Estimation Of Lithium-ion Batteries Research Articles

A Hybrid Method for State-of-Charge Estimation for Lithium-Ion Batteries Using a Long Short-Term Memory Network Combined with Attention and a Kalman Filter

A battery management system (BMS) is an important link between on-board power battery and electric vehicles. The BMS is used to collect, process, and store important information during the operation of a battery pack in real time. Due to the wide application of lithium-ion batteries in electric vehicles, the correct estimation of the state of charge (SOC) of lithium-ion batteries (LIBS) is of great importance in the battery management system. The SOC is used to reflect the remaining capacity of the battery, which is directly related to the efficiency of the power output and management of energy. In this paper, a new long short-term memory network with attention mechanism combined with Kalman filter is proposed to estimate the SOC of the Li-ion battery in the BMS. Several different dynamic driving plans are used for training and testing under different temperatures and initial errors, and the results show that the method is highly reliable for estimating the SOC of the Li-ion battery. The average root mean square error (RMSE) reaches 0.01492 for the US06 condition, 0.01205 for the federal urban driving scheme (FUDS) condition, and 0.00806 for the dynamic stress test (DST) condition. It is demonstrated that the proposed method is more reliable and robust, in terms of SOC estimation accuracy, compared with the traditional long short-term memory (LSTM) neural network, LSTM combined with attention mechanism, or LSTM combined with the Kalman filtering method.

SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output

The state of charge (SOC) estimation of lithium-ion battery (LIB) based on recurrent neural network (RNN) has been a popular research due to its suitability for time series data prediction. However, there are significant output fluctuations in solo network, which lead to unstable SOC estimation performance. To solve this problem, this paper proposes a novel long short-term memory recurrent neural network (LSTM-RNN) with extended input (EI) and constrained output (CO) for battery SOC estimation, named EI-LSTM-CO. For the network input, an additional slow time-varying information sliding window average voltage is introduced to enhance the ability of network to map the nonlinear characteristics of the battery and reduce the output SOC fluctuations. In terms of the network output, a state flow strategy based on the Ampere-hour integration (AhI) is designed to constrain the variation between adjacent output SOCs of the network to smooth the network output and further improve the SOC estimation performance. In the experiments, the LiFePO4 battery datasets at various temperatures are used to validate the SOC estimation performance and generalization ability. In particular, the root mean square error (RMSE) and the maximum error (MAXE) of the proposed method on unknown data are less than 1.3% and 3.2% respectively.

Voltage sensor fault detection, isolation and estimation for lithium-ion battery used in electric vehicles via a simple and practical method

Accurate measurement information, especially precise voltage, is essential for model-based multi-state estimation algorithms of lithium-ion battery. Regarding the shortcomings in existing diagnosis methods, such as the difficulty in threshold value determination, low voltage sensor fault detection efficiency and the assumption of no multiple faults occur simultaneously, a simple and practical voltage sensor fault detection, isolation and estimation method is proposed in this paper. Firstly, the widely used algorithm by fusion of recursive least square and extend Kalman filter is adopted to obtain the measurement innovation (MI) between measured and estimated terminal voltage during online state-of-charge estimation process. Subsequently, focusing on the MI generated under faultless and faulty voltage sensors, a simple feature point (FP) identification-based voltage sensor detection and isolation method is further designed. Finally, with the identified FP's y-coordinate, a three-step based voltage sensor fault estimation method is developed to determine the fault mode and the corresponding fault value. Moreover, the proposed voltage sensor fault diagnosis method is verified by Federal Urban Driving Schedule test, whose results demonstrate that it can still realize immediate voltage sensor fault detection (at the moment of sensor fault occurrence) and accurate voltage sensor fault estimation even though there exist certain current sensor fault.

A Novel Square Root Adaptive Unscented Kalman Filter Combined with Variable Forgetting Factor Recursive Least Square Method for Accurate State-of-charge Estimation of Lithium-Ion Batteries

A Novel Square Root Adaptive Unscented Kalman Filter Combined with Variable Forgetting Factor Recursive Least Square Method for Accurate State-of-charge Estimation of Lithium-Ion Batteries

A Dual-Input Neural Network for Online State-of-Charge Estimation of the Lithium-Ion Battery throughout Its Lifetime.

