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• An analytical parameters estimation results by the MLE method for the Wiener process with random effects is obtained. • The comparative study between different MLE methods is implemented. • An unbiased parameters estimation method is proposed and applied to other types of Wiener process-based degradation models. • An empirical unbiased parameters estimation results when the degradation data is measured at different times is developed. • Mis-specification issue about whether considering the random effects is analyzed in theory. Accurate parameters estimation is one of the most crucial components in remaining useful life (RUL) prediction. This paper proposes an unbiased parameters estimation method and analyzes the impact of model mis-specification of Wiener process-based degradation model with random effects. First, we obtain an analytical expression of parameters estimation for the linear Wiener process. Then, by analyzing the natures of parameters estimation, an unbiased parameters estimation method is proposed and applied to other types of Wiener process-based degradation models. This unbiased estimation method is very similar to the sample variance of normal distribution. Since the sample size of degradation data is typically small, hence the proposed unbiased estimation method is suggested. After that, we present an empirical unbiased parameters estimation method with a closed-form solution when the degradation data is measured at different times. The proposed unbiased estimation method could increase computational speed and avoid falling into local minimum. In addition, based on parameters estimation results, the mis-specification issue about whether the random effects should be considered in the degradation modeling is analyzed in theory. Finally, several numerical examples and case studies are used for experimental verification. The experimental results demonstrate that the proposed unbiased parameters estimation method is superior to other parameters estimation methods and the random effects are suggested to be considered. [ABSTRACT FROM AUTHOR]

Accurately predicting the health status of batteries through easily available data is crucial for the battery management system (BMS) in electric vehicles. This paper utilizes the battery aging information obtained from the partial charging process as the input to accurately predict the battery charging process based on the bi-directional long short-term memory (Bi-LSTM) network. Firstly, in order to obtain a precise incremental capacity (IC) curve containing battery aging information, a generalized derivative method based on the back propagation (BP) neural network for discrete data is proposed. Then, using this derivative method, the interrelated three-dimensional features, namely charged capacity (Q), voltage (V), and the d Q /d V values, are identified from the IC curve containing a complete peak with an interval length of 100 mV. Subsequently, a Bi-LSTM network is constructed to learn the correlation between the three-dimensional features and charging capacity, and predict the charging process within the interval of 3.8V–4.2V. Finally, the Oxford battery degradation dataset is used for verification. The results show that the battery aging information extracted during the partial charging process is closely related to battery capacity degradation, and the proposed capacity prediction method with fusing aging information can accurately predict the charging process of lithium-ion batteries. • A generalized derivative method based on BP neural network for discrete data. • Capacity prediction method of lithium-ion batteries with fusing aging information. • A Bi-LSTM network with the interrelated 3D features for capacity prediction. [ABSTRACT FROM AUTHOR]

Health assessment is one of the most crucial components in lithium-ion battery (LIB) management. However, traditional methods often require disassembling LIBs, which are inconvenient for implementation. On the contrary, non-destructive diagnostics can obtain the aging information of LIBs without causing damage to batteries, which is beneficial for the periodic inspection and secondary use of LIBs in other fields. Therefore, accurate non-destructive diagnostics is necessary in practical applications. This paper proposes a new voltage reconstruction model considering the polarization process to get the battery internal information. Through matching the equilibrium potentials of the positive and negative electrodes, combined with an equivalent circuit model, this voltage reconstruction model can accurately simulate the whole charging and discharging processes of LIBs. The parameters with clear physical interpretation in the model can be quickly and accurately identified within the MATLAB software, and these parameters can be used to quantitatively identify the degradation modes of LIBs. Finally, the post-mortem analysis of the aged LIB is used for verification. The results show that the proposed model could precisely reflect the battery internal ageing information and could be used for online diagnostics in real electric vehicles. • The quantitative analysis of LLI and LAM for lithium-ion batteries (LIBs) • A voltage reconstruction model considering the polarization process for LIBs • Non-destructive diagnostics for analysing the aging mechanism of LIBs [ABSTRACT FROM AUTHOR]

