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Energy storage is recognized as a key technology for enabling the transition to a low-carbon, sustainable future. Energy storage requires careful management, and capacity prediction of a lithium-ion battery (LIB) is an essential indicator in a battery management system for Electric Vehicles and Electricity Grid Management. However, present techniqu...
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... dataset contains the ageing results of four LIBs named RW9, RW10, RW11, and RW12, acquired at room temperature. The general properties of the battery are summarised in Table 1. These four LIBs were cycled using two cycling protocols known as random walk cycling mode and reference charge and discharge cycling mode. ...Similar publications
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... The model structure of the pre-trained CNN model, is illustrated in Figure 10. The methodologies from Cho et al. [64], Li et al. [65], Wang et al. [66], El-Dalahmeh et al. [67], and Tan et al. [61] all focus on improving the performance, efficiency, and accuracy of battery technologies, specifically in predicting temperature for LIB cells, streamlining CNN models for better estimation performance with limited datasets, estimating the capacity of lithium-ion (Li-ion) cells, diagnosing degradation of LIBs through time-frequency image (TFI) analysis and transfer deep learning algorithms, and predicting the SOH in LIBs, respectively. The structure of the DCNN-ETL model is demonstrated in Figure 11. ...
... The architecture of one among n DCNN-TL models constituting the proposed DCNN-ETL mode is illustrated in Figure 12. Wang et al. [66] employ a cutting-edge methodology that leverages transfer learning and ensemble learning to estimate the capacity of Li-ion cells, and El-Dalahmeh et al. [67] propose a pioneering methodology that combines timefrequency image (TFI) analysis with a transfer deep learning algorithm to extract diagnostic attributes related to LIBs' degradation. Lastly, Ye et al. [68] focus on predicting the SOH in LIBs by addressing the issue of limited training data through transfer learning. ...
... Li et al. [65] and Wang et al. [66] utilize transfer learning and ensemble learning to improve estimation performance but may require a large source dataset for pre-training. El-Dalahmeh et al. [67] use TFI analysis and transfer deep learning to accurately predict the capacity of LIBs, but this may require more computational resources. Ye et al. [68] overcome the limitations of training data by integrating knowledge from one task to improve predictions in a related task, but this approach may have limitations in certain scenarios where there is not a strong foundation for transfer learning. ...
In recent years, the rapid evolution of transportation electrification has been propelled by the widespread adoption of lithium-ion batteries (LIBs) as the primary energy storage solution. The critical need to ensure the safe and efficient operation of these LIBs has positioned battery management systems (BMS) as pivotal components in this landscape. Among the various BMS functions, state and temperature monitoring emerge as paramount for intelligent LIB management. This review focuses on two key aspects of LIB health management: the accurate prediction of the state of health (SOH) and the estimation of remaining useful life (RUL). Achieving precise SOH predictions not only extends the lifespan of LIBs but also offers invaluable insights for optimizing battery usage. Additionally, accurate RUL estimation is essential for efficient battery management and state estimation, especially as the demand for electric vehicles continues to surge. The review highlights the significance of machine learning (ML) techniques in enhancing LIB state predictions while simultaneously reducing computational complexity. By delving into the current state of research in this field, the review aims to elucidate promising future avenues for leveraging ML in the context of LIBs. Notably, it underscores the increasing necessity for advanced RUL prediction techniques and their role in addressing the challenges associated with the burgeoning demand for electric vehicles. This comprehensive review identifies existing challenges and proposes a structured framework to overcome these obstacles, emphasizing the development of machine-learning applications tailored specifically for rechargeable LIBs. The integration of artificial intelligence (AI) technologies in this endeavor is pivotal, as researchers aspire to expedite advancements in battery performance and overcome present limitations associated with LIBs. In adopting a symmetrical approach, ML harmonizes with battery management, contributing significantly to the sustainable progress of transportation electrification. This study provides a concise overview of the literature, offering insights into the current state, future prospects, and challenges in utilizing ML techniques for lithium-ion battery health monitoring.
... To increase the superiority of diagnostic features derived under a random current profile, El-Dalahmeh et al. [43] proposed the continuous wavelet transforms (CWT) linked with a deep CNN to analyze the non-linearity and non-stationarity behavior of the LIB terminal voltage under a dynamic load profile in the time-frequency domain simultaneously. The proposed approach is based on converting the measured terminal voltage from the time-domain (1D) into a time-frequency image (2D scalogram) using the Morlet wavelet as a mother wavelet and then feeding the obtained image into CNN for features extraction based on the energy concentration in the acquired images. ...
... The adaptive EWT is a newly developed analysis technique and becoming a powerful tool for studying and analyzing the characteristics of the nonlinear and non-stationary signals in the time-frequency domain [50][51][52]. The superiority of using EWT instead of CWT [43] and DWT [45] is that no mother wavelet selection process is required. Therefore, the EWT can be used to develop a generic framework to analyze the characteristics of LIBs cycled at various dynamic operating conditions. ...
