Battery State Of Charge Estimation Research Articles

Mechanics-based state of charge estimation for lithium-ion pouch battery using deep learning technique

Accurate state of charge (SOC) estimation helps achieve efficient battery management, which is essential for transportation electrification. Significantly different from existing data-driven estimation methods only considering battery electrical information, this study proposes a mechanics-based battery SOC estimation using deep learning techniques. First, an experimental setup for measuring pouch-type battery stress is designed, followed by constructing four typical operating conditions to establish a sophisticated battery dataset and investigate the primary relationship between battery stress and SOC. Then, a data-driven estimation model composed of long short-term memory neural network is investigated to achieve the interlink between battery external measurements and internal states, in which the battery voltage, current, and stress sequences within a sliding window length are fed into the deep learning model. Quantitative experimental results demonstrate that the proposed mechanics-based battery SOC estimation approach can achieve acceptable accuracy and is adaptable to different training and operating conditions, as well as ensure the estimation performance under limited data length. Moreover, the proposed method has also been proven robust to the interference of battery measurement noise compared to the traditional estimation method.

State of charge estimation method by using a simplified electrochemical model in deep learning framework for lithium-ion batteries

To ensure the secure and healthy usage of lithium-ion batteries, it is necessary to accurately estimate the state of charge (SOC) in battery management systems. The development of deep learning (DL) provides a new solution for battery SOC estimation. However, the directly measured physical quantities contain less useful information and have low estimation accuracy. This paper proposes a method of integrating the mechanism knowledge of the battery domain into the DL framework. Firstly, the simplified electrochemical model is utilized to obtain the mechanism-related physical variables to expand the input of the DL model. Secondly, the long short-term memory (LSTM) network is used with the Bayesian optimization, and the variables with high correlation are identified. The best SOC estimation performance can be obtained by adding all the selected highly-correlated variables to the input for training together. The results show that the proposed method can improve the SOC estimation performance with only a slight increase in computation cost. Finally, other DL models are utilized to further validate the effectiveness, to reveal the universality. These results show that the performance of the DL model can be effectively improved by using the knowledge of the battery domain.

Joint Estimation of State of Charge and State of Health of Lithium Ion Battery

Abstract Overcharge and discharge of power battery not only increase the battery loss but also lead to fire and other accidents under harsh environmental conditions. Accurate estimation of battery parameters and status is an important reference in the battery management system to prevent battery overcharge and discharge. In this article, the following studies are carried out by focusing on the time separation scale and estimating parameters and state values online based on the improved particle filter: (1) The unscented transform and multi-innovation were applied to the particle filter to optimize the particle distribution and update the status value from the historical information, and the multi-innovation unscented particle filter was formed to estimate the state of battery charge. (2) Considering the influence of parameter variation on the estimation of battery state of charge (SOC). Due to the slow change characteristics of parameters and fast change characteristics of states, the parameters and states are jointly estimated from macro and micro time scales, respectively. The capacity change estimated by the unscented particle filter is used to characterize the battery health state, and finally, the joint estimation of battery SOC and state of health (SOH) is formed; (3) Three different working conditions are used to verify the algorithm. The joint algorithm accurately estimates the real-time changes of SOC and SOH, and the average error of SOC is less than 0.5%, which confirms the high accuracy of the joint algorithm.

State of Charge Estimation for Batteries Based on Common Feature Extraction and Transfer Learning

The state of charge (SOC) of a battery is a key parameter of electrical vehicles (EVs). However, limited by the lack of computing resources, the SOC estimation strategy used in vehicle-mounted battery management systems (V-BMS) is usually simplified. With the development of the new energy vehicle big data platforms, it is possible to obtain the battery SOC through cloud-based BMS (C-BMS). In this paper, a battery SOC estimation method based on common feature extraction and transfer learning is proposed for C-BMS applications. Considering the diversity of driving cycles, a common feature extraction method combining empirical mode decomposition (EMD) and a compensation strategy for C-BMS is designed. The selected features are treated as the new inputs of the SOC estimation model to improve the generalization ability. Subsequently, a long short-term memory (LSTM) recurrent neural network is used to construct a basic model for battery SOC estimation. A parameter-based transfer learning method and an adaptive weighting strategy are used to obtain the C-BMS battery SOC estimation model. Finally, the SOC estimation method is validated on laboratory datasets and cloud platform datasets. The maximum root-mean-square error (RMSE) of battery SOC estimation with the laboratory dataset is 2.2%. The maximum RMSE of battery pack SOC estimation on two different electric vehicles is 1.3%.

