220 research outputs found
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%
Data-Driven Methods for the State of Charge Estimation of Lithium-Ion Batteries: An Overview
In recent years, there has been a noticeable shift towards electric mobility and an increasing emphasis on integrating renewable energy sources. Consequently, batteries and their management have been prominent in this context. A vital aspect of the BMS revolves around accurately determining the battery pack’s SOC. Notably, the advent of advanced microcontrollers and the availability of extensive datasets have contributed to the growing popularity and practicality of data-driven methodologies. This study examines the developments in SOC estimation over the past half-decade, explicitly focusing on data-driven estimation techniques. It comprehensively assesses the performance of each algorithm, considering the type of battery and various operational conditions. Additionally, intricate details concerning the models’ hyperparameters, including the number of layers, type of optimiser, and neuron, are provided for thorough examination. Most of the models analysed in the paper demonstrate strong performance, with both the MAE and RMSE for the estimation of SOC hovering around 2% or even lower
Remaining Useful Life Prediction of Lithium-ion Batteries using Spatio-temporal Multimodal Attention Networks
Lithium-ion batteries are widely used in various applications, including
electric vehicles and renewable energy storage. The prediction of the remaining
useful life (RUL) of batteries is crucial for ensuring reliable and efficient
operation, as well as reducing maintenance costs. However, determining the life
cycle of batteries in real-world scenarios is challenging, and existing methods
have limitations in predicting the number of cycles iteratively. In addition,
existing works often oversimplify the datasets, neglecting important features
of the batteries such as temperature, internal resistance, and material type.
To address these limitations, this paper proposes a two-stage remaining useful
life prediction scheme for Lithium-ion batteries using a spatio-temporal
multimodal attention network (ST-MAN). The proposed model is designed to
iteratively predict the number of cycles required for the battery to reach the
end of its useful life, based on available data. The proposed ST-MAN is to
capture the complex spatio-temporal dependencies in the battery data, including
the features that are often neglected in existing works. Experimental results
demonstrate that the proposed ST-MAN model outperforms existing CNN and
LSTM-based methods, achieving state-of-the-art performance in predicting the
remaining useful life of Li-ion batteries. The proposed method has the
potential to improve the reliability and efficiency of battery operations and
is applicable in various industries, including automotive and renewable energy
Enhanced Gaussian Process Dynamical Models with Knowledge Transfer for Long-term Battery Degradation Forecasting
Predicting the end-of-life or remaining useful life of batteries in electric
vehicles is a critical and challenging problem, predominantly approached in
recent years using machine learning to predict the evolution of the
state-of-health during repeated cycling. To improve the accuracy of predictive
estimates, especially early in the battery lifetime, a number of algorithms
have incorporated features that are available from data collected by battery
management systems. Unless multiple battery data sets are used for a direct
prediction of the end-of-life, which is useful for ball-park estimates, such an
approach is infeasible since the features are not known for future cycles. In
this paper, we develop a highly-accurate method that can overcome this
limitation, by using a modified Gaussian process dynamical model (GPDM). We
introduce a kernelised version of GPDM for a more expressive covariance
structure between both the observable and latent coordinates. We combine the
approach with transfer learning to track the future state-of-health up to
end-of-life. The method can incorporate features as different physical
observables, without requiring their values beyond the time up to which data is
available. Transfer learning is used to improve learning of the hyperparameters
using data from similar batteries. The accuracy and superiority of the approach
over modern benchmarks algorithms including a Gaussian process model and deep
convolutional and recurrent networks are demonstrated on three data sets,
particularly at the early stages of the battery lifetime
The development of machine learning-based remaining useful life prediction for lithium-ion batteries
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