Battery State Of Health Research Articles

Estimation of health status of lithium-ion battery based on PF-SA

The aging of lithium-ion batteries is a gradual process, which can usually be expressed by the state of health (SOH). Effective estimation of its SOH is helpful to reduce the probability of fire, fire and other dangerous accidents. In this paper, particle filter (PF) is used to predict the health of the battery, and then the simulated annealing algorithm (SA) is used to optimize the PF to realize the prediction of battery SOH.

리튬이온 배터리 노화상태 추정을 위한 건전성 지표 추출 및 CNN 적용

Due to the output characteristics of lithium-ion batteries used in electric vehicles (EVs) vary according to battery aging, accurate prediction of the state-of-health (SOH) reflecting the aging state is important. However, it is difficult to consider factors affecting battery characteristics, such as frequent charging and discharging, operating temperature, and state of charge, so there is a limitation in predicting battery SOH. Even in a model that considers the aging state of the battery under these various conditions, there is also a problem in that the complexity of the calculation process and parameters becomes serious. Therefore, in this paper, in order to research on the estimation of the aging state during operation of the INR18650-25R battery, a health indicator (HI) that can reflect the internal state of the battery according to aging is extracted. This research produce a learning image through the extracted HI and build a model study that enables algorithm learning through the image. The experimental profile used for model training and validation was an Urban Dynamometer Driving Schedule (UDDS), and a Convolutional Neural Network (CNN) with strength in image learning was applied for the estimation algorithm.

Efficient linear predictive model with short term features for lithium-ion batteries state of health estimation

The need to predict the State of Health (SoH) of lithium-ion batteries accurately and efficiently is rising with growing use of such batteries in safety critical applications. In this paper, an intuitive and efficient predictive algorithm that can estimate the SoH of lithium-ion batteries with accuracy on par with more complex and computationally demanding models is presented. The predictive algorithm uses the battery's temperature and voltage time discharge profile to predict its current SoH with root mean square error (RMSE) of 1%. It is shown that the crux in achieving good prediction accuracy lies in data preprocessing, which are cleansing, normalization and retaining of key features that are rich in information from the raw measured data. Finally, a simplified version of the algorithm using only voltage time profiles for model training is proposed that provides less than 12% RMSE error, comparable with current state of the art. This algorithm can be easily implemented in most applications since only measured voltage data is required. Throughout this paper, the proposed algorithm is tested with publicly available dataset and comparison is done with existing literature results to benchmark the proposed algorithm performance.

Lithium-Ion Battery State of Health Estimation Based on Improved Deep Extreme Learning Machine

Abstract Accurate estimation of the state of health (SOH) is an important guarantee for safe and reliable battery operation. In this paper, an online method based on indirect health features (IHFs) and sparrow search algorithm fused with deep extreme learning machine (SSA-DELM) of lithium-ion batteries is proposed to estimate SOH. First, the temperature and voltage curves in the battery discharge data are acquired, and the optimal intervals are obtained by ergodic method. Discharge temperature difference at equal time intervals (DTD-ETI) and discharge time interval with equal voltage difference (DTI-EVD) are extracted as IHF. Then, the input weights and hidden layer thresholds of the DELM algorithm are optimized using SSA, and the SSA-DELM model is applied to the estimation of battery's SOH. Finally, the established model is experimentally validated using the battery data, and the results show that the method has high prediction accuracy, strong algorithmic stability, and good adaptability.

A Model for the Estimation of the Residual Driving Range of Battery Electric Vehicles Including Battery Ageing, Thermal Effects and Auxiliaries

Sustainable mobility has recently become a priority of research for on-road vehicles. Shifting towards vehicle electrification is one of the most promising solutions concerning the reduction in pollutant emissions and greenhouse gases, especially for urban areas. Nevertheless, battery electric vehicles might carry substantial limitations compared with other technologies. Specifically, the electric range could be highly affected by the ageing process, non-optimal thermal management of the battery and cabin conditioning. In this paper, a model for the estimation of the residual range of electric vehicles is proposed accounting for the influence of battery state of health, battery pack temperature, power consumption of the main vehicle auxiliaries, and battery pre-heating on the residual driving range. The results of the model application to an L7 battery electric vehicle highlighted that the electric range can be highly affected by several factors related to real-world driving conditions and can consistently differ from nominal values.