A novel combined online method for SOC estimation of a Li-Ion battery with practical and industrial considerations

Estimation of the state of charge (SOC) in the battery management systems (BMS) is very crucial. However, the BMS consistently suffers from inaccuracy of SOC estimation and high computational burden in industrial applications. Herein, we propose a novel combined SOC estimation approach with high accuracy, simplicity in implementation and, robustness that can be implemented on the low-cost microcontrollers. The basis of the method is to online identification the open circuit voltage (OCV) value of the battery simultaneously with other unknown parameters of the battery equivalent circuit model (ECM). Using this identified OCV, SOC is extracted from the OCV-SOC curve, directly. This direct method is an open-loop estimation method and tends to get lower SOC accuracy compared with known methods. To overcome this disadvantage, a combined method is proposed. Hence, to estimate SOC in this method, in the middle part of the OCV-SOC curve (linear part) and after identifying OCV from recursive least square algorithm (RLS) method with a forgetting factor, SOC is estimated directly via OCV-SOC curve. Furthermore, in the other parts of the curve especially non-linear parts, SOC is estimated with the EKF algorithm. In summary, not only estimation accuracy will increase, but also the computational burden of the EKF will decrease. In this paper, voltage and current samples of the battery at each sample time are the inputs of the algorithms and gained from the known “LA-92 dynamic driving cycle” test. To validate the efficiency of the presented method, results are compared with the EKF algorithm and direct method. The experimental results show that the proposed method reduces the EKF calculations by almost 15 % and guarantee high accuracy SOC estimation. The maximum error of the proposed method is <2.5 %.

A novel approach to estimate the state of charge for lithium-ion battery under different temperatures incorporating open circuit voltage online identification

The open circuit voltage (OCV) is inherently related to the state of charge (SoC) and their relationships under different temperatures are crucial for accurate SoC estimation for the lithium-ion battery based on the equivalent circuit model (ECM), which requires long time-consuming offline OCV tests. In this research, an online closed-loop SoC estimation without conducting OCV tests is put forward. Firstly, the parameters of the Thevenin model for the lithium-ion battery are identified online through the adaptive recursive least square with forgetting factor (AFFRLS). Afterwards, the adaptive unscented Kalman filter (AUKF) is applied to achieve the online closed-loop SoC estimation with the identified parameters. Subsequently, the relationships between the OCV and SoC under different temperatures have been reconstructed online. The proposed method is validated under different temperatures. The research reveals this method can accurately estimate the SoC and is robust to the initial SoC values in wide temperature range.

Design and Implementation of a Battery Big Data Platform Through Intelligent Connected Electric Vehicles

The development of a battery management algorithm is highly dependent on high-quality battery operation data, especially the data in extreme conditions such as low temperatures. The data in faults are also essential for failure and safety management research. This study developed a battery big data platform to realize vehicle operation, energy interaction and data management. First, we developed an electric vehicle with vehicle navigation and position detection and designed an environmental cabin that allows the vehicle to operate autonomously. Second, charging and heating systems based on wireless energy transfer were developed and equipped on the vehicle to investigate optimal charging and heating methods of the batteries in the vehicle. Third, the data transmission network was designed, a real-time monitoring interface was developed, and the self-developed battery management system was used to measure, collect, upload, and store battery operation data in real time. Finally, experimental validation was performed on the platform. Results demonstrate the efficiency and reliability of the platform. Battery state of charge estimation is used as an example to illustrate the availability of battery operation data.

SOC estimation and fault diagnosis framework of battery based on multi-model fusion modeling

Accurate estimation of the battery state characteristics can ensure the safe driving of electric vehicles(EVs). This paper aims to propose a new method for state of charge (SOC) estimation and fault diagnosis based on multiple equivalent circuit models(ECMs) fusion approach. This paper improves the accuracy of SOC estimation and fault diagnosis from the following four aspects. Firstly, the Thevenin model and second-order ECM are selected to describe the dynamic characteristics of the battery and the least square method is then used to determine the model parameters. Besides, the unscented Kalman filter(UKF) is employed to estimate the SOC from the two models and the Bayesian theorem is employed to determine the optimal weights for synthesizing the SOCs estimated from the two models. Moreover, the voltage residual innovation sequence(RIS) is introduced to adaptively adjust the size of the window width in real-time, which can promote the iterative performance of traditional UKF and the prediction ability of the models. Finally, an adaptive fault diagnosis framework based on multiple ECMs fusion is constructed to determine the current battery operation status based on the probability weight of each model, and also to achieve accurate early warning of the current sensor fault. The experimental results show that the SOC estimation errors are controlled within 1 %, and the battery status and current sensor fault can also be accurately diagnosed.

