Estimation Of Lithium-ion Batteries Research Articles

An error covariance correction-adaptive extended Kalman filter based on piecewise forgetting factor recursive least squares method for the state-of-charge estimation of lithium-ion batteries

Accurate state-of-charge (SOC) estimation is essential for fully utilizing the battery performance of electric vehicles. Considering the demand for algorithms with the advantages of simplicity, fewer calculations, good stability, and high accuracy in practical applications, this paper proposes a novel error covariance correction-adaptive extended Kalman filter (ECC-AEKF) for accurate and robust SOC estimation. In this paper, the maximum likelihood function of the probability density function of the error series (conditional on the priori covariance) is calculated by mathematical derivation to obtain a new priori error covariance, which is used to obtain a more appropriate Kalman gain. The ECC-AEKF can minimize the estimation error and reduces the effect of process noise characteristics and inappropriate error covariances on priori estimates. Meanwhile, a piecewise forgetting factor recursive least square (PFFRLS) is presented for model parameter identification. The PFFRLS using error feedback for real-time adaptively adjusts the forgetting degree of data based on the principle of integral separation. Furthermore, a comparative analysis of SOC estimation in PFFRLS-ECC-AEKF and commonly used methods is presented for validation of the performance of the proposed method under different temperatures and operating conditions. The results prove that the PFFRLS-ECC-AEKF achieves higher accuracy with less computation time than other methods.

Hybrid deep neural network with dimension attention for state-of-health estimation of Lithium-ion Batteries

Lithium-ion batteries (LIBs) are widely used and became the main energy storage medium for many devices. Accurate estimation of LIBs state-of-health (SOH) is crucial for safe and reliable operation of devices. This study designs an end-to-end multi-battery shared hybrid neural network (NN) prognostic framework that combines a convolutional neural network (CNN), a multi-layer variant long-short-term memory (VLSTM) NN and a dimensional attention mechanism (CNN-VLSTM-DA) to SOH estimation for LIBs. First, feature extraction and selection on the raw input data are performed by using a CNN. Second, a suitable VLSTM is designed. The network adds a “peephole connection” to the forget gate and output gate, respectively, which enhances the network’s ability to distinguish subtle features between input sequences. Besides, the forget gate and the input gate are coupled, so that, together, they determine the information that needs to be forgotten and the new data that needs to be added. Then, the output data of the CNN layer are fed into a multi-layer VLSTM NN to further capture the temporal correlation of these data. Finally, the attention mechanism is applied to the output of the VLSTM, to assign different weights to the features of each dimension and to give the prediction results. Several experiments are carried out on three datasets from NASA, CALCE and Oxford. These include full charge/discharge data, charge/discharge data in different SOC ranges, and non-fixed discharge current data. The results verify the effectiveness of the proposed method.

An improved proportional control forgetting factor recursive least square-Monte Carlo adaptive extended Kalman filtering algorithm for high-precision state-of-charge estimation of lithium-ion batteries

An improved proportional control forgetting factor recursive least square-Monte Carlo adaptive extended Kalman filtering algorithm for high-precision state-of-charge estimation of lithium-ion batteries

Digital Twin for Real-time Li-Ion Battery State of Health Estimation With Partially Discharged Cycling Data

To meet the fairly high safety and reliability requirements in practice, the state of health (SOH) estimation of Lithium-ion batteries (LIBs), which has a close relationship with the degradation performance, has been extensively studied with the widespread applications of various electronics. The conventional SOH estimation approaches with digital twin are end-of-cycle estimation that require the completion of a full charge/discharge cycle to observe the maximum available capacity. However, under dynamic operating conditions with partially discharged data, it is impossible to sense accurate real-time SOH estimation for LIBs. To bridge this research gap, we put forward a digital twin framework to gain the capability of sensing the battery's SOH on the fly, updating the physical battery model. The proposed digital twin solution consists of three core components to enable real-time SOH estimation without requiring a complete discharge. First, to handle the variable training cycling data, the energy discrepancy-aware cycling synchronization is proposed to align cycling data with guaranteeing the same data structure. Second, to explore the temporal importance of different training sampling times, a time-attention SOH estimation model is developed with data encoding to capture the degradation behavior over cycles, excluding adverse influences of unimportant samples. Finally, for online implementation, a similarity analysis-based data reconstruction has been put forward to provide real-time SOH estimation without requiring a full discharge cycle. Through a series of results conducted on a widely used benchmark, the proposed method yields the real-time SOH estimation with errors less than 1% for most sampling times in ongoing cycles.

