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Summary: With the rapid development of global electric vehicles, artificial intelligence, and aerospace, lithium‐ion batteries (LIBs) have become more and more widely used due to their high property. More and more disasters are caused by battery combustion. Among them, the temperature prediction of LIBs is the key to prevent the occurrence of fire. At present, using surface temperature sensor to measure the temperature of LIBs is the main method. High‐capacity LIB packs used in electric vehicles and grid‐tied stationary energy storage system essentially consist of thousands of individual LIB cells. Therefore, installing a physical sensor at each cell, especially at the cell core, is not practically feasible from the solution cost, space, and weight point of view. So developing a new method for battery temperature prediction has become an urgent problem to be solved. Electrochemical impedance spectroscopy (EIS) is a widely applied non‐destructive method of characterization of LIBs. In recent years, methods of predicting LIBs temperature by EIS have been developed. The prediction of LIBs temperature based on EIS has the advantages of high real‐time performance and prediction accuracy, and the device is simple and practical. The proposed method has a good development prospect in electric vehicles and other fields and can effectively solve the current problems of LIBs temperature prediction. Therefore, it is urgent to summarize these works to promote the next development. This review summarizes the main methods of using EIS to predict the temperature of LIBs in recent years, including the methods based on the impedance, phase shift, and intercept frequency. The principle and application of various methods are reviewed. The advantages and disadvantages of different methods and the future development direction are discussed. Highlights: Use EIS to quickly and effectively predict the internal temperature changes of LIBs.No hardware temperature sensors and thermal model are required.The methods to predict battery temperature based on impedance, phase shift, and intercept frequency are reviewed. [ABSTRACT FROM AUTHOR]

Summary: Implementing carbon neutrality and emission peak policies requires a high‐level electric vehicle field. Lithium‐ion batteries have been considered an essential component of electric vehicle power batteries. Effective state of charge (SOC) estimation for lithium‐ion batteries is a critical problem that needs to be addressed at present. With the feature extraction and fitting capability, the neural network can achieve accurate SOC estimation without considering the internal electrochemical state of the battery. This article overviews the definition of SOC and the relationship with battery aging state. Then, by examining recent literature on estimating the SOC of Lithium‐ion batteries using neural network methods, the methods are classified into three categories: feed‐forward neural network method, deep learning method, and hybrid method. The progress of neural network methods in SOC estimation applications is systematically reviewed, including principles, advantages, disadvantages, current status, and estimation errors. Possible recommendations for next‐generation intelligent battery management systems and SOC estimation are also presented. This review's highlighted insights will inspire researchers in the battery field and point the way to developing electric vehicles. [ABSTRACT FROM AUTHOR]

Polypropylene (PP) membrane with low porosity and poor hydrophilicity limits the performance of lithium-ion batteries. In mechanism, the function of amorphous structure of precursor film on microporous structure of PP membrane is yet unknown. In this work, the porous and hydrophilic PP/reduced hydroxyl-modified polypropylene (PPrOH) membrane was fabricated by blending esterified-PPOH firstly and then hydrolyzing to introduce the hydrogen bond in amorphous region and avoid phase separation. The results showed that the hydrogen bond was successfully introduced into the amorphous region of PP/PPrOH film and led to an increase of physical crosslinking points. Therefore, a large region of microphase separation emerged in the PP/PPrOH membrane after cold stretching, which resulted in a 45.6 % increase of porosity at maximum compared to the PP membrane. Besides, the hydrophilicity of PP/PPrOH membrane was improved with the blend content exceeding 10 wt%. Eventually, the lithium-ion conductivity of PP/PPrOH-10 membrane enhanced by 37.5 % over the base PP membrane. This work provides a novel perspective for preparing hydrophilic PP membrane with high porosity. [Display omitted] • Introducing hydrogen bond in amorphous of PP film by melt blending with PPrOH. • Hydrogen crosslinking in amorphous results in more region of microphase separation at stretching. • Addition of PPrOH increases porosity by 45.6 % and enhances lithium-ion conductivity by 37.5 % compared to PP membrane. • Crosslinking density in amorphous is positively correlated with porosity of membrane. [ABSTRACT FROM AUTHOR]