... LIBs have a long-life cycle, in which the model parameters change with the battery aging, resulting in insufficient accuracy of battery state estimation in the whole life cycle due to a mismatch of model parameters and adequate signals. Migration learning brings feasible solutions for battery models and signal enhancement [178][179][180][181]. X. Thang et al. [182] restored large-scale battery aging data sets by combining industrial data with accelerated aging tests through migration-based machine learning, reducing the cost of aging tests. ...
The state estimation technology of lithium-ion batteries is one of the core functions elements of the battery management system (BMS), and it is an academic hotspot related to the functionality and safety of the battery for electric vehicles. This paper comprehensively reviews the research status, technical challenges, and development trends of state estimation of lithium-ion batteries. First, the key issues and technical challenges of battery state estimation are summarized from three aspects of characteristics, models, and algorithms, and the technical challenges in state estimation are deeply analyzed. Second, four typical battery states (state of health, state of charge, state of energy, and state of power) and their joint estimation methods are reviewed, and feasible estimation frameworks are proposed, respectively. Finally, the development trends of state estimation are prospected. Advanced technologies such as artificial intelligence and cloud networking have further reshaped battery state estimation, bringing new methods to estimate the state of the battery under complex and extreme operating conditions. The research results provide a valuable reference for battery state estimation in the next-generation battery management system.
... It is expressed as a percentage and is 100% if the capacity of the battery is the same as the initial battery state. The SOH decreases when the battery is repeatedly used, and therefore, it can be used to estimate the remaining useful life of the battery in the battery management system [31]. The SOH is expressed as follows: ...
Lithium batteries are secondary batteries used as power sources in various applications, such as electric vehicles, portable devices, and energy storage devices. However, because explosions frequently occur during their operation, improving battery safety by developing battery management systems with excellent reliability and efficiency has become a recent research focus. The performance of the battery management system varies depending on the estimated accuracy of the state of charge (SOC) and state of health (SOH). Therefore, we propose a SOH and SOC estimation method for lithium–ion batteries in this study. The proposed method includes four neural network models—one is used to estimate the SOH, and the other three are configured as normal, caution, and fault neural network model banks for estimating the SOC. The experimental results demonstrate that the proposed method using the long short-term memory model outperforms its counterparts.
... An image recognition method that is conducive to deep learning and Naive Bayes algorithm was proposed in [15]. A multi-domain features time-frequency image (TFI) analysis and transfer deep learning algorithm for capacity prediction of lithium-ion batteries is defined in [16]. Recently, deep learning based on deep structure model, B Yepeng Guan ypguan@shu.edu.cn 1 such as recurrent neural network (RNN) [17], has greatly improved the performance of video frame prediction. ...
Deep learning based video prediction is challenging. The prediction network fails to make use of the useful information of each network layer and cannot establish a backtracking mechanism at present. A novel video prediction based on spatial information transfer and time backtracking (SITB) has been proposed. In order to transfer useful information to the next moment, the developed SITB network adaptively allocates weights according to the contribution of spatial information at each layer network. At the same time, a time backtracking mechanism is embedded in the network to correct the prediction error through feedback and reduce the prediction error of further future video frames. It helps the network to capture the long-term spatiotemporal change trend and enhances the spatiotemporal prediction ability of the network. The network loss function of backtracking mechanism is constructed by combining both forward and backward predictions. The proposed method is tested at some challenging datasets with vastly different practical meanings. The experimental results show that the developed method has excellent performance by comparisons with some state-of-the-art ones.
... Linear regression, support vector machine (SVM), Gaussian process (GP), deep learning (DL), and numerous classes of artificial neural networks (ANN) [18] are examples of data-driven algorithms that have been modified in the area of SOH estimation. For instance, the authors in [19] utilised the time-frequency image (TFI) as input for deep learning convolutional neural network to learn the correlation between the generated power of TFI and the capacity degradation of LIB. Moreover, ANN is proposed for SOH estimation of LIB because of its capability to handle the nonlinear data and provide high accuracy on unseen data [2]. ...
... The SOH is an indicator of performance degradation because of the battery, and the battery state can be measured on the basis of the SOH. The SOH refers to the current battery capacity as a percentage of the initial capacity, defined as follows [27]: ...
Lithium batteries are the most common energy storage devices in items such as electric vehicles, portable devices, and energy storage systems. However, if lithium batteries are not continuously monitored, their performance could degrade, their lifetime become shortened, or severe damage or explosion could be induced. To prevent such accidents, we propose a lithium battery state of health monitoring method and state of charge estimation algorithm based on the state of health results. The proposed method uses four neural network models. A neural network model was used for the state of health diagnosis using a multilayer neural network model. The other three neural network models were configured as neural network model banks, and the state of charge was estimated using a multilayer neural network or long short-term memory. The three neural network model banks were defined as normal, caution, and fault neural network models. Experimental results showed that the proposed method using the long short-term memory model based on the state of health diagnosis results outperformed the counterpart methods.