Online state-of-charge (SOC) estimation for lithium-ion batteries is one of the most important tasks of the battery management system in ensuring its operation safety and reliability. Due to the advantages of learning the long-term dependencies in between the sequential data, recurrent neural networks (RNNs) have been developed and have shown their superiority over SOC estimation. However, only time-series measurements (e.g., voltage and current) are taken as inputs in these RNNs. Considering that the mapping relationship between the SOC and the time-series measurements evolves along with the battery degradation, there still remains a challenge for RNNs to estimate the SOC accurately throughout the battery’s lifetime. In this paper, a dual-input neural network combining gated recurring unit (GRU) layers and fully connected layers (acronymized as a DIGF network) is developed to overcome the above-mentioned challenge. Its most important characteristic is the adoption of the state of health (SOH) of the battery as the network input, in addition to time-series measurements. According to the experimental data from a batch of LiCoO2 batteries, it is validated that the proposed DIGF network is capable of providing more accurate SOC estimations throughout the battery’s lifetime compared to the existing RNN counterparts. Moreover, it also shows greater robustness against different initial SOCs, making it more applicable for online SOC estimations in practical situations. Based on these verification results, it is concluded that the proposed DIGF network is feasible for estimating the battery’s SOC accurately throughout the battery’s lifetime against varying initial SOCs.

State-of-Charge Estimation for Lithium-Ion Batteries Using Residual Convolutional Neural Networks.

State-of-charge (SOC) is a relative quantity that describes the ratio of the remaining capacity to the present maximum available capacity. Accurate SOC estimation is essential for a battery-management system. In addition to informing the user of the expected usage until the next recharge, it is crucial for improving the utilization efficiency and service life of the battery. This study focuses on applying deep-learning techniques, and specifically convolutional residual networks, to estimate the SOC of lithium-ion batteries. By stacking the values of multiple measurable variables taken at many time instants as the model inputs, the process information for the voltage or current generation, and their interrelations, can be effectively extracted using the proposed convolutional residual blocks, and can simultaneously be exploited to regress for accurate SOCs. The performance of the proposed network model was evaluated using the data obtained from a lithium-ion battery (Panasonic NCR18650PF) under nine different driving schedules at five ambient temperatures. The experimental results demonstrated an average mean absolute error of 1.260%, and an average root-mean-square error of 0.998%. The number of floating-point operations required to complete one SOC estimation was 2.24 × 106. These results indicate the efficacy and performance of the proposed approach.

A Novel Adaptive Back Propagation Neural Network-Unscented Kalman Filtering Algorithm for Accurate Lithium-Ion Battery State of Charge Estimation

Accurate State of Charge (SOC) estimation for lithium-ion batteries has great significance with respect to the correct decision-making and safety control. In this research, an improved second-order-polarization equivalent circuit (SO-PEC) modelling method is proposed. In the process of estimating the SOC, a joint estimation algorithm, the Adaptive Back Propagation Neural Network and Unscented Kalman Filtering algorithm (ABP-UKF), is proposed. It combines the advantages of the robust learning rate in the Back Propagation (BP) neural network and the linearization error reduction in the Unscented Kalman Filtering (UKF) algorithm. In the BP neural network part, the self-adjustment of the learning factor accompanies the whole estimation process, and the improvement of the self-adjustment algorithm corrects the shortcomings of the UKF algorithm. In the verification part, the model is validated using a segmented double-exponential fit. Using the Ampere-hour integration method as the reference value, the estimation results of the UKF algorithm and the Back Propagation Neural Network and Unscented Kalman Filtering (BP-UKF) algorithm are compared, and the estimation accuracy of the proposed method is improved by 1.29% under the Hybrid Pulse Power Characterization (HPPC) working conditions, 1.28% under the Beijing Bus Dynamic Stress Test (BBDST) working conditions, and 2.24% under the Dynamic Stress Test (DST) working conditions. The proposed ABP-UKF algorithm has good results in estimating the SOC of lithium-ion batteries and will play an important role in the high-precision energy management process.