In this article, the estimation problem of the state of charge (SOC) of Lithium-ion batteries is investigated. In order to truly reflect the unreliability of the sensor measured data, the data missing phenomenon with respect to the sensor measurement (e.g., the terminal voltage) is taken into account for the addressed estimation issue. By introducing a stochastic variable obeying the Bernoulli distribution with a known probability, the random occurrence of the sensor measurement unavailability is well characterized. The second-order resistor–capacitor equivalent circuit model, where the model parameters are identified by the recursive least-squares method, is developed to govern the dynamical behaviors of a Lithium-ion battery. A data-unavailability-resistant nonlinear recursive filtering algorithm is proposed to estimate the real SOC in an unreliable industrial environment. An upper bound of the filtering error covariance is obtained, which is further minimized at each sampling instant. In addition, the filter gain is recursively parameterized by solving an optimization problem with respect to two coupled recursive Riccati-like equations, thereby being suitable for the online implementation. Finally, extensive experiments are conducted to demonstrate the validity of the proposed filtering approach. [ABSTRACT FROM AUTHOR]

With the increasing application of lithium-ion batteries, accurate estimation of the state of health (SOH) of lithium-ion batteries is of great significance for the safe operation of lithium-ion battery systems and the reduction of operation and maintenance costs. The complex physical and chemical reactions inside the lithium battery and the complex external working conditions make it challenging to achieve an accurate health-state estimation and life prediction. Therefore, the accurate estimation of the SOH of lithium-ion batteries is an important issue. At present, electrochemical impedance spectroscopy (EIS) is widely used in the study of battery-power impedance characteristics and battery-state estimation due to its advantage of nondestructive measurement. For this reason, this paper summarizes the research progress of lithium-ion SOH estimation based on EIS in recent years and details it layer by layer, mainly from two aspects: first, the quantitative relationship model between the characteristic parameters and SOH is established by constructing a frequency domain-equivalent circuit model. Secondly, we construct a quantitative relationship model between EIS data and SOH using the data-driven method. Finally, the advantages and disadvantages of different methods and estimation accuracy are analyzed and compared, and the future estimation of SOH based on EIS is prospected. [ABSTRACT FROM AUTHOR]

As an important energy storage device, lithium-ion batteries (LIBs) have been widely used in various fields due to their remarkable advantages. The high level of precision in estimating the battery's state of health greatly enhances the safety and dependability of the application process. In contrast to traditional model-based prediction methods that are complex and have limited accuracy, data-driven prediction methods, which are considered mainstream, rely on direct data analysis and offer higher accuracy. Therefore, this paper reviews how to use the latest data-driven algorithms to predict the SOH of LIBs, and proposes a general prediction process, including the acquisition of datasets for the charging and discharging process of LIBs, the processing of data and features, and the selection of algorithms. The advantages and limitations of various processing methods and cutting-edge data-driven algorithms are summarized and compared, and methods with potential applications are proposed. Effort was also made to point out their application methods and application scenarios, providing guidance for researchers in this area. [ABSTRACT FROM AUTHOR]

