In this study, we developed a health indicator-capacity (HI-C) dual Gaussian process regression (GPR) model based on incremental capacity analysis (ICA) and optimized its kernel function to...
The strategy for predicting the RUL of lithium batteries in this study is based on Principal Component Analysis (PCA), the health Indicator (HI), and improved Gaussian process regression (IGPR).
The initial discharge internal resistance, polarization internal resistance, and polarization capacitance of the battery are taken as important indicators to map the SOH of the lithium battery, so as to predict the actual capacity of the battery according to its partial discharge characteristics.
Currently, model-based prediction and data-driven prediction are the two most commonly used methods for Li battery life prediction 4, 5. Model-based prediction often requires the construction of mathematical or empirical models based on the analysis of the relevant physicochemical reactions within the battery 6.
The capacity prediction error is corrected by the Bi-LSTM model. The reliability and superiority of the proposed method are verified by experiments. Accurate and reliable prediction of the remaining useful life (RUL) of lithium-ion batteries (LIB) is very important for the safety of power systems.
Jafari et al. developed a method for estimating the remaining useful life of lithium batteries using random forest and Light-GBM algorithms with optimized parameters for accurate prediction of RUL. Xu and colleagues investigated a method to diagnose the state of health and predict remaining useful life.
Accurate and reliable prediction of the remaining useful life (RUL) of lithium-ion batteries (LIB) is very important for the safety of power systems. To solve the nonlinear and time-varying problems of LIB aging trajectories, an RUL prediction method based on variational mode decomposition (VMD) and integrated machine learning is proposed.
In this study, we developed a health indicator-capacity (HI-C) dual Gaussian process regression (GPR) model based on incremental capacity analysis (ICA) and optimized its kernel function to...
In this study, we developed a health indicator-capacity (HI-C) dual Gaussian process regression (GPR) model based on incremental capacity analysis (ICA) and optimized …
To ensure long and reliable operation of lithium-ion battery storage workstations, accurate, fast, and stable lifetime prediction is crucial. However, due to the complex and interrelated ageing mechanisms of Li-ion batteries, using physical model-based methods for accurate description is challenging.
In this paper, a lithium-ion battery RUL prediction method based on convolutional neural network and deep bidirectional long short-term memory network is …
The accurate estimation of the State of Health (SOH) of lithium-ion batteries is essential for ensuring their safe and reliable operation, as direct measurement is not feasible. This paper presents a novel SOH estimation method that integrates Particle Swarm Optimization (PSO) with an Extreme Learning Machine (ELM) to improve prediction accuracy. Health …
Propose a hybrid data-driven method to predict battery degradation trends and local fluctuation characteristics. The capacity prediction error is corrected by the Bi-LSTM model. The reliability and superiority of the proposed method are verified by experiments.
To address this issue, we extract a novel health indicator (HI) from the battery current profiles that can be directly measured online. Furthermore, the indicator is optimized …
To ensure long and reliable operation of lithium-ion battery storage workstations, accurate, fast, and stable lifetime prediction is crucial. However, due to the complex and interrelated ageing mechanisms of Li-ion …
This paper proposes a multi-dimensional health indicator (HI) battery state of health (SOH) prediction method involving the analysis of the battery equivalent circuit model and constant current discharge characteristic curve. The values of polarization resistance, polarization capacitance, and initial discharge resistance are identified as the ...
Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is advantageous for maintaining the stability of electrical systems. In this paper, an interpretable online...
Many researchers at home and abroad have proposed various methods for predicting the remaining useful life of lithium-ion batteries. RUL prediction methods can be …
With the large-scale application of lithium-ion batteries in new energy vehicles and power energy storage, higher requirements are put forward for the SOH assessment and prediction technology. In engineering practice, …
Many researchers at home and abroad have proposed various methods for predicting the remaining useful life of lithium-ion batteries. RUL prediction methods can be categorized into three types: adaptive filtering, artificial intelligence, and nonlinear estimation models [15, 16, 17].