A Hybrid Data-Driven Method for State-of-Charge Estimation of Lithium-Ion Batteries

As a portable energy storage system, lithium-ion batteries (LIBs) are widely used in wireless sensor networks, electric vehicles and other fields. To ensure the continuity of power supply, it is necessary to monitor the state of charge (SOC) of LIBs. However, due to the nonlinearity of battery operation, accurate SOC estimation has become a challenging task. In this paper, a SOC estimation method based on long-term short-term memory (LSTM) network and improved particle filter (IPF) is proposed, which maps the easily observed voltage, current and temperature to the target SOC. Firstly, through a layer of the LSTM network, the timing characteristics of the data are fully utilized to obtain the SOC variation trend of LIBs. Then, the noise variance adaptive algorithm and particle distribution optimization algorithm are introduced to improve the standard particle filter (PF). On this basis, the estimation results of the LSTM network are optimized by IPF. In addition, the performance of the proposed LSTM-IPF method is compared with other methods. The results show that the estimation performance of the proposed model is excellent, and the root mean squared error (RMSE) and maximum error (MAX) are controlled below 1% and 2% respectively, which meets the requirements of SOC estimation and verifies the effectiveness of the proposed method.

A novel deep neural network model for estimating the state of charge of lithium-ion battery

State of charge (SOC) estimation of lithium-ion battery is an important step in battery management system (BMS). As the internal chemical mechanism of battery is a complex process, there is a strong nonlinear relationship between the external measurable variable of battery and SOC. In order to accurately estimate the SOC, this paper proposes a novel deep neural network (DNN) model based on deep learning, which takes the data unit composed of voltage, current and temperature data of the battery sampled in 10 s as the input, and the SOC estimate as the output. The proposed model consists of convolution layer, ultra-lightweight subspace attention mechanism (ULSAM) layer, simple recurrent unit (SRU) layer and dense layer. The convolution layer can extract the features of the input and get the corresponding feature sequence. The ULSAM layer can highlight key information in the feature sequence. The SRU layer is used to process the feature sequence and transfer historical information. The dense layer is responsible for outputting SOC estimate. The proposed model is simulated by using the two public battery datasets, the test results show that the model has high estimation accuracy, and has good adaptability to battery degradation, ambient temperature and discharge conditions.

Study on the Capacity Degradation Mechanism and Capacity Predication of Lithium-Ion Battery Under Different Vibration Conditions in Six Degrees-of-Freedom

Abstract In order to study the degradation mechanism of lithium-ion batteries subjected to vibration aging in actual use and also to achieve capacity estimation and prediction, the following work has been done: First, the road spectra of two commonly seen domestic roads in China are collected in the field and modeled on a six degrees-of-freedom motion platform as the vibration working conditions of the batteries. Second, aging cycle experiments were conducted on batteries with different placement directions (X-axis direction, Y-axis direction, and Z-axis direction) under two vibration conditions, and the effects of experimental conditions on the decline results were analyzed; third, quantification of battery decline patterns to analyze the main causes of battery capacity decline; and then, through further analysis of the two vibration conditions on the lithium battery by in-situ and ex-situ methods as its internal mechanisms. Finally, the quantified results were input into the generative adversarial networks and long-term and short-term memory network prediction model to predict the capacity, and the errors of 20 predictions are as follows: the average values are 2.8561% for Group X, 2.7997% for Group Y, 3.0182% for Group Z, and 2.9478% for Group N, which meet the requirements of battery management system estimation. This paper provides a basis for the study of aging mechanism and capacity estimation of lithium-ion batteries under vibration aging conditions, which helps manufacturers to package batteries more rationally to extend battery life and develop battery management system (BMS)-related strategies.

State-of-charge estimation for lithium-ion battery during constant current charging process based on model parameters updated periodically

With online established battery model, model-based estimation method can track battery state-of-charge (SOC) precisely under dynamic conditions. Nevertheless, both recursive least square-based and filter-based methods cannot distinguish whether the voltage difference comes from SOC difference or internal resistance difference during constant current (CC) conditions, further leading to erroneously identified model parameters and inaccurate SOC estimation. To address this issue, a novel SOC estimation method during CC charging process by fusion of global optimization algorithm and Kalman filter family algorithm is developed in this paper. Firstly, some key parameters that are helpful for initialization and lower/upper bounds setting for global optimization method are extracted from electric vehicles’ driving process. Secondly, considering the shortcomings in traditional global optimization methods, including possible premature convergence, slow search speed in the late stage and relatively large computational cost, an improved particle swarm optimization is designed to periodically update model parameters during CC charging process. With obtained model parameters, SOC is further tracked via extended Kalman filter (EKF). The verification results based on experimental data demonstrates that the developed method can significantly weaken the strong cross-interference between model parameters and SOC, further achieving much more accurate SOC estimation than existing dual/joint EKF during CC charging process.