• A physical informed neural network is built for aging mechanism identification. • A hybrid prediction method combining the aging mechanism and data-driven method. • Entire charging curve prediction using developed deep hybrid neural network. • Fast capacity prediction only using 50 mV charging voltage window. Accurate and robust capacity prediction is significant for battery management system to identify the state of health and life condition for lithium-ion batteries. This paper proposes a fast capacity prediction method by developing a novel deep aging mechanism-informed bidirectional long-short term memory (AM-Bi-LSTM) neural network. Firstly, a physical informed aging mechanism (AM) layer is established with the random charging curve sequences as input to identify the degradation features. Then the deep learning framework with two bidirectional long-short term memory (Bi-LSTM) layers is built to reflect the entire constant current charging curves and predict the battery capacity. In which, the battery aging mechanism is integrated into the artificial intelligence algorithm of capacity prediction for the first time. Several case studies are implemented to verify the effectiveness of developed method, and the influence of voltage window length on capacity prediction is further discussed. The results demonstrate that the charging curves can be accurately and fast captured with a capacity prediction root mean square error of less than 0.49% for 0.74 Ah batteries with 50 mV voltage window charging points collected in only less than 2.09 minutes mean cost time in the whole life cycle. It shows the proposed aging mechanism-informed data-driven prediction method has stronger robustness, faster prediction speed and higher accuracy compared with other data-driven methods. [ABSTRACT FROM AUTHOR]

• Arrhenius based stochastic degradation rate model • A capacity conversion path from random temperature to reference temperature • A new battery aging model under time-varying temperature condition • A novel online RUL prediction method under Bayesian framework Time-varying temperature condition has a significant impact on discharge capacity and aging law of lithium-ion battery. Consequently, a novel remaining useful life (RUL) prediction method for lithium-ion battery under time-varying temperature condition is developed in this paper. Firstly, a stochastic degradation rate model based on Arrhenius temperature model is proposed, and an interesting battery capacity conversion path from random temperature condition to reference temperature condition is established. Secondly, the aging model of lithium-ion battery under time-varying temperature condition is developed based on Wiener process, and a two-step unbiased estimation method based on maximum likelihood estimation (MLE) combined with genetic algorithm (GA) is proposed. Next, the random parameter is online updated under Bayesian framework. Then the probability density function (PDF) of the RUL for lithium-ion battery under time-varying temperature condition is derived. Finally, a case study is implemented to verify the effectiveness, and the results show that the proposed prediction method has higher accuracy and smaller uncertainty. [ABSTRACT FROM AUTHOR]

In the backdrop of the carbon neutrality, lithium-ion batteries are being extensively employed in electric vehicles (EVs) and energy storage stations (ESSs). Extremely harsh conditions, such as vehicle to grid (V2G), peak-valley regulation and frequency regulation, seriously accelerate the life degradation. Consequently, developing long-life batteries for typical scenarios has emerged as a crucial focus for future endeavors. This paper concentrates on the fundamental scientific inquiries and comprehensively reviews aspects including the urgent requirements, degradation mechanisms, design methods, durability modeling and management approaches for long-life batteries. Firstly, it underscores the significance and urgency of developing long-life batteries under the typical EV and ESS scenarios. Subsequently, we delve into the long-life battery design methods concerning degradation mechanism suppression. Following this, the degradation modeling and advanced management strategies for achieving long-life batteries are elucidated. Lastly, facing the existing challenges and future opportunities, we provide new insights and perspectives to promote the development and practical application of long-life lithium-ion batteries. • A comprehensive review of the key issues towards long-life battery is presented. • The pressing long-life demands of EV and ESS typical application is highlighted. • A critical review of degradation mechanism and modeling is emphasized. • Challenges and solutions of long-life battery design and management are discussed. • Prospects of long-life battery are illustrated. [ABSTRACT FROM AUTHOR]

Remaining useful life (RUL) prediction has great importance in prognostics and health management (PHM). Relaxation effect refers to the capacity regeneration phenomenon of lithium-ion batteries during a long rest time, which can lead to a regenerated useful time (RUT). This paper mainly studies the influence of the relaxation effect on the degradation law of lithium-ion batteries, and proposes a novel RUL prediction method based on Wiener processes. This method can simplify the modeling complexity by using the RUT to model the recovery process. First, the life cycle of a lithium-ion battery is divided into the degradation processes that eliminate the relaxation effect and the recovery processes caused by relaxation effect. Next, the degradation model, after eliminating the relaxation effect, is established based on linear Wiener processes, and the model for RUT is established by using normal distribution. Then, the prior parameters estimation method based on maximum likelihood estimation and online updating method under the Bayesian framework are proposed. Finally, the experiments are carried out according to the degradation data of lithium-ion batteries published by NASA. The results show that the method proposed in this paper can effectively improve the accuracy of RUL prediction and has a strong engineering application value. [ABSTRACT FROM AUTHOR]