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Lithium-ion batteries are commonly employed in energy storage because of their extended service life and high energy density. This trend has coincided with the rapid growth of renewable energy and electric automobiles. However, as usage cycles increase, their effectiveness diminishes over time, which can undermine both the system's performance and security. Therefore, monitoring the state of charge (SOC) and state of health (SOH) of batteries in real time is particularly important. Traditional SOC calculation methods typically treat SOC and SOH as independent variables, overlooking the coupling between them. To tackle this issue, the paper introduces a joint SOC-SOH estimation approach (BiLSTM-SA) that leverages a bidirectional long short-term memory (BiLSTM) network combined with a self-attention (SA) mechanism. The proposed approach is validated using a publicly available dataset. With the SOH taken into account, the MAE and RMSE of the SOC are 0.84% and 1.20%, showing notable increases in accuracy relative to conventional methods. Additionally, it demonstrates strong robustness and generalization across datasets with multiple temperatures. [ABSTRACT FROM AUTHOR]

Single-flow zinc–nickel batteries are a novel type of flow batteries that feature a simple structure, large-scale energy storage capacity, and low cost. The state of charge (SOC) is a crucial indicator of battery performance, providing essential data for the management and control of the battery management system. However, in the estimation of SOC using a traditional unscented Kalman filter, the covariance matrix P often falls into a non-positive definite state during the decomposition steps, leading to algorithm failure. To address this issue, this paper incorporates the singular-value decomposition method into the unscented Kalman filter, resulting in an improved unscented Kalman filter algorithm. Considering the potential low accuracy of a single filtering method for SOC estimation, the improved unscented Kalman filter algorithm is combined with the extended Kalman filter for joint estimation. Experimental comparisons demonstrate that the improved unscented Kalman filter and extended Kalman filter joint estimation achieve higher estimation accuracy compared to using the improved unscented Kalman filter alone. [ABSTRACT FROM AUTHOR]

More and more attention has been paid to ships with a DC power grid. State-of-charge (SOC) estimation is a pivotal and challenging assignment for lithium-ion batteries in such ships. However, the precision of SOC estimation is strongly connected with the system parameters. To better identify these parameters in lithium-ion batteries, a differential evolution (DE) algorithm was introduced into this paper as the optimizer. Initially, a first-order RC equivalent circuit model (ECM) was created to characterize the battery's dynamic behavior. Following this, to estimate open-circuit voltage (OCV) throughout the entire dynamic process, a math model of optimization was established to minimize inaccuracies between the real and estimated terminal voltages. Moreover, estimated SOC values were obtained through OCV-SOC mappings and were contrasted against the true SOC values. The findings manifested the efficacy of the presented structure and technique in comparison with various frequently-cited DE variants. [ABSTRACT FROM AUTHOR]

This paper proposes an SOC estimation method for lithium battery, which combines the online parameter identification and an improved particle filter algorithm. Targeted at the particle degradation issue in particle filtering, grey wolf optimization is introduced to optimize particle distribution. Its strong global optimization ability ensures particle diversity, effectively suppresses particle degradation, and improves the filtering accuracy. The recursive least square method with forgetting factor is also introduced to update the model parameters in a real-time manner, which further improves the estimation accuracy of SOC alternately with the improved particle filter algorithm. Experimental results validate the proposed method, with an average estimation error less than ±0.15%. Compared with conventional extended Kalman filter and unscented Kalman filter algorithms, the proposed algorithm has higher estimation accuracy and stability for battery SOC estimation. [ABSTRACT FROM AUTHOR]

This paper presents a method to validate state of charge (SOC) estimations in batteries for their use in remotely manned aerial vehicles (UAVs). The SOC estimation must provide the mission control with a measure of the available range of the aircraft, which is critical for extended missions such as search and rescue operations. However, the uncertainty about the initial state and depth of discharge during the mission makes the estimation challenging. In order to assess the estimation provided to mission control, an a posteriori re-estimation is performed. This allows for the assessment of estimation methods. A reverse-time Kalman estimator is proposed for this task. Accurate SOC estimations are crucial for optimizing the utilization of multiple UAVs in a collaborative manner, ensuring the efficient use of energy resources and maximizing mission success rates. Experimental results for LiFePO4 batteries are provided, showing the capabilities of the proposal for the assessment of online SOC estimators. [ABSTRACT FROM AUTHOR]