A data-driven approach for estimating state-of-health of lithium-ion batteries considering internal resistance

State-of-health (SOH) estimation of lithium-ion batteries is an important issue in electric vehicle energy management. The complication of the internal electrochemical reaction mechanism and the uncertainty of the external operating conditions pose a significant challenge to SOH estimation. This paper develops a data-driven approach to estimate the SOH of lithium-ion batteries with consideration of the battery's internal resistance, which is used as a bridge to effectively integrate the equivalent circuit model (ECM) and the data-driven method. We try to identify the internal resistance under constant current charging conditions by simplifying the ECM. The poles and offsets are extracted from the differential thermal voltammetry, differential thermal capacity, and incremental capacity curves as thermoelectric coupling features. Then the internal resistance and thermoelectric coupling features are combined as model inputs. An explanation boosting machine (EBM) is used to construct the SOH estimator according to the good fitting performance and interpretability. The model parameters of EBM are optimized by using an ant colony algorithm to improve its robustness. Finally, comparative experiments between features and the model are carried out on the Oxford dataset. The results demonstrate that the mean absolute error of the proposed method is less than 1%.

Lithium-ion battery state-of-charge estimation strategy for industrial applications

The state-of-charge (SOC) estimation of lithium-ion batteries is very important for the safe and reliable operation of the battery pack. In the industry, the ampere-hour integration plus open-circuit voltage (OCV) strategy is normally used to estimate the SOC. However, the use of OCV is very demanding, requiring several hours. In this paper, a new estimation strategy is proposed, which can calibrate SOC during battery operation to eliminate the accumulated error of ampere-hour integration. Specifically, when the current of the battery is constant for a given period, or the change is small while charging or discharging, the SOC can be corrected according to its voltage. The detailed theoretical analysis is given in this paper and an experimental platform is built for testing.

Transfer-Learning-Based State-of-Health Estimation for Lithium-Ion Battery With Cycle Synchronization

Accurately estimating a battery's state of health (SOH) helps prevent battery-powered applications from failing unexpectedly. With the superiority of reducing the data requirement of model training for new batteries, transfer learning (TL) emerges as a promising machine learning approach that applies knowledge learned from a source battery, which has a large amount of data. However, the determination of whether the source battery model is reasonable and which part of information can be transferred for SOH estimation are rarely discussed, despite these being critical components of a successful TL. To address these challenges, this article proposes an interpretable TL-based SOH estimation method by exploiting the temporal dynamic to assist TL, which consists of three parts. First, with the help of dynamic time warping, the temporal data from the discharge time series are synchronized, yielding the warping path of the cycle-synchronized time series responsible for capacity degradation over cycles. Second, the canonical variates retrieved from the spatial path of the cycle-synchronized time series are used for distribution similarity analysis between the source and target batteries. Third, when the distribution similarity is within the predefined threshold, a comprehensive target SOH estimation model is constructed by transferring the common temporal dynamics from the source SOH estimation model and compensating the errors with a residual model from the target battery. Through a widely used open-source benchmark dataset, the estimation error of the proposed method evaluated by the root mean squared error is as low as 0.0034 resulting in a 77 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> accuracy improvement compared with existing methods.