Analysis of a Li-ion battery state of charge by artificial neural network

The state of charge (SOC) is a battery residual capacity crucial assessment metric. The need for a precise SOC estimate is very important to ensure the safe functioning of a Li-ion battery and to prevent overload and over-depletion. However, the renewable energy-based standalone application has become a key problem to determine the exact capacity of SOC of the Li-ion battery. To estimate the capacity over time, the battery management system calculates the SOC of a Li-ion battery. This allows for the implementation of intelligent control systems. This paper presents an enhanced radial basis function (RBF) of the SOC battery estimate following the limits and weaknesses of the back propagation (BP) neural network (NN) in estimating battery SOC, such as sluggish convergence speed, poor generalization and can increase the accuracy of the network but it takes time to iterate. Train the enhanced RBF with experimental data in real-time. The trained NN of SOC is compared to actual values and the MATLAB is used to simulate the method to evaluate its accuracy.

Uncertainty Management in Lebesgue-Sampling-Based Li-Ion Battery SFP Model for SOC Estimation and RDT Prediction

The state-of-charge (SOC) estimation and remaining-dischargeable-time (RDT) prediction based on the simplified first principle (SFP) model under Lebesgue sampling (LS) has advantages of less computation and higher performance. The accuracy, precision, and convergence speed of SOC estimation and RDT prediction are significantly influenced by the model. This article introduces an online dichotomy method for the parameter adaptation of the LS-based SFP model to improve the accuracy and convergence speed. Besides, the uncertainty of RDT prediction is managed by adjusting model noises through a short-term correction loop to improve the precision of RDT prediction. The developed SFP is then integrated with an extended Kalman filter (EKF) in the LS framework for lithium-ion battery SOC estimation and RDT prediction. Experimental results show that the proposed approach can greatly improve the performance in terms of convergence speed, accuracy, and computing efficiency. Compared with the LS-EKF, the convergence speed and RDT prediction accuracy of the proposed method are improved by 98.6% and 47.4% under dynamic loading profiles, respectively. The absolute average error of SOC estimation keeps in the range of 2%.

Assessing the Limits of Equivalent Circuit Models and Kalman Filters for Estimating the State of Charge: Case of Agricultural Robots

The battery State of Charge (SoC) is critical information to overcome agricultural robots’ limitations related to battery and energy management. Although several SoC estimation methods have been proposed in the literature, the performance of these methods has not been validated for different battery chemistries in agricultural mobile robot applications. Compared to previous work, this paper evaluates the limits of the SoC estimation using the RC model and the Thevenin model for a Lithium Iron Phosphate (LFP) battery and a Sealed Lead Acid (SLA) battery. This evaluation used a custom agricultural robot in a controlled indoor environment. Consequently, this work assessed the limitations of two ECM-based SoC estimation methods using battery packs, low-cost sensors and discharge cycles typically used in agricultural robot applications. Finally, the results indicate that the RC model is not suitable for SoC estimation for LFP battery; however, it achieved a mean absolute error (MAE) of 2.2% for the SLA battery. On the other hand, the Thevenin model performed properly for both chemistries, achieving MAE lower than 1%.

Active acoustic emission sensing for fast co-estimation of state of charge and state of health of the lithium-ion battery

With the rapid development of clean energy technologies, the lithium-ion batteries have emerged as dominant power source. It is of great significance to monitor the state of charge (SOC) and state of health (SOH) accurately and efficiently for ensuing high safety and reliability. This paper proposes an active acoustic emission (AE) sensing technology and demonstrates the feasibility for co-estimation of SOC and SOH of the lithium-ion battery. The proposed method aims to achieve a fast monitoring of SOC and SOH by putting insights into the variation of material properties during operations. In the method, the appropriate power ultrasound is used to excite the battery and trigger elastic waves. A wide AE transducer can actively capture the released AE events of the battery material with different properties in a wide range of frequency band. Therefore, this allows the battery SOC/SOH to be fast characterised during charging, discharging, and ageing. Experimental studies have found that calculated RMS of AE signals in the frequency band 270–300 kHz, around the 7th harmonic of ultrasonic excitation, can successfully achieve the joint estimation of battery SOC and SOH at any operating phases. This new finding can be designed as a standalone method for fast SOC/SOH estimation.

