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The performance of lithium-ion batteries will decline dramatically with the increase in usage time, which will cause anxiety in using lithium-ion batteries. Some data-driven models have been employed to predict the remaining useful life (RUL) model of lithium-ion batteries. However, there are limitations to the accuracy and applicability of traditi...
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... In this research, the traditional easy-to-obtain input data were replaced by the health factors with the highest overall relevance as inputs to the neural network [37], combined with Pearson's correlation coefficient analysis and Spearman's correlation coefficient analysis for effective information extraction. Because the correlation is very high, the factor can better characterize the lithium-ion battery capacity degradation trend, the health factor as a neural network input, to obtain the neural network model [38]. ...
Capacity estimation of lithium-ion batteries is significant to achieving the effective establishment of the prognostics and health management (PHM) system of lithium-ion batteries. A capacity estimation model based on the variable activation function-long short-term memory (VAF-LSTM) algorithm is proposed to achieve the high-precision lithium-ion battery capacity estimation. By re-selecting each activation function, the proposed algorithm avoids the low estimation accuracy caused by the fixed activation function of the long short-term memory (LSTM) algorithm, and meanwhile, it can effectively speed up the convergence. The algorithm inputs consider two correlation coefficients so that the health factor with the highest correlation coefficient is chosen as the network input. The experimental data used for the experimental validation is the NASA public battery data under different temperature operating conditions. The validation results show that the estimation accuracy of the VAF-LSTM algorithm under different training sets is greatly improved compared with the traditional LSTM algorithm and the back propagation (BP) algorithm, and the average estimation accuracy can reach more than 97.5%. The improvement of estimation accuracy is also clearly demonstrated under the MAE, MSE, and RMSE. Therefore, the capacity estimation model will provide an important reference role in high-precision battery management systems.
... Data on the variation of the discharge capacity in relation to the number of operating cycles were used to train the model. Experimental results indicate that the model performs better than a single model formed using an LSTM-type network [20]. ...
Over the past decade, Unmanned Aerial Vehicles (UAVs) have begun to be increasingly used due to their untapped potential. Li-ion batteries are the most used to power electrically operated UAVs for their advantages, such as high energy density and the high number of operating cycles. Therefore, it is necessary to estimate the Remaining Useful Life (RUL) and the prediction of the Li-ion batteries’ capacity to prevent the UAVs’ loss of autonomy, which can cause accidents or material losses. In this paper, the authors propose a method of prediction of the RUL for Li-ion batteries using a data-driven approach. To maximize the performance of the process, the performance of three machine learning models, Support Vector Machine for Regression (SVMR), Multiple Linear Regression (MLR), and Random Forest (RF), were compared to estimate the RUL of Li-ion batteries. The method can be implemented within UAVs’ Predictive Maintenance (PdM) systems.
... It is well known that CNN focuses on capturing local spatial features, while LSTM specializes in modeling long-term dependencies in sequential data [11,[28][29][30][31][32]. Over the past few years, the coronavirus disease (COVID-19) pandemic significantly impacted people's travel behavior [28]. ...
... Furthermore, Koller et al. [30] utilized a combined CNN and LSTM network to recognize the meanings of different hand shapes and lip-reading representations in sign language videos. Chen et al. [31] successfully applied a CNN and LSTM network to predict the remaining useful life of lithium-ion batteries. In the context of traffic problems, Bogaerts et al. [11] utilized a hybrid CNN and LSTM network to train travel demand data for online car-hailing services, enabling effective prediction of traffic demand within a timeframe ranging from the next five minutes to two hours. ...
Intelligent transportation systems need to realize accurate traffic congestion prediction. The spatio-temporal features of traffic flow are essential to analyze and predict congestion. Our study proposes a data-driven model to predict the traffic congested flow. Firstly, the traffic zone/grid method is used to store the local area roads’ average speed of the vehicles. Second, the discrete snapshot set is proposed to characterize traffic flow’s spatial and temporal features over a continuous period. Third, the evolution of traffic congested flow in various time dimensions (weekly days, weekend days, and one week) is examined by transforming the global urban transportation network into traffic zones. Finally, the data-driven model is constructed to predict urban road traffic congestion by using the extracted spatio-temporal characteristics of traffic zones’ traffic flow, the snapshot set of which serves as inputs for this model. The model adopts the convolutional LSTM network to learn the temporal and local spatial features of traffic flow, while utilizing a convolutional neural network to effectively capture the global spatial features inherent in traffic flow. The numerical experiments are conducted on two cities’ transportation networks, and the results demonstrate that the performance of the proposed model outperforms traditional traffic flow prediction models.
... Ren et al. [41] presented an autoencoder integrated with a deep neural network (DNN) to predict lithium-ion batteries' RUL. Chen et al. [42] proposed a mixed model based on convolutional neural networks (CNN)-LSTM to select a health factor through gray correlation analysis and to achieve the prediction of RUL for lithium batteries. Liao et al. [43] presented a stochastic configuration network (SCN)-based method for predicting lithium-ion batteries' RUL. ...
Lithium-ion batteries are widely utilized in various fields, including aerospace, new energy vehicles, energy storage systems, medical equipment, and security equipment, due to their high energy density, extended lifespan, and lightweight design. Precisely predicting the remaining useful life (RUL) of lithium batteries is crucial for ensuring the safe use of a device. In order to solve the problems of unstable prediction accuracy and difficultly modeling lithium-ion battery RUL with previous methods, this paper combines a channel attention (CA) mechanism and long short-term memory networks (LSTM) to propose a new hybrid CA-LSTM lithium-ion battery RUL prediction model. By incorporating a CA mechanism, the utilization of local features in situations where data are limited can be improved. Additionally, the CA mechanism can effectively mitigate the impact of battery capacity rebound on the model during lithium-ion battery charging and discharging cycles. In order to ensure the full validity of the experiments, this paper utilized the National Aeronautics and Space Administration (NASA) and the University of Maryland Center for Advanced Life Cycle Engineering (CALCE) lithium-ion battery datasets and different prediction starting points for model validation. The experimental results demonstrated that the hybrid CA-LSTM lithium-ion battery RUL prediction model proposed in this paper exhibited a strong predictive performance and was minimally influenced by the prediction starting point.
... Currently, there are two common fault-tolerant structures in batteries [41][42][43]: seriesparallel fault-tolerant structure (SP) and parallel-series fault-tolerant structure (PS). This paper focuses on the study of the PS structure. ...
In terms of the battery management system of a mobile music speaker, reliability optimization has always been an important topic. This paper proposes a new dynamic redundant battery management algorithm based on the existing fault-tolerant structure of a lithium battery pack. The internal configuration is adjusted according to the SOC of each battery, and the power supply battery is dynamically allocated. This paper selects four batteries to experiment on with two different algorithms. The simulation results show that compared with the traditional battery management algorithm, the dynamic redundant battery management algorithm extends the battery pack working time by 18.75%, and the energy utilization rate of B 1 and B 4 increases by 96.0% and 99.8%, respectively. This proves that the dynamic redundant battery management algorithm can effectively extend battery working time and improve energy utilization.