Low-Temperature Flexible Micro Hydrogen Sensor Embedded in a Proton Battery for Real-Time Microscopic Diagnosis.

The proton battery is a very novel emerging research area with practicability. The proton battery has charging and discharging functions. It not only electrolyzes water: the electrolyzed protons can be stored but also released, which are combined with oxygen to generate electricity, and the hydrogen is not required; the hydrogen ions will be released from the battery. According to the latest document, the multiple important physical parameters (e.g., hydrogen, voltage, current, temperature, humidity, and flow) inside the proton battery are unlikely to be obtained accurately and the multiple important physical parameters mutually influence the data; they have critical effects on the performance, life, and health status of the proton battery. At present, the proton battery is measured only from the outside to indirectly diagnose the health status of battery; the actual situation inside the proton battery cannot be obtained instantly and accurately. This study uses micro-electro-mechanical systems (MEMS) technology to develop a low-temperature micro hydrogen sensor, which is used for monitoring the internal condition of the proton battery and judging whether or not there is hydrogen leakage, so as to enhance the safety.

Combining empirical mode decomposition and deep recurrent neural networks for predictive maintenance of lithium-ion battery

Predictive maintenance of lithium-ion batteries has been one of the popular research subjects in recent years. Lithium-ion batteries can be used as the energy supply for industrial equipment, such as automated guided vehicles and battery electric vehicles. Predictive maintenance plays an important role in the application of smart manufacturing. This mechanism can provide different levels of pre-diagnosis for machines or components. Remaining useful life (RUL) prediction is crucial for the implementation of predictive maintenance strategies. RUL refers to the estimated useful life remaining before the machine cannot operate after a certain period of operation. This study develops a hybrid data science model based on empirical mode decomposition (EMD), grey relational analysis (GRA), and deep recurrent neural networks (RNN) for the RUL prediction of lithium-ion batteries. The EMD and GRA methods are first adopted to extract the characteristics of time series data. Then, various deep RNNs, including vanilla RNN, gated recurrent unit, long short-term memory network (LSTM), and bidirectional LSTM, are established to forecast state of health (SOH) and the RUL of lithium-ion batteries. Bayesian optimization is also used to find the best hyperparameters of deep RNNs. Experimental results with the lithium-ion batteries data of NASA Ames Prognostics Data Repository show that the proposed hybrid data science model can accurately predict the SOH and RUL of lithium-ion batteries. The LSTM network has the optimal results. The proposed hybrid data science model with multiple artificial intelligence-based technologies also demonstrates critical digital-technology enablers for digital transformation of smart manufacturing and transportation.

State-of-health estimation for lithium-ion batteries by combining model-based incremental capacity analysis with support vector regression

Accurate state-of-health (SOH) estimation for lithium-ion batteries is of great significance for future intelligent battery management systems. This study proposes a novel method combining voltage-capacity (VC)-model-based incremental capacity analysis (ICA) with support vector regression (SVR) for battery SOH estimation. For accurate and efficient capture of IC curves, 18 VC models are first compared, and then, suitable models are selected for two types of batteries with different chemistries, enabling multitype health features to be obtained by parameterizing the VC models. After correlation analysis of these extracted health features with the reference battery capacity, the SVR algorithm is adopted to construct SOH estimation models. Finally, four aging datasets are employed for validation of the proposed method. The experimental results show that the SVR models achieve high accuracy in SOH estimation, i.e., the respective mean absolute errors (MAEs) and root mean square errors (RMSEs) of all batteries are limited to within 1.1%. Moreover, the method is robust against different initial aging statuses and cycle conditions of the batteries: after migration and fine-tuning, both the MAEs and RMSEs can be confined to within 2.3% by utilizing the established SVR models.