Higher Order Sliding-Mode Observers for State-of-Charge and State-of-Health Estimation of Lithium-Ion Batteries

This paper proposes a new higher-order sliding mode-based approach to deal with the problem of online state of charge (SOC) and state of health (SOH) estimation for Lithium-ion batteries. The proposed approach is based on the conjunction of a Higher-Order Sliding Mode (HOSM) observer with two Generalized Super-Twisting (GST) observer-based identification algorithms. The HOSM observer provides an exact estimation of SOC, while the two identification algorithms guarantee the finite time estimation of the battery capacity and the battery inner resistance. Thanks to the relationship between the battery capacity and the SOH, an accurate estimation of this latter is obtained. The proposed approach has two main advantages: it ensures SOC estimation using only one observer and estimates the battery parameters without requiring any assumption on the system states. Experimental results using the Worldwide Harmonized Light Vehicles Test procedures (WLTP) are used to illustrate the high efficiency of the proposed approach.

Distributed state-of-charge estimation for lithium-ion batteries with random sensor failure under dynamic event-triggering protocol

The state of charge (SOC) performs as an indicator of the remaining capacity of the Lithium-ion batteries (LBs). An accurate SOC estimation of LB is of great significance for its operation optimization and life extension of the battery. In this article, the issue of distributed SOC estimation is addressed for LBs. To design the distributed filter for SOC estimation, the equivalent circuit model comprised of the resistor–capacitor networks, Warburg element, ohmic resistance, battery current and voltage is established. In order to reflect the properties of the random sensor failure (RSF) well, a set of Bernoulli-distributed sequences with known probabilities is introduced. The communication resources over the wireless networks are usually limited, for the purpose of saving communication resources, the dynamic event-triggering mechanism (DETM) is adopted to regulate transmission of the signals. The main objective of this article is to design a distributed SOC estimation approach for LBs subject to RSF under DETM over the sensor networks. The upper bound of the estimation error covariance is first ensured and then such upper bound is minimized by parameterizing the estimator gain. In addition, by virtue of the matrix simplification technique, the issue of sensor network topology’s sparseness is effectively tackled. At last, experimental examples are employed to validate the feasibility of the developed distributed SOC estimation algorithm.

Robust lithium-ion state-of-charge and battery parameters joint estimation based on an enhanced adaptive unscented Kalman filter

Accurate modeling and state of charge (SOC) estimation of lithium-ion battery against the model uncertainty and data uncertainty are difficult tasks nowadays. In this paper, a model and data uncertainties-robust method is proposed simultaneous estimation of the model parameters and the SOC using an enhanced adaptive unscented Kalman filter (AUKF). An extended state observer is established to integrate all unknown variables including parameters and SOC into a vector. An covariance matching technique with adaptive forgetting factor is proposed to obtain uncertain model and data statistics, in combination with a singular value decomposition based unscented transform to guarantee the positive definiteness of the error covariance matrix. Furthermore, establishing new protocols to handle missing input and missing output separately, the battery SOC and parameters can be estimated from missing measurements. Benefits from above procedures, the proposed method is more robust to model uncertainties and the data uncertainties compared to the conventional SOC estimation method. The robustness of the proposed method is verified at different operation temperatures and dynamic load profiles. The results shows that the proposed method possesses high accuracy and excellent robustness.

Deep domain adaptation network for transfer learning of state of charge estimation among batteries

Machine learning-based methods have been extensively explored for state of charge (SoC) estimation of lithium-ion batteries. However, for transfer learning among different batteries, most methods focus on the fine tuning of network weights while the domain feature adaptation from source set to target set is not explicitly explored. Here, a Deep Domain Adaptation Network (DDAN) is proposed for SoC estimation with transfer purpose among different batteries. The proposed method incorporates domain adversarial mechanism and maximum mean discrepancy (MMD) to get domain invariant features between source and target datasets. It can be used for both supervised and unsupervised learning depending on the availability of target labels. Experimental results on different batteries show the effectiveness of the proposed method with reduced error to over half compared with the direct prediction baseline, which can be attributed to the intrinsic domain adaptation mechanism between source and target batteries. Our proposed method outperforms the state of the art methods and can be generalized to other batteries.