Recursive ARMAX-Based Global Battery SOC Estimation Model Design using Kalman Filter with Optimized Parameters by Radial Movement Optimization Method

Precise estimating battery state-of-charge (SOC) is an important factor in determining vehicle range in an electric or hybrid vehicle. However, the parameters of battery are highly dependent on environmental conditions such as temperature. One of the main drawbacks in battery SOC estimation methods by using existing approaches is inability to adapt to the variable environmental operating conditions. In this study, a hybrid technique which combines the Kalman filter and recursive autoregressive exogeneous moving average model is proposed for the online battery parameters estimation. To design robust SOC estimation to varying battery parameters, the parameters of Kalman filter are optimized with the radial movement optimization metaheuristic algorithm. The proposed approach is implemented considering both different temperatures (0 °C, 25 °C, and 40 °C) and driving test cycles, UDDS, LA-92, and US06. Second-order RC battery equivalent model-based approach are compared with the proposed method, and the result shows that the proposed method is better than the conventional method in the aspects of average statistical parameters for all the driving test cycles.

Improved Backward Smoothing—Square Root Cubature Kalman Filtering and Variable Forgetting Factor—Recursive Least Square Modeling Methods for the High-Precision State of Charge Estimation of Lithium-Ion Batteries

Accurate lithium-ion battery charge state estimation is crucial for battery management systems. Modeling of dual polarization—electrical equivalent circuit based on ternary lithium batteries as a research object, a variable forgetting factor recursive least square method is proposed for parameter identification given the insufficient tracking ability of the traditional recursive least squares method for abrupt and time-varying signals in a non- stationary environment. A backward smoothing square root cubature Kalman filtering algorithm is applied to enhance the accuracy and convergence speed of SOC estimation. The algorithm uses the square root update to ensure the numerical stability of the filtering and uses the idea of backward smoothing-forward filtering to improve the filtering accuracy on the basis of the first forward filtering. Finally, variable forgetting factor recursive least square is combined with backward smoothing square root cubature Kalman filtering to achieve the joint estimation of model parameters and state of charge, and the feasibility of the battery state of charge estimation is verified in different working conditions. The simulation results show that the variable forgetting factor recursive least square-backward smoothing square root cubature Kalman filter algorithm improves the study’s filtering accuracy and convergence speed of lithium-ion batteries.

The Scheme for SOC Estimation of Lithium-ion Batteries based on EQ-OCV-Ah-EKF

The state of charge (SOC) of a battery is one of the most important indicators to monitor in a Battery Management System (BMS). It is very important to accurately and quickly predict the state of battery charge for the safe driving of electric vehicles and the efficient utilization of battery equipment. However, several factors, such as the working environment and the aging degree of the battery, can have a coupling effect on the internal state of the battery. This leads it difficult to accurately predict the SOC of a battery. For this purpose, this paper proposes an online estimation scheme for the SOC of Li-ion batteries with an equivalent circuit model and an extended Kalman filtering algorithm. Firstly, a batch of ternary Li-ion batteries are charged and discharged at constant current with 1C current condition under constant temperature at 25°C using the hybrid power pulse (HPPC) test method, and the collected data are pre-processed. Then, the parameters of the second-order Thevenin circuit model were fitted by combining the Genetic Algorithm (GA) and Levenberg-Marquardt algorithm (LM) and verified by the MATLAB program. Finally, the Extended Kalman algorithm (EKF) is leveraged to further improve the accuracy of prediction results. The proposed scheme also has some reference significance for the identification of equivalent circuit model parameters of lithium-ion batteries and the rapid estimation of battery charge states.

Combining Multi-Scale Convolutional Neural Network with Long Short-Term Memory Neural Network for State of Charge Estimation of Lithium-ion Batteries

To develop safe and intelligent battery management systems for electric vehicles, it is necessary to accurately estimate the state of charge (SOC) of lithium-ion batteries. At present, deep learning methods have been broadly applied in the field of SOC estimation of lithium-ion batteries. However, existing deep SOC estimators are difficult to capture global trends due to being too sensitive to the changes of continuous time data points. In addition, a single non-linear neural network tends to ignore the linear features of the data, which makes the robustness of the estimation not good enough. To address these two issues, this paper proposes a SOC estimation method by combining multi-scale convolutional neural network with long short-term memory neural network (called MCNN-LSTM). Specifically, on the one hand, multiple one-dimension convolutions with different dilation rates are used to extract features at different time scales, and LSTM neural networks are used to acquire the long-term dependencies of the data. On the other hand, an additional fully connected layer is used to extract the linear features, which reduces the volatility of the estimation and enhances the robustness of the estimation. The results suggest that MCNN-LSTM has higher estimation accuracies and better robustness than DNN, LSTM, and CNN-LSTM.