State of health prediction based on multi-kernel relevance vector machine and whale optimization algorithm for lithium-ion battery

The state of health (SOH) reflects the health status of the lithium-ion battery and is expected to accurately predicted, so as the corresponding maintenance measures can be taken to ensure the safe operation of the battery. This paper proposed a SOH prediction method based on multi-kernel relevance vector machine (RVM) and whale optimization algorithm (WOA). Firstly, the original features were obtained from the battery voltage and temperature data in charging and discharging phases. Secondly, the minimal-redundancy-maximal-relevance (mRMR) algorithm was introduced to select the optimal feature set. Then, the online model and offline model based on multi-kernel RVM and WOA were constructed. Finally, a hybrid model which combines the online model and offline model was proposed to prediction the SOH of the lithium-ion battery. The performance of the proposed method was evaluated with two kinds of data sets. The experimental results showed that the proposed method obtained higher prediction accuracy in both long-term and short-term periods than other methods.

Li-ion battery state of health Prediction based on Long Short-Term Memory Recurrent Neural Network

The state of health (SOH) prediction of lithium-ion battery is essential for the health management of batteries. At present, the prediction method combined with the extraction of health indicators in charge-discharge process has received extensive attention, however, many studies ignored that the extraction of battery discharge data will be affected by the actual operating conditions, which will affect the effectiveness of health indicators extraction. In this work, a type of recurrent neural network (RNN), which is long short-term memory-RNN(LSTM-RNN), is proposed to prediction the SOH of Li-ion batteries through the data of charging process and capacity. Because the different choice of network parameters will also affect the performance of the model, particle swarm optimization (PSO) algorithm is used to optimize LSTM model. The test results show that this method can effectively predict SOH of battery, and the maximum RMSE is less than 0.01. Compared with the traditional LSTM algorithm, it has higher accuracy.

A novel long short-term memory network for lithium-ion battery health diagnosis using charging curve

Lithium-ion batteries are widely used as the power source in electric vehicles. The state of health (SOH) diagnosis is very important for the safety and storage capacity of lithium-ion batteries. In order to accurately and robustly estimate lithium-ion battery SOH, a novel long short-term memory network (LSTM) based on the charging curve is proposed for SOH estimation in this work. Firstly, aging features that reflect the battery degradation phenomenon are extracted from the charging curves. Then, considering capture the long-term tendency of battery degradation, some improvements are made in the proposed LSTM model. The connection between the input gate and the output gate is added to better control output information of the memory cell. Meanwhile, the forget gate and input gate are coupled into a single update gate for selectively forgetting before the accumulation of information. To achieve more reliability and robustness of the SOH estimation method, the improved LSTM network is adaptively trained online by using a particle filter. Furthermore, to verify the effectiveness of the proposed method, we compare the proposed method with two data-driven methods on the public battery data set and the commercial battery data set. Experimental results demonstrate the proposed method can obtain the highest SOH accuracy.

A Fusion Method Based on EEMD, Pearson Correlation Analysis, Improved LSTM, and Gaussian Function-Trust Region Algorithm for State of Health Prediction of Lithium-Ion Batteries

Abstract Aiming at the phenomenon of battery capacity regeneration, which leads to inaccurate prediction of lithium-ion battery state of health (SOH), a new fusion method based on ensemble empirical mode decomposition (EEMD), Pearson correlation analysis (PCA), and improved long short-term memory (LSTM) network and Gaussian function-trust region (GS-TR) algorithm is introduced to predict battery SOH. First, the EEMD method is adopted to process the battery SOH data to eliminate the impact of capacity recovery. Second, the decomposed data signals are classified by the PCA method, and the signals classified as high frequency and low frequency are respectively predicted by the improved LSTM algorithm and the GS-TR algorithm. Finally, the prediction results of the improved LSTM and GS-TR algorithms are integrated. The proposed fusion method avoids the complexity of the hybrid neural network model and improves the prediction efficiency. Based on the average results of the three data sets from NASA, the RMSE result of the proposed algorithm is reduced by 9.56% compared with the improved LSTM with the EEMD algorithm and 37.57% compared with the improved LSTM without the EEMD algorithm. The results show that the proposed method has higher adaptability and prediction accuracy.