Highlights: What are the main findings? Rapid acquisition technology of electrochemical impedance spectroscopy. EIS was used to quickly and effectively estimate the SOH of LIBs. Machine learning method and equivalent circuit method to estimate battery health state. What is the implication of the main finding? EIS were found to be better SOH estimation method. The technology based on EIS has the characteristics of high precision advantages. The method based on EIS of LIBs SOH estimation has been applied to the BMS. Lithium-ion batteries stand out from other clean energy sources because of their high energy density and small size. With the increasing application scope and scale of lithium-ion batteries, real-time and accurate monitoring of its state of health plays an important role in ensuring the healthy and stable operation of an energy storage system. Due to the interaction of various aging reactions in the aging process of lithium-ion batteries, the capacity attenuation shows no regularity. However, the traditional monitoring method is mainly based on voltage and current, which cannot reflect the internal mechanism, so the accuracy is greatly reduced. Recently, with the development of electrochemical impedance spectroscopy, it has been possible to estimate the state of health quickly and accurately online. Electrochemical impedance spectroscopy can measure battery impedance in a wide frequency range, so it can reflect the internal aging state of lithium-ion batteries. In this paper, the latest impedance spectroscopy measurement technology and electrochemical impedance spectroscopy based on lithium-ion battery health state estimation technology are summarized, along with the advantages and disadvantages of the summary and prospects. This fills the gap in this aspect and is conducive to the further development of this technology. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries have been used in all aspects of life for environmental and resource reasons. The accurate estimation of the state of charge (SOC) ensures the proper operation of the battery. However, few methods have focused on the problem of SOC estimation at low temperatures. A hybrid method based on the CNN-BWGRU network is proposed in this paper. The method optimizes the influence of battery information on the results through a "multi-moment input" structure and the bidirectional network. The convolutional neural network (CNN) is used to learn the feature parameters in the input. The bidirectional weighted gated recurrent unit (BWGRU) can improve the fitting performance of the network at low temperatures by changing the weights. The proposed network has a strong generalization capability, estimation accuracy, and robustness. SOC estimation is performed under different conditions to verify the plausibility of the network. The experimental results show that the method has higher accuracy and stability than other networks. In addition, the proposed method can overcome the effect of different initial SOC on the estimation results. Therefore, the CNN-BWGRU network provides a new method in battery SOC estimation while providing helpful guidance for safe and stable battery operation in natural environments. [Display omitted] [ABSTRACT FROM AUTHOR]

Highlights: EIS was used to estimate the SOH of LIBs found to be fast and effective. It is more convenient to use CNN to extract features of EIS data automatically. The improved ECM method and IPSO-CNN-BiLSTM method are proposed in this paper. IPSO algorithm was first proposed to applied to the problem of optimizing the initial parameters of the neural networks. The state of health (SOH) is critical to the efficient and reliable use of lithium-ion batteries (LIBs). Recently, the SOH estimation method based on electrochemical impedance spectroscopy (EIS) has been proven effective. In response to different practical applications, two models for SOH estimation are proposed in this paper. Aiming at based on the equivalent circuit model (ECM) method, a variety of ECMs are proposed. Used EIS to predict the ECM, the results show that the improved method ensures the correctness of the ECM and improves the estimation results of SOH. Aiming at a data-driven algorithm, proposes a convolution neural network (CNN) to process EIS data which can not only extract the key points but also simplifies the complexity of manual feature extraction. The bidirectional long short-term memory (BiLSTM) model was used for serial regression prediction. Moreover, the improved Particle Swarm Optimization (IPSO) algorithm is proposed to optimize the model. Comparing the improved model (IPSO-CNN-BiLSTM) with the traditional PSO-CNN-BiLSTM, CNN-BiLSTM and LSTM models, the prediction results are improved by 13.6%, 93.75% and 94.8%, respectively. Besides that, the two proposed methods are 27% and 35% better than the existing gaussion process regression (GPR) model, which indicates that the proposed improved methods are more flexible for SOH estimation with higher precision. [ABSTRACT FROM AUTHOR]

With the increasing carbon emissions worldwide, lithium-ion batteries have become the main component of energy storage systems for clean energy due to their unique advantages. Accurate and reliable state-of-charge (SOC) estimation is a central factor in the widespread use of lithium-ion batteries. This review, therefore, examines the recent literature on estimating the SOC of lithium-ion batteries using the hybrid methods of neural networks combined with Kalman filtering (NN-KF), classifying the methods into Kalman filter-first and neural network-first methods. Then the hybrid methods are studied and discussed in terms of battery model, parameter identification, algorithm structure, implementation process, appropriate environment, advantages, disadvantages, and estimation errors. In addition, this review also gives corresponding recommendations for researchers in the battery field considering the existing problems. [ABSTRACT FROM AUTHOR]