The rapid development of lithium-ion battery (LIB) technology promotes its wide application in electric vehicle (EV), aerospace, and mobile electronic equipment. During application, state of health (SOH) of LIB is crucial to enhance stable and reliable operation of the battery system. However, accurate estimation of SOH is a tough task, especially in its large …
Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is advantageous for maintaining the stability of electrical systems. In this paper, an interpretable …
Currently, the primary methods for estimating SOH can be categorized into three main approaches: direct measurement, model-based, and data-driven methods. The direct measurement approach assesses the current …
In this paper, a lithium-ion battery RUL prediction method based on convolutional neural network and deep bidirectional long short-term memory network is proposed, which solves the problem of low prediction accuracy of RUL in small samples and low long-term prediction accuracy in large samples. During the experiment, different data sets were ...
Battery state of power (SOP) estimation is an important parameter index for electric vehicles to improve battery utilization efficiency and maximize battery safety. Most of the current studies on the SOP estimation of lithium–ion batteries consider only a single constraint and rarely pay attention to the estimation of battery state on different time scales, which can …
Propose a hybrid data-driven method to predict battery degradation trends and local fluctuation characteristics. The capacity prediction error is corrected by the Bi-LSTM …
SOH and RUL are critical indicators for assessing battery aging, and a comprehensive evaluation of battery condition requires the validation of their joint prediction. For comparative analysis, two points with identical SOH but different RUL are selected from CS2-35 and CS2-36, as presented in Table 4. The remaining available capacity of the CS2-35 battery …
The results in 5.3.1 RUL prediction for multiple-formula Li-ion batteries, 5.3.2 RUL prediction with different amounts of available test data demonstrate that the multi-source transfer prediction method can reach high accuracy for multiple-formula Li-ion batteries regardless of how the formula changes. Because the proposed method merges the degradation information from …
1. Introduction. Lithium-ion batteries (LiBs) are extensively used in various applications, including new energy vehicles and battery energy storage systems, due to their excellent energy efficiency, high power density, and prolonged self-discharge life [].The state of health (SOH) of LiBs is influenced by complex electrochemical reactions, resulting in internal …
Therefore, in this article we propose a hybrid prediction method for the SOH of lithium batteries based on variational mode decomposition and LSTM with self-attention mechanism (SA-LSTM) model, which makes up for the shortcomings of the degradation characteristics of battery performance that cannot be fully covered by a single scale input, low prediction accuracy, and …
Currently, the primary methods for estimating SOH can be categorized into three main approaches: direct measurement, model-based, and data-driven methods. The direct measurement approach assesses the current health status of the battery through its capacity and internal resistance, yielding relatively accurate results [6].
The prediction precision dependent upon GPR relies on the specific parameters of the model. In this paper, a prediction strategy combing PCA for the RUL lithium batteries is suggested. First, the lithium battery charge and discharge tests were carried out, and the voltage cycle diagram was obtained. HI was extracted according to the ...
Accurate state of health (SOH) prediction is significant to guarantee operation safety and avoid latent failures of lithium-ion batteries. With the development of communication and artificial intelligence technologies, a body of researches have been performed toward precise and reliable SOH prediction method based on machine learning (ML) techniques.
To address this issue, we extract a novel health indicator (HI) from the battery current profiles that can be directly measured online. Furthermore, the indicator is optimized by Box-Cox transformation and evaluated by correlation analysis for …
This paper proposes a multi-dimensional health indicator (HI) battery state of health (SOH) prediction method involving the analysis of the battery equivalent circuit model and constant …
As the most mature portable power source, lithium-ion battery has become the mainstream of power source for electric vehicles (EVs) by virtue of its high energy density, long cycle life and relatively low cost. However, an excellent battery management system remained to be a problem for the operational states monitoring and safety guarantee for EVs. In this paper, …
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