Rapid prediction of the state of health of retired power batteries based on electrochemical impedance spectroscopy

In order to solve the problems of long time-consuming, low accuracy and high energy consumption in detecting the state of health (SOH) of retired power batteries, a rapid prediction method of SOH based on electrochemical impedance spectroscopy (EIS) is proposed. This method realizes the rapid estimation of the SOH of retired power batteries through the mathematical model established by the constant phase element parameters in the constructed electrochemical impedance spectroscopy equivalent circuit model and the battery SOC and SOH. The verification experiment results show that using this method can greatly reduce the test time, save energy, and achieve rapid estimation of the unknown state of charge and health of the battery, and the prediction error is less than 2%.

A Review of Lithium-Ion Battery State of Health Estimation and Prediction Methods

Lithium-ion power batteries have been widely used in transportation due to their advantages of long life, high specific power, and energy. However, the safety problems caused by the inaccurate estimation and prediction of battery health state have attracted wide attention in academic circles. In this paper, the degradation mechanism and main definitions of state of health (SOH) were described by summarizing domestic and foreign literatures. The estimation and prediction methods of lithium-ion power battery SOH were discussed from three aspects: model-based methods, data-driven methods, and fusion technology methods. This review summarizes the advantages and disadvantages of the current mainstream SOH estimation and prediction methods. This paper believes that more innovative feature parameter extraction methods, multi-algorithm coupling, combined with cloud platform and other technologies will be the development trend of SOH estimation and prediction in the future, which provides a reference for health state estimation and prediction of lithium-ion power battery.

On-Line Estimation Method of Lithium-Ion Battery Health Status Based on PSO-SVM

Battery management system (BMS) refers to a critical electronic control unit in the power battery system of electric vehicles. It is capable of detecting and estimating battery status online, especially estimating state of charge (SOC) and state of health (SOH) accurately. Safe driving and battery life optimization are of high significance. As indicated from recent literature reports, most relevant studies on battery health estimation are offline estimation, and several problems emerged (e.g., long time-consuming, considerable calculation and unable to estimate online). Given this, the present study proposes an online estimation method of lithium-ion health based on particle swarm support vector machine algorithm. By exploiting the data of National Aeronautics and Space Administration (NASA) battery samples, this study explores the changing law of battery state of charge under different battery health. In addition, particle swarm algorithm is adopted to optimize the kernel function of the support vector machine for the joint estimation of battery SOC and SOH. As indicated from the tests (e.g., Dynamic Stress Test), it exhibits good adaptability and feasibility. This study also provides a certain reference for the application of BMS system in electric vehicle battery online detection and state estimation.

A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery

Lithium-ion batteries are used in a wide range of applications including energy storage systems, electric transportations, and portable electronic devices. Accurately obtaining the batteries’ state of health (SOH) is critical to prolong the service life of the battery and ensure the safe and reliable operation of the system. Machine learning (ML) technology has attracted increasing attention due to its competitiveness in studying the behavior of complex nonlinear systems. With the development of big data and cloud computing, ML technology has a big potential in battery SOH estimation. In this paper, the five most studied types of ML algorithms for battery SOH estimation are systematically reviewed. The basic principle of each algorithm is rigorously derived followed by flow charts with a unified form, and the advantages and applicability of different methods are compared from a theoretical perspective. Then, the ML-based SOH estimation methods are comprehensively compared from following three aspects: the estimation performance of various algorithms under five performance metrics, the publication trend obtained by counting the publications in the past ten years, and the training modes considering the feature extraction and selection methods. According to the comparison results, it can be concluded that amongst these methods, support vector machine and artificial neural network algorithms are still research hotspots. Deep learning has great potential in estimating battery SOH under complex aging conditions especially when big data is available. Moreover, the ensemble learning method provides an emerging alternative trading-off between data size and accuracy. Finally, the outlooks of the research on future ML-based battery SOH estimation methods are closed, hoping to provide some inspiration when applying ML methods to battery SOH estimation.