Lithium battery state of health (SOH) is a key parameter to characterize the actual battery life. SOH cannot be directly measured. In order to further improve the accuracy of SOH estimation of lithium batteries, a model combining incremental capacity analysis (ICA) and bidirectional long- and short-term memory (Bi-LSTM) neural networks based on health characteristic parameters is proposed to predict the SOH of lithium-ion batteries. First, the health characteristic parameters are initially selected from the lithium battery charging curve, and the health characteristics are extracted by the Pearson correlation coefficient, including the charging time of constant current, charging time of constant voltage, voltage change rate from 300 s to 1000 s, 200s of voltage per cycle at a time. Second, ICA was used to deeply mine the deep associations related to SOH and the peaks of IC curves and their corresponding voltages were extracted as additional inputs to the model. Then, Bi-LSTM is used to form a combined SOH estimation model through adaptive weighting factors. Finally, the verification is based on the 5th battery parameters of the NASA lithium battery data set. The experimental results show that the proposed combined model reduces the mean square error by 55.17%, 49.28%, and 41.47%, respectively, compared with single models such as BP neural network (BPNN), LSTM, and gated recurrent neural network (GRU). [ABSTRACT FROM AUTHOR]

Lithium-ion batteries (LIBs) need to maintain high energy efficiency and power level in several application scenario. Accurate state of health (SOH) forecast is essential for designing a safe and reliable battery management systems (BMS). Temporal convolutional network (TCN) is a prevailing deep learning method for estimating the SOH of lithium-ion batteries. However, the hyperparameters in the network are usually difficult to predefine, which poses a challenge for the SOH estimation accuracy in real-world. To solve this problem, this paper proposes a data-driven estimation approach, where the TCN is combined with the modified flower pollination algorithm (MFPA) to determine the network topology. After hyperparameter optimization, the external sensor raw data and identified ohmic resistances trajectories in the equivalent circuits model (ECM) are both input to the TCN model to estimate SOH of LIBs. In contrast to prior approaches for feature extraction, this method is not only conductive to improve SOH estimation accuracy, but also can reduce on-board estimation computing burden. We carry out experiments on the same type of cells from NASA public data resources. The experimental results systematically validate the superiority of the proposed method, which covers high estimation accuracy, great robustness to varied training set and satisfied universality to different batteries. [Display omitted] • The MFPA algorithm is introduced to optimize several key hyper parameters in the TCN structure • Extracting external morphological features from the raw voltage and current curves • Ohmic resistances trajectories in the ECM with aging mechanisms to improve the SOH estimation accuracy • High estimation accuracy, great robustness to varied training set and satisfied universality to different battery types [ABSTRACT FROM AUTHOR]

Lithium-ion batteries have become the fastest-growing energy storage equipment available for extrinsic and intrinsic reasons. State of Charge (SOC) is one of the lithium-ion batteries' most critical performance indicators, reflecting the remaining capacity. An accurate and stable estimate of SOC is critical for any lithium-ion battery. This paper proposes a hybrid method to achieve stable and real-time battery SOC estimation at different temperatures, composed of an Improved Bidirectional Gated Recurrent Unit (IBGRU) network and Unscented Kalman filtering (UKF). The proposed method is experimentally validated using data from UDDS and US06 driving cycles. The verification results show that the method can adapt to various working conditions and obtain good estimation accuracy and robustness, with MAE and RMSE less than 0.83% and 1.12%, respectively. After transfer learning, the method can also be applied to new lithium-ion batteries and achieve good estimation performance at new temperature conditions. The maximum errors are 4.98% and 5.76% at 25 °C and −10 °C, respectively. Therefore, the IBGRU-UKF method can achieve a more accurate and stable SOC estimation with good expansion performance for different lithium-ion batteries. [Display omitted] • A new hybrid method is proposed to estimate the SOC of lithium-ion batteries. • The IBGRU is designed to improve estimation performance at different temperatures. • The UKF method is developed to smooth the IBGRU network estimation. • The proposed method can be applied to other batteries with transfer learning. • The method can hopefully estimate the battery pack SOC in practical application. [ABSTRACT FROM AUTHOR]