An accurate assessment of the state of charge (SOC) of electric vehicle batteries is critical for implementing frequency regulation and peak shaving. This study proposes mechanism- and data-driven SOC fusion calculation methods. First, a second-order Thevenin battery model is developed to obtain the physical parameters of the battery. Second, data from the Thevenin battery model and data from four standard cycling conditions in the electric vehicle industry are added to the dataset of the feed-forward neural network data-driven model to construct the test and training sets of the data-driven model. Finally, the error of the mechanism and data-driven fusion modeling method is quantitatively analyzed by comparing the estimation error of the method for the battery SOC at different temperatures with the accuracy of the data-driven SOC estimation method. The simulation results show that the root mean square error, the mean age absolute error, and the maximum error of mechanism and data-driven method for the estimation error of battery SOC are lower than those of the data-driven method by 0.9%, 0.65%, and 1.3%, respectively. The results show that the mechanism and data-driven fusion SOC estimation method has better generalization performance and higher SOC estimation accuracy. [ABSTRACT FROM AUTHOR]

The state of charge (SOC) is a core parameter in the battery management system for LMFP batteries. Accurate SOC estimation is crucial for ensuring the safety and reliability of energy storage applications and new energy vehicles. In order to achieve better SOC estimation accuracy, this article proposes an adaptive whale optimization algorithm (WOA) with chaotic mapping to improve the BP neural network (BPNN) model. The SOC estimation accuracy of the BPNN model was improved by utilizing WOA to find the optimal target weight values and thresholds. Comparative simulation experiments (including constant current and working condition discharge experiments) were conducted in Matlab/Simulink R2018a to verify the proposed algorithm and the other four algorithms. The experimental results show that the proposed algorithm had higher SOC estimation accuracy than the other four algorithms, and its prediction errors were less than 1%. This indicates that the proposed SOC estimation method has better prediction accuracy and stability, and has certain theoretical research significance. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are essential to modern technology, requiring accurate estimation of the state of charge (SOC) for optimal performance. Traditional methods such as Coulomb Counting (CC) are ineffective in the face of temperature variations, leading to inaccuracies in SOC estimation, which in turn cause obvious deformation of hysteresis curves. To address this, this paper introduces a novel method called Polynomial Fit State of Charge (FPSOC), for effective SOC estimation. This method incorporates a fifth-degree polynomial fitting that accounts for a wide range of temperature variations (from -10 °°C to +80 °°C), a feature that, according to the authors, has not been offered by all previously published methods. A series of simulation tests using the MATLAB/Simulink tool are conducted under various temperature profiles to evaluate the effectiveness of the FPSOC method. The results demonstrate the notable superiority of the FPSOC model compared to the CC method, with a significantly reduced RMSE of only 0.93 % compared to 6.77 % of the CC model. Particularly effective at low SOC levels (30 %), the FPSOC model demonstrates precision up to six times higher compared to the CC model. Additionally, when evaluated against other recent SOC estimation techniques such as CM, RLSF, EKF, DST, BBDST, ASMO, LPM_H, LSTM-SA Group A and B, and baseline ECM-ID, The FPSOC method proves extremely accurate, with the lowest average error under different temperature conditions.