Estimation of state of charge integrating spatial and temporal characteristics with transfer learning optimization

State of charge (SOC) estimation of lithium-ion batteries is of vital significance for the control strategy in battery management systems. To integrate the spatial and temporal characteristics of the data and to accomplish the transfer of knowledge, a novel convolutional neural network-bidirectional long short-term memory network based on transfer learning optimization (CNN-BiLSTM-TF) is proposed to estimate the SOC. Specifically, the spatial and temporal features hidden in the data are learned through CNN and BiLSTM, respectively. Furthermore, the CNN-BiLSTM network is established under one working condition and transferred to other working conditions through transfer learning, from which the SOC can be estimated online. A lithium-ion battery data set is applied to verify the proposed SOC estimation approach. The results of a case study demonstrate that the estimation performance of CNN-BiLSTM-TF is better than some existing approaches.

HFCM-LSTM: A novel hybrid framework for state-of-health estimation of lithium-ion battery

Accurate estimating the state of health (SOH) of lithium-ion battery plays a significant role in the safe operation of electric vehicles. With the development of deep learning, neural network-based methods have attracted much attention from researchers. While most of the existing SOH estimation methods are built by a single network, failing to sufficiently extract data information, and thus leading to the limited accuracy and generality (i.e., such a single network makes it difficult to estimate the SOH of battery, with different types and working conditions). Towards this issue, a novel hybrid network, called HFCM (Hierarchical Feature Coupled Module)-LSTM (Long–short-term memory), is designed to fully extract the original data information, making it more accurate to estimate the SOH of battery, with different types and working conditions. Specifically, the proposed HFCM-LSTM mainly consists of two components, HCFM and LSTM. The HCFM is proposed to comprehensively extract the original data feature information from the original samples. On the other hand, following the HFCM, a LSTM module is employed to model time series information. Based on this HFCM-LSTM network, the data obtained directly from the battery are fed into the model as input, enabling an end-to-end SOH estimation of the battery. A series of experiments are conducted on both NASA dataset and Oxford dataset, the experimental results demonstrate that the proposed SOH estimation algorithm outperforms several existing state-of-the-art methods, in terms of accuracy and generality.

Effect of training algorithms in accurate state of charge estimation of lithium ion batteries using NARX model

Effect of training algorithms in accurate state of charge estimation of lithium ion batteries using NARX model

Outlier-Robust Extended Kalman Filter for State-of-Charge Estimation of Lithium-Ion Batteries

Outlier-Robust Extended Kalman Filter for State-of-Charge Estimation of Lithium-Ion Batteries

A novel convolutional informer network for deterministic and probabilistic state-of-charge estimation of lithium-ion batteries

Accurate and real-time state-of-charge (SOC) estimation for lithium-ion batteries (LiB) is crucial for battery management system. However, the nonlinearity and complex dynamic properties of LiB pose a great challenge to the estimation of SOC. Some previous methods have undertaken high-precision SOC point estimation; however, the reliability of the estimated results has not been evaluated. These methods are too conservative and unreliable to describe the SOC sequence with random fluctuation characteristics by a certain number. In this study, a novel method for deterministic and probabilistic SOC estimation is proposed, called the Laplace distribution-based convolutional Informer network. The convolutional neural network (CNN) was used to extract spatial characteristics from the original input and enhance the ability of the model to capture sequential location information. An Informer network integrating the attention mechanism was used to learn the mapping relationship between these high-dimensional characteristics extracted by CNN and the SOC. This design makes the model suitable for fully extracting feature information from these data with complex temporal degradation properties. Considering the universality of measurement error and the importance of uncertainty estimation, the Laplace-based loss function was derived and used to train the proposed convolutional Informer network, reducing the influence of outliers on the estimated results and making the proposed model achieve uncertainty quantization. The effectiveness of the proposed model was evaluated on two public battery datasets. The results demonstrated that the proposed model produced accurate SOC point estimates and reliable interval estimates for different batteries under various operating conditions.