A Novel 2-RC Equivalent Model Based on the Self-Discharge Effect for Accurate State-Of-Charge Estimation Of Lithium-Ion Batteries

A Novel 2-RC Equivalent Model Based on the Self-Discharge Effect for Accurate State-Of-Charge Estimation Of Lithium-Ion Batteries

A Novel Square-Root Adaptive Unscented Kalman Filtering Method for Accurate State-of-Charge Estimation of Lithium-ion Batteries

A Novel Square-Root Adaptive Unscented Kalman Filtering Method for Accurate State-of-Charge Estimation of Lithium-ion Batteries

Lithium-Ion Battery Health Prediction on Hybrid Vehicles Using Machine Learning Approach

Efforts to decarbonize the world have shown a quick increase in electric vehicles (EVs), limiting increasing pollution. During this electric transportation revolution, lithium-ion batteries (LIBs) play a vital role in storing energy. To determine the range of an electric vehicle (EV), the state of charge and the state of health (SOH) of the battery pack is essential. Access to high-quality data on battery parameters is a crucial challenge for researchers working in the energy storage domain due primarily to confidentiality constraints on manufacturers of batteries and EVs. This paper proposes a hybrid framework for predicting the state of a lithium-ion battery for electric vehicles (EV). Electric vehicles are growing worldwide because of their environmental and sustainability advantages. Batteries are replacing fossil fuels in electric vehicles. In order to prevent failure, Li-ion batteries in electric vehicles should be operated and controlled in a controlled and progressive manner to ensure increased efficiency and safety. An extreme gradient boosting (XGBoost) algorithm is used in this paper to estimate the state of health (SOH) of lithium-ion batteries used in electric vehicles. The model is subjected to error analysis to optimize the battery’s performance parameter. The model undergoes an error analysis to optimize its performance parameters. Furthermore, a state of health (SOH) estimation method based on the extreme gradient boosting algorithm with accuracy correction is proposed here to improve the accuracy of state of health (SOH) estimation for lithium-ion batteries. To describe the aging process of batteries, we extract several features such as average voltages, voltage differences, current differences, and temperature differences. The extreme gradient boosting (XGBoost) model for estimating the state of health (SOH) of lithium-ion batteries is based on the ensemble learning algorithm’s higher prediction accuracy and generalization ability. Experimental results suggest that the boundary gradient lifting algorithm model is capable of more accurate prediction.

A cubature Kalman filter for online state-of-charge estimation of lithium-ion battery using a gas-liquid dynamic model

A novel state of charge (SOC) estimation method is proposed based on a gas-liquid dynamic model using a Cubature Kalman filter (CKF) with state constraints to improve the accuracy of estimations. The constraints derived from the principle of aerodynamic are introduced, which does not need the temperature correction coefficient and can achieve rapid SOC convergence due to its unique iterative form. Non-linear Kalman filter used to eliminate the violent jitter of the original algorithm when switching working conditions are also incorporated. Then, the proposed method is tested using a cylindrical-format ternary lithium-ion battery with a nominal capacity of 2.6 Ah and the estimated SOC were compared with the experimental results. As a result, comparative studies of two non-linear filters revealed that the CKF has the most outstanding performance based on concerning estimation accuracy, recovery time from an initial offset, and computational time. That is 36.4 % more accurate and the convergence time is 97.5 % shorter than the Extended Kalman filter (EKF) when the initial offset is 60 %, respectively.