A Systematic and Comparative Study of Distinct Recurrent Neural Networks for Lithium-Ion Battery State-of-Charge Estimation in Electric Vehicles

The precise estimation of the state of charge (SOC) is fundamental to the reliable operation of lithium-ion batteries. The development of deep learning techniques makes it possible to employ advanced methods to estimate a battery’s SOC. In order to better utilize a recurrent neural network (RNN) for battery SOC estimation, this paper conducts a comparative study of SOC estimation methods based on different RNN models. First, a general framework for deep-learning-based SOC estimation is undertaken, followed by the description of four kinds of RNNs employed in the estimation. Then, the estimation performances of these RNN models are compared under three scenarios, including the SOC estimation accuracy, the adaptability against different battery aging statuses, and the robustness against measurement uncertainties, in which the estimation performances of different RNN models are quantitively evaluated. Finally, a multiple-criteria decision-making method based on the analytic hierarchy process (AHP) is utilized to reflect the comprehensive performance of each RNN model, and the model with the highest score could be chosen for online SOC estimation during actual applications. This paper provides an in-depth analysis of RNN models in battery SOC estimation and could help battery management engineers develop the most appropriate estimation methods.

Interacting Multiple Model for Lithium-Ion Battery State of Charge Estimation Based on the Electrochemical Impedance Spectroscopy

In terms of the dynamic changes of battery model parameters in a single-model filtering algorithm, the filter estimation accuracy can be poor, and filtering is scattered due to the different internal state parameters of lithium-ion batteries in different aging states, which affects the state of charge (SOC). In order to address these issues, an Interacting Multiple Model (IMM) algorithm was proposed in this study, which adopted an Unscented Kalman Filter (UKF) to better approximate the nonlinear characteristics of the state equation while better stabilizing the filter and having lower computational requirements. Accordingly, the IMM was used to solve the problem of the accurate estimation of the SOC under the dynamic change of model parameters. Moreover, an electrochemical impedance spectrum was used to establish the electrochemical model, after which the lithium-ion equivalent electrochemical circuit model was established, which improved the complexity problem due to its high accuracy but complicated the calculation of the multi-order equivalent circuit model. By conducting experiments and simulations, the algorithm of IMM-UKF was shown to achieve an effective estimation of the battery SOC, even when the state parameters of lithium-ion batteries were uncertain.

Sensorless Coestimation of Temperature and State-of-Charge for Lithium-Ion Batteries Based on a Coupled Electrothermal Model

Accurate estimations of the temperature and the state-of-charge (SOC) are of extreme importance for the safety of lithium-ion battery operation. Traditional battery temperature and SOC estimation methods often omit the relation between battery temperature and SOC, which may lead to significant errors in the estimations. This study presents a coupled electrothermal battery model and a coestimation method for simultaneously estimating the temperature and SOC of lithium-ion batteries. The coestimation method is performed by a coupled model-based dual extended Kalman filter (DEKF). The coupled estimators utilizing electrochemical impedance spectroscopy (EIS) measurements, rather than utilizing direct battery surface measurements, are adopted to estimate the battery temperature and SOC, respectively. The information being exchanged between the temperature estimator and the SOC estimator effectively improves the estimation accuracy. Extensive experiments show that, in contrast with the EKF-based separate estimation method, the DEKF-based coestimation method is more favorable in reducing errors for estimating both the temperature and SOC even if the battery core temperature has increased by 17°C or more during the process of test.

End-Cloud Collaboration Approach for State-of-Charge Estimation in Lithium Batteries Using CNN-LSTM and UKF

The accurate estimation of the state of charge (SOC) plays a crucial role in ensuring the range of electric vehicles (EVs) and the reliability of the EVs battery. However, due to the dynamic working conditions in the implementation of EVs and the limitation of the onboard BMS computational force, it is challenging to achieve a reliable, high-accuracy and real-time online battery SOC estimation under diverse working scenarios. Therefore, this study proposes an end-cloud collaboration approach of lithium-ion batteries online estimate SOC. On the cloud-side, a deep learning model constructed based on CNN-LSTM is deployed, and on the end-side, the coulomb counting method and Kalman’s filter are deployed. The estimation results at both sides are fused through the Kalman filtering algorithm, realizing high-accuracy and real-time online estimation of SOC. The proposed approach is evaluated with three dynamic driving profiles and the results demonstrate the proposed approach has high accuracy under different temperatures and initial errors, where the root means square error (RMSE) is lower than 1.5% and the maximum error is lower than 5%. Furthermore, this method could achieve high-accuracy and real-time SOC online estimation under the cyber hierarchy and interactional network (CHAIN) framework and can be extended to multi-state collaborative online estimation.