Electrochemical Impedance Spectroscopy Based State-of-Health Estimation for Lithium-Ion Battery Considering Temperature and State-of-Charge Effect

State of health (SOH) is critical to the efficient and reliable use of lithium-ion batteries (LIBs), especially in electric vehicle (EV) applications. Recently, electrochemical impedance spectroscopy (EIS)-based technique has been proven to be effective for SOH estimation of LIB. However, existing EIS-based methods failed to consider the impact of ambient temperature and battery state of charge (SOC), leading to the limited flexibility of these methods under dynamic environments. In this work, a novel EIS-based method is proposed for battery SOH estimation considering variations of temperature and SOC. An equivalent circuit model (ECM) is first introduced, in which the solid electrolyte interface (SEI) resistance and charge transfer resistance are employed to map their relationship with SOH under variant temperature and SOC. Subsequently, a probabilistic model, taking charge transfer resistance, temperature, and SOC as input variables, is developed for LIB SOH estimation. Experimental study indicates that the estimation error of the proposed method is around 4% when simultaneously considering the temperature and SOC effects. Moreover, the estimation error can reach 1.29% under certain conditions (e.g., 80% SOC at 30 °C). Both results of estimation error are better than the existing EIS-based methods, which indicates that the proposed method is more flexible for SOH estimation with higher precision.

A Study on State of Charge Estimation Method for Lithium Carbon Fluorides Primary Batteries

Recently, lots of work has been focused on the state of charge (SOC) estimation of lithium-ion batteries. However, such little research has been on the SOC estimation of primary batteries. The non-rechargeable trait of primary batteries induces accurate, reliable and non-destructive SOC estimation one of the critical tasks for their application, especially for long time storage. In this work, three kinds of lithium carbon fluorides (Li/CF x ) batteries (>3Ah) with different cathode material and design of batteries have been prepared, all of which can discharge at 1C. The energy density of the modified pouch cell with a capacity of 9Ah can reach 511 Wh kg−1 at 1C. Furthermore, four types of open circuit voltage (OCV) models (34 models) are employed to estimate the SOC of Li/CF x batteries for the first time. The OCV fitting error of polynomial models can be limited within ±0.05 V. And the corresponding SOC estimation results outperform other models, showing high accuracy. At last, the applicability of the proposed OCV based SOC estimation method are verified.

A joint state-of-health and state-of-energy estimation method for lithium-ion batteries through combining the forgetting factor recursive least squares and unscented Kalman filter

The state of energy (SOE) estimation of lithium-ion batteries is the basis of the mileage prediction of electric vehicles. However, its accurate estimation is affected by various factors such as the state of health (SOH) and temperature. In this study, a joint SOH-SOE estimation method combining the forgetting factor recursive least squares (FFRLS) and the unscented Kalman filter algorithm is proposed to improve the SOE estimation accuracy under complex and dynamic working conditions. The FFRLS algorithm is used to identify and update the battery model parameters and the open circuit voltage curves online, and the ordinary least squares algorithm is used to update battery capacity. The experiments show that the proposed algorithm can achieve satisfactory estimation accuracy under different battery aging degrees and temperatures. SOH and SOE estimation errors are within 4 % and 3 %, respectively. Even at a low temperature of −10 °C, the SOE estimation error is less than 4 %.

State-of-Charge Estimation of Lithium-Ion Batteries Using Convolutional Neural Network With Self-Attention Mechanism

Abstract State of charge (SOC) of lithium-ion batteries is an indispensable performance indicator in a battery management system (BMS), which is essential to ensure the safe operation of the battery and avoid potential hazards. However, SOC cannot be directly measured by sensors or tools. In order to accurately estimate the SOC, this paper proposes a convolutional neural network based on self-attention mechanism. First, the one-dimensional convolution is introduced to extract features from battery voltage, current, and temperature data. Then, the self-attention mechanism can reduce the dependence on external information and well capture the internal correlation of features extracted by the convolutional layer. Finally, the proposed method is validated on four dynamic driving conditions at five temperatures and compared with the other two deep learning methods. The experimental results show that the proposed method has good accuracy and robustness.