Adaptive robust unscented Kalman filter-based state-of-charge estimation for lithium-ion batteries with multi-parameter updating

State-of-charge (SOC) estimation is one of the key technologies for the development and application of battery management system (BMS). To achieve high accuracy in SOC estimation, which can adapt to any conditions, an adaptive robust unscented Kalman filter (ARUKF) based on multi-parameter update is proposed herein. First, the DP battery model is applied to replicate the dynamic behavior of lithium-ion batteries, and the model parameters are identified online by the improved forgetting factor recursive least square (IFFRLS). Then, the Institute of Geodesy and Geophysics (IGGIII) weight function is introduced into unscented Kalman filter (UKF) as the form of a robust factor to adjust the weights of observation residuals, and receding horizon based adaptive filter tuning is employed to obtain the time-varying noise covariance. Subsequently, joint estimation of battery model parameters and SOC with capacity updating is implemented which can suppress the system disturbance caused by outliers, mistuning, unknown initial value, and aging. Finally, the superior performance of accuracy and convergence is verified by cycle and aging tests. The maximum absolute error of SOC estimation under the proposed method is kept within 2%. The convergence speed of SOC estimation utilizing ARUKF is nearly 80 s faster than that of robust UKF (RUKF) and UKF under a 20% SOC initial error.

Differential Capacity as a Tool for SOC and SOH Estimation of Lithium Ion Batteries Using Charge/Discharge Curves, Cyclic Voltammetry, Impedance Spectroscopy, and Heat Events: A Tutorial

State-of-charge (SOC) and state-of-health (SOH) of different cell chemistries were investigated using long-time cycle tests. This practical guide illustrates how differential capacity dQ/dU (capacitance) obtained from discharge curves, impedance spectra, and cyclic voltammograms can be used for the instant diagnosis of lithium-ion batteries without fully charging and discharging the cell. The increase of dU/dQ is an early indicator of upcoming heat events and deep discharge. The criterion dQ/dU = dU/dQ = 1 could serve as an indicator for “full charge”. The frequency response of capacitance correlates with the available charge of the battery and reflects overcharge events and deep discharges long before the battery fails. It is not necessary to measure down to extremely low frequencies because the charge transfer pseudocapacitance of around 10 Hz reflects well the SOC. Computer-aided calculation methods for the evaluation of measurements in industrial environments and for the training of students are presented.

State-of-health estimation for lithium-ion batteries using differential thermal voltammetry and Gaussian process regression

State-of-health estimation for lithium-ion batteries using differential thermal voltammetry and Gaussian process regression

State of health estimation based on modified Gaussian process regression for lithium-ion batteries

State of health (SOH) estimation of lithium-ion batteries is a challenging and crucial task for consumer electronics, electric vehicles, and micro-rids. This study presents a data-driven battery SOH estimation method based on a novel integrated Gaussian process regression (GPR) model. First, the aging characteristics of batteries are analyzed from multiple perspectives, and three health indicators (HIs) are extracted from battery charging and discharging curves. Then, the Pearson correlation analysis method is used to quantitatively analyze the correlation between the selected HIs and SOH. Next, a novel compound kernel function is proposed for battery SOH estimation, and different pairs of mean function and kernel function chosen from four mean functions and sixteen kernel functions are used to construct GPR models, and their estimation accuracy is compared subsequently. Finally, four different batteries with various initial health conditions from the NASA battery dataset are used to verify the performance of the proposed method. Experiments show that the method proposed in this paper has satisfactory estimation results in terms of accuracy, generalization ability, and robustness. Specifically, its estimated mean-absolute-error (MAE) and root-mean-square-error (RMSE) is only 1.7%, and 2.41%, respectively.

State-of-Health Estimation for Lithium-Ion Batteries Using Domain Adversarial Transfer Learning

Lithium-ion batteries are the main energy source of devices, and the estimation of their state-of-health (SOH) has become a hot point in prognostics and health management. However, many existing methods assume that training and testing data follow the same distribution. The model based on dataset under one working condition may be ineffective for the dataset under another working condition due to the distribution discrepancy. Thus, this article proposes a novel battery health prognostic model based on transfer learning. First, a novel transfer learning-based prognostic model, called deep domain adversarial network, is developed for SOH estimation of Lithium-ion batteries. Second, an unsupervised feature alignment metric is proposed, where maximum mean discrepancy and correlation alignment are considered simultaneously. Moreover, a generative adversarial learning is developed to guide the feature generator to provide the domain-invariant features. Finally, a novel feature generator, called dense bidirectional gated recurrent unit, is proposed to extract discriminate features from sensor signals. The effectiveness of DDAN for SOH estimation is verified on a battery dataset. The experimental results indicate that DDAN can effectively predict SOH of Lithium-ion battery, and significantly improve the performance of feature learning and knowledge transferring.