health state estimation of LIBs is verified
Hybrid Neural Networks for Enhanced Predictions of Remaining Useful Life in Lithium-Ion Batteries
With the proliferation of electric vehicles (EVs) and the consequential increase in EV battery circulation, the need for accurate assessments of battery health and remaining useful life (RUL) is paramount, driven by environmentally friendly and sustainable goals. This study addresses this pressing concern by employing data-driven methods, specifically harnessing deep learning techniques to enhance RUL estimation for lithium-ion batteries (LIB). Leveraging the Toyota Research Institute Dataset, consisting of 124 lithium-ion batteries cycled to failure and encompassing key metrics such as capacity, temperature, resistance, and discharge time, our analysis substantially improves RUL prediction accuracy. Notably, the convolutional long short-term memory deep neural network (CLDNN) model and the transformer LSTM (temporal transformer) model have emerged as standout remaining useful life (RUL) predictors. The CLDNN model, in particular, achieved a remarkable mean absolute error (MAE) of 84.012 and a mean absolute percentage error (MAPE) of 25.676. Similarly, the temporal transformer model exhibited a notable performance, with an MAE of 85.134 and a MAPE of 28.7932. These impressive results were achieved by applying Bayesian hyperparameter optimization, further enhancing the accuracy of predictive methods. These models were bench-marked against existing approaches, demonstrating superior results with an improvement in MAPE ranging from 4.01% to 7.12%
Cerberus: A Deep Learning Hybrid Model for Lithium-Ion Battery Aging Estimation and Prediction Based on Relaxation Voltage Curves
The degradation process of lithium-ion batteries is intricately linked to
their entire lifecycle as power sources and energy storage devices,
encompassing aspects such as performance delivery and cycling utilization.
Consequently, the accurate and expedient estimation or prediction of the aging
state of lithium-ion batteries has garnered extensive attention. Nonetheless,
prevailing research predominantly concentrates on either aging estimation or
prediction, neglecting the dynamic fusion of both facets. This paper proposes a
hybrid model for capacity aging estimation and prediction based on deep
learning, wherein salient features highly pertinent to aging are extracted from
charge and discharge relaxation processes. By amalgamating historical capacity
decay data, the model dynamically furnishes estimations of the present capacity
and forecasts of future capacity for lithium-ion batteries. Our approach is
validated against a novel dataset involving charge and discharge cycles at
varying rates. Specifically, under a charging condition of 0.25C, a mean
absolute percentage error (MAPE) of 0.29% is achieved. This outcome underscores
the model's adeptness in harnessing relaxation processes commonly encountered
in the real world and synergizing with historical capacity records within
battery management systems (BMS), thereby affording estimations and
prognostications of capacity decline with heightened precision.Comment: 3 figures, 1 table, 9 page
Integrating Physics-Based Modeling with Machine Learning for Lithium-Ion Batteries
Mathematical modeling of lithium-ion batteries (LiBs) is a primary challenge
in advanced battery management. This paper proposes two new frameworks to
integrate physics-based models with machine learning to achieve high-precision
modeling for LiBs. The frameworks are characterized by informing the machine
learning model of the state information of the physical model, enabling a deep
integration between physics and machine learning. Based on the frameworks, a
series of hybrid models are constructed, through combining an electrochemical
model and an equivalent circuit model, respectively, with a feedforward neural
network. The hybrid models are relatively parsimonious in structure and can
provide considerable voltage predictive accuracy under a broad range of
C-rates, as shown by extensive simulations and experiments. The study further
expands to conduct aging-aware hybrid modeling, leading to the design of a
hybrid model conscious of the state-of-health to make prediction. The
experiments show that the model has high voltage predictive accuracy throughout
a LiB's cycle life.Comment: 15 pages, 10 figures, 2 tables. arXiv admin note: text overlap with
arXiv:2103.1158
Neural Network-Based Li-Ion Battery Aging Model at Accelerated C-Rate
Lithium-ion (Li-ion) batteries are widely used in electric vehicles (EVs) because of their
high energy density, low self-discharge, and superior performance. Despite this, Li-ion batteries’
performance and reliability become critical as they lose their capacity with increasing charge and
discharging cycles. Moreover, Li-ion batteries are subject to aging in EVs due to load variations in
discharge. Monitoring the battery cycle life at various discharge rates would enable the battery
management system (BMS) to implement control parameters to resolve the aging issue. In this
paper, a battery lifetime degradation model is proposed at an accelerated current rate (C-rate).
Furthermore, an ideal lifetime discharge rate within the standard C-rate and beyond the C-rate is
proposed. The consequence of discharging at an accelerated C-rate on the cycle life of the batteries
is thoroughly investigated. Moreover, the battery degradation model is investigated with a deep
learning algorithm-based feed-forward neural network (FNN), and a recurrent neural network
(RNN) with long short-term memory (LSTM) layer. A comparative assessment of performance of
the developed models is carried out and it is shown that the LSTM-RNN battery aging model has
superior performance at accelerated C-rate compared to the traditional FNN network
Battery state-of-charge estimation using machine learning analysis of ultrasonic signatures
The potential of acoustic signatures to be used for State-of-Charge (SoC) estimation is demonstrated using artificial neural network regression models. This approach represents a streamlined method of processing the entire acoustic waveform instead of performing manual, and often arbitrary, waveform peak selection. For applications where computational economy is prioritised, simple metrics of statistical significance are used to formally identify the most informative waveform features. These alone can be exploited for SoC inference. It is further shown that signal portions representing both early and late interfacial reflections can correlate highly with the SoC and be of predictive value, challenging the more common peak selection methods which focus on the latter. Although later echoes represent greater through-thickness coverage, and are intuitively more information-rich, their presence is not guaranteed. Holistic waveform treatment offers a more robust approach to correlating acoustic signatures to electrochemical states. It is further demonstrated that transformation into the frequency domain can reduce the dimensionality of the problem significantly, while also improving the estimation accuracy. Most importantly, it is shown that acoustic signatures can be used as sole model inputs to produce highly accurate SoC estimates, without any complementary voltage information. This makes the method suitable for applications where redundancy and diversification of SoC estimation approaches is needed. Data is obtained experimentally from a 210 mAh LiCoO2/graphite pouch cell. Mean estimation errors as low as 0.75% are achieved on a SoC scale of 0–100%
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