Comparing seven methods for state-of-health time series prediction for the lithium-ion battery packs of forklifts

A key aspect for the forklifts is the state-of-health (SoH) assessment to ensure the safety and the reliability of uninterrupted power source. Forecasting the battery SoH well is imperative to enable preventive maintenance and hence to reduce the costs. This paper demonstrates the capabilities of gradient boosting regression for predicting the SoH timeseries under circumstances when there is little prior information available about the batteries. We compared the gradient boosting method with light gradient boosting, extra trees, extreme gradient boosting, random forests, long short-term memory networks and with combined convolutional neural network and long short-term memory networks methods. We used multiple predictors and lagged target signal decomposition results as additional predictors and compared the yielded prediction results with different sets of predictors for each method. For this work, we are in possession of a unique data set of 45 lithium-ion battery packs with large variation in the data. The best model that we derived was validated by a novel walk-forward algorithm that also calculates point-wise confidence intervals for the predictions; we yielded reasonable predictions and confidence intervals for the predictions. Furthermore, we verified this model against five other lithium-ion battery packs; the best model generalised to greater extent to this set of battery packs. The results about the final model suggest that we were able to enhance the results in respect to previously developed models. Moreover, we further validated the model for extracting cycle counts presented in our previous work with data from new forklifts; their battery packs completed around 3000 cycles in a 10-year service period, which corresponds to the cycle life for commercial Nickel–Cobalt–Manganese (NMC) cells.

Battery State of Health and Charge Estimation Using Machine Learning Methods

In this study, state of health (SOH) and state of charge (SOC) estimation of series connected batteries were evaluated for their charge and discharge durations. For this purpose, an ARM-based electronics card module was developed for observing instantaneous batteries voltage, current and temperature values during the charge and discharge process. The implemented microcontroller based card module gathers data from the current, voltage, and temperature sensors and it transfers to the computer environment via serial communication port. A specific human machine interface is designed via app-designer. The obtained variables were used for estimating regression models of the machine-learning toolbox. Random forest, decision tree, polynomial, extreme gradient boosting, linear and gradient boosting regression models were used for instantaneous SOH and SOC estimation for batteries during the charge-discharge period. Root Mean Square Error (RMSE) and R^2score results were used for performance evaluation of regression models. When the RMSE and R^2 score results were compared, the decision tree regression model was the regression model that made the most accurate SOH and SOC estimation and the results were presented.

Small sample state of health estimation based on weighted Gaussian process regression

-Battery state of health (SOH) estimation is essential for the safety and reliability of electric vehicles. Data-driven approaches are compelling in SOH estimation as they work effectively without human intervention and have excellent nonlinear approximation capabilities. Most studies assume that the training data is sufficient. However, in practical applications, data acquisition is often expensive and time-consuming. A novel weighted Gaussian process regression SOH estimation method is proposed to reduce the model's dependence on data through knowledge transfer. The squared exponential covariance function is introduced with a penalty mechanism to control the cross-battery knowledge transfer process. Experiments are carried out with battery cyclic aging data under different working conditions. Experimental results show that the proposed weighted Gaussian process SOH estimation model can obtain reliable prediction results, although the training data only accounts for 20% of the total dataset.11Corresponding author: Hanmin Sheng email: shenghanmin@hotmail.comThis paper is sponsored by National Key R&D Program of China (No. 2018YFC1505203), National Natural Science Foundation of China (No. 61903066), China Postdoctoral Science Foundation (No. 2021M690560, No. 2018M640905), and Sichuan Province Science and Technology Program (No. 2021YFH0042)

State of Health Prediction of Lithium-Ion Batteries Based on the Discharge Voltage and Temperature

Accurate state of health (SOH) prediction of lithium-ion batteries is essential for battery health management. In this paper, a novel method of predicting the SOH of lithium-ion batteries based on the voltage and temperature in the discharging process is proposed to achieve the accurate prediction. Both the equal voltage discharge time and the temperature change during the discharge process are regarded as health indicators (HIs), and then, the Pearson and Spearman relational analysis methods are applied to evaluate the relevance between HIs and SOH. On this basis, we modify the relevance vector machine (RVM) to a multiple kernel relevance vector machine (MKRVM) by combining Gaussian with sigmoid function to improve the accuracy of SOH prediction. The particle swarm optimization (PSO) is used to find the optimal weight and kernel function parameters of MKRVM. The aging data from NASA Ames Prognostics Center of Excellence are used to verify the effectiveness and accuracy of the proposed method in numerical simulations, whose results show that the MKRVM method has higher SOH prediction accuracy of lithium-ion batteries than the relevant methods.