This paper introduces a novel convolutional neural network (CNN) architecture tailored for state of charge (SoC) estimation in battery management systems (BMS), accompanied by an advanced optimization technique to enhance training efficiency. The proposed CNN architecture comprises multiple one-dimensional convolutional (Conv1D) layers followed by batch normalization and one-dimensional max-pooling (MaxPooling1D) layers, culminating in dense layers for regression-based SoC prediction. To improve training effectiveness, we introduce an advanced dynamic k-decay learning rate scheduling method. This technique dynamically adjusts the learning rate during training, responding to changes in validation loss to fine-tune the training process. Experimental validation was conducted on various drive cycles, including the dynamic stress test (DST), Federal Urban Driving Schedule (FUDS), Urban Dynamometer Driving Schedule (UDDS), United States 2006 Supplemental Federal Test Procedure (US06), and Worldwide Harmonized Light Vehicles Test Cycle (WLTC), spanning four temperature conditions (−5 °C, 5 °C, 25 °C, 45 °C). Notably, the test error of DST and US06 drive cycles, the CNN with optimization achieved a mean absolute error (MAE) of 0.0091 and 0.0080, respectively at 25 °C, and a root mean square error (RMSE) of 0.013 and 0.0095, respectively. In contrast, the baseline CNN without optimization yielded higher MAE and RMSE values of 0.011 and 0.014, respectively, on the same drive cycles. Additionally, training time with the optimization technique was significantly reduced, with a recorded time of 324.14 s compared to 648.59 s for the CNN without optimization at room temperature. These results demonstrate the effectiveness of the proposed CNN architecture combined with advanced dynamic learning rate scheduling in accurately predicting SoC across various battery types and drive cycles. The optimization technique not only improves prediction accuracy but also substantially reduces training time, highlighting its potential for enhancing battery management systems in electric vehicle applications. [ABSTRACT FROM AUTHOR]

With the continuous development of the industrial and energy industries, the development of new energy vehicles is entering a period of rapid development and is one of the hot research directions today. Due to the needs of different working environments, the demand for mobile power sources in automobiles is increasing, which means that battery design and battery system management (BMS) determine their work efficiency. How to enable users to accurately and in real-time understand the usage status of their electric vehicle batteries is a very important thing, and it is also an important challenge faced in the development process of electric vehicles. This article proposes a battery state-of-charge (SOC) estimation method based on the extended Kalman filter algorithm (EKF) for one of the core areas of the BMS–battery state-of-charge (SOC). According to the guidance and direction of Industry 4.0 in Germany, we hope to address some of the aforementioned challenges for users of automotive and robotics products while developing our industry. Therefore, we made some innovative explorations in this direction. In this study, it was found that the algorithm can adjust parameters in real-time to achieve better convergence. The final estimation results indicate that the algorithm had high accuracy and robustness and can meet the current needs of battery estimation for new energy vehicles, providing an important means for the safety control of automotive BMS. In the long run, this will change the current situation of battery monitoring using mobile power sources. At the same time, it provided an effective and practical implementation method and template for current production estimation, which has a certain heuristic effect on the future process of Industry 4.0 and production estimation. [ABSTRACT FROM AUTHOR]

Abstract-In recent years, electric vehicles have garnered significant attention, with lithium-ion batteries (LIBs) being central to their operation. Researchers and scholars have prioritized the accurate estimation of the state of charge (SOC) within the battery management system (BMS) as a key area of study. In this paper, by analyzing different equivalent circuit models, we choose to use the second-order RC model, elaborate the Kalman filter (KF) principle, and propose the adaptive extended Kalman filter (AEKF) to construct the estimation model of SOC. MATLAB validates the AEKF estimation model under two different operating conditions, UDDS and LA92, and the results show that the designed model can efficiently and accurately estimate the battery charge state with high competitiveness and accurately predict the real SOC direction regardless of the initial state, AEKF is more competitive than KF in terms of SOC prediction accuracy, Despite the different initial values of SOC, the roof-mean-square error of prediction was able to be controlled around one percent. [ABSTRACT FROM AUTHOR]

Lithium (Li) cells find widespread applications, particularly in electric vehicles their dynamic characteristics are often represented through equivalent circuit models. In this study, two different second-order equivalent circuit models of LiFePO4 cells are modeled and simulated in MATLAB/Simulink. The first model exhibits capacity changes based on drawn current, while the second assumes constant capacity. The analysis of the simulations results focuses on key parameters such as State of Charge (SOC), Open Circuit Voltage (OCV), and terminal voltage (VT). Comparative evaluations between the first and second cell models utilize formulas derived from prior experimental cell studies. Specifically, a 0.0155% variance in SOC, a 0.00003% difference in OCV, and a 0.00003% distinction in VT were observed between the two models during discharge. A similar assessment during charging observed an error of 0.0447% in SOC, 0.00007% in OCV, and 0.00003% in VT. Furthermore, the discharge process in the first model demonstrates lower SOC, OCV, and VT values, contrasting with higher values during charging. Despite these variances, the study concludes that both models yield similar results, establishing them as viable references for equivalent circuit representations of Lithium cells. [ABSTRACT FROM AUTHOR]

Timely and accurate fault diagnosis for a lithium-ion battery pack is critical to ensure its safety. However, the early fault of a battery pack is difficult to detect because of its unobvious fault effect and nonlinear time-varying characteristics. In this paper, a fault diagnosis method based on relative entropy and state of charge (SOC) estimation is proposed to detect fault in lithium-ion batteries. First, the relative entropies of the voltage, temperature and SOC of battery cells are calculated by using a sliding window, and the cumulative sum (CUSUM) test is adopted to achieve fault diagnosis and isolation. Second, the SOC estimation of the short-circuit cell is obtained, and the short-circuit resistance is estimated for a quantitative analysis of the short-circuit fault. Furthermore, the effectiveness of our method is validated by multiple fault tests in a thermally coupled electrochemical battery model. The results show that the proposed method can accurately detect different types of faults and evaluate the short-circuit fault degree by resistance estimation. The voltage/temperature sensor fault is detected at 71 s/58 s after faults have occurred, and a short-circuit fault is diagnosed at 111 s after the fault. In addition, the standard error deviation of short-circuit resistance estimation is less than 0.12 Ω/0.33 Ω for a 5 Ω/10 Ω short-circuit resistor. [ABSTRACT FROM AUTHOR]

In high-energy and high-power applications, thousands of batteries are connected in series and parallel, imposing a substantial computational burden for state of charge (SOC) estimation. The second-order RC equivalent circuit model is often utilized for SOC estimation. However, this model requires the identification of numerous parameters, rendering the calculations complex and computationally intensive. Furthermore, the model often neglects the impact of temperature. To enhance the speed and accuracy of SOC estimation for numerous individual cells, an equivalent circuit model is constructed. This model incorporates temperature correlation coefficients and the electrical characteristics of lithium-ion batteries at various temperatures. Subsequently, a combined forgetting factor recursive least squares and extended Kalman filter algorithm is introduced for battery SOC estimation. The results demonstrate that the improved model significantly reduces SOC estimation time. Compared to the traditional second-order RC model, the improved model reduces the time by 37.8%, 58.3%, and 34% at − 10 °C, 0 °C, and 25 °C, respectively. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

More and more attention has been paid to ships with a DC power grid. State-of-charge (SOC) estimation is a pivotal and challenging assignment for lithium-ion batteries in such ships. However, the precision of SOC estimation is strongly connected with the system parameters. To better identify these parameters in lithium-ion batteries, a differential evolution (DE) algorithm was introduced into this paper as the optimizer. Initially, a first-order RC equivalent circuit model (ECM) was created to characterize the battery’s dynamic behavior. Following this, to estimate open-circuit voltage (OCV) throughout the entire dynamic process, a math model of optimization was established to minimize inaccuracies between the real and estimated terminal voltages. Moreover, estimated SOC values were obtained through OCV-SOC mappings and were contrasted against the true SOC values. The findings manifested the efficacy of the presented structure and technique in comparison with various frequently-cited DE variants.

By the original estimation method, the state of charge (SOC) of lithium battery pack is measured after battery discharge. When the battery internal resistance is large, it is difficult to obtain clear open circuit voltage, leading to errors in SOC estimation of lithium battery pack. To solve the problem, the SOC estimation method of lithium battery pack based on segmented polymerization and Kalman filter was designed. Based on the construction of equivalent circuit model, the parameters of lithium battery were identified, and the SOC estimation indexes such as open circuit voltage were defined. The feedback path of lithium battery was switched by piecewide polymerization, and the SOC value of lithium battery was estimated by linearly recursive Kalman filter. The experimental results show that under the pulse discharge condition of lithium battery, the estimation result of the proposed method is basically consistent with the actual SOC value, and the estimation error can be controlled within 0.4% when SOC is 0.6. [ABSTRACT FROM AUTHOR]

This paper considers the estimation of SOC and SOH for lithium batteries using multi-innovation Levenberg–Marquardt and adaptive weighting unscented Kalman filter algorithms. For parameter identification, the second-order derivative of the objective function to optimize the traditional gradient descent algorithm is used. For SOC estimation, an adaptive weighting unscented Kalman filter algorithm is proposed to deal with the nonlinear update problem of the mean and covariance, which can substantially improve the estimation accuracy of the internal state of the lithium battery. Compared with fixed weights in the traditional unscented Kalman filtering algorithm, this algorithm adaptively adjusts the weights according to the state and measured values to improve the state estimation update accuracy. Finally, according to simulations, the errors of this algorithm are all lower than 1.63 %, which confirms the effectiveness of this algorithm. [ABSTRACT FROM AUTHOR]

Embracing the potential of cloud technology in the field of electric vehicle advancements, this paper explores the application of support vector regression (SVR) for accurate state of charge (SOC) estimation of lithium-ion batteries in various computational landscapes. This study aims to scrutinize and compare the performance of SOC estimation, with a specific focus on precision, computational efficiency, and execution speed. The investigation is conducted across diverse environments, including a traditional non-cloud setup and two cloud-based platforms-a standard cloud environment employing Amazon web services (AWS) EC2 servers and an enhanced configuration utilizing the MATLAB production server. The investigation not only emphasizes the effectiveness of cloud integration but also provides valuable insights into the strengths and weaknesses of the proposed methodology. The experimental results contribute to a nuanced understanding of the methodology's performance, shedding light on its potential implications for advancing electric vehicle technologies. This study thus extends its significance beyond technical considerations, providing a broader perspective on its relevance to global electrification initiatives. [ABSTRACT FROM AUTHOR]

Flooded Lead Acid (FLA) batteries remain a cost-effective choice in various industries. Accurate State of Charge (SOC) estimation is crucial for effective battery management systems. This paper thoroughly examines the behavior of Open-Circuit Voltage (OCV) during hysteresis in FLA batteries, proposing a novel hysteresis modeling approach based on this behavior to enhance the SOC estimation accuracy. Additionally, we introduce an Adaptive Unscented Kalman Filter (AUKF) to further refine the SOC estimation precision. Experimental validation confirms the effectiveness of the proposed hysteresis modeling. A comparative analysis against the traditional Unscented Kalman Filter (UKF) under random charge/discharge profiles underscores the superior performance of AUKF, showcasing an improved convergence to the correct SOC value and a significant reduction in the SOC estimation error to approximately 2%, in contrast to the 5% error observed with the traditional UKF. [ABSTRACT FROM AUTHOR]

The safe and efficient operation of the electric vehicle significantly depends on the accurate state-of-charge (SOC) and state-of-temperature (SOT) of Lithium-ion (Li-ion) batteries. Given the influence of cross-interference between the two states indicated above, this study establishs a co-estimation framework of battery SOC and SOT. This framwork is based on an innovative electrothermal model and adaptive estimation algorithms. The first-order RC electric model and an innovative thermal model are components of the electrothermal model. Specifically, the thermal model includes two lumped-mass thermal submodels for two tabs and a two-dimensional (2-D) thermal resistance network (TRN) submodel for the main battery body, capable of capturing the detailed thermodynamics of large-format Li-ion batteries. Moreover, the proposed thermal model strikes an acceptable compromise between the estimation fidelity and computational complexity by representing the heat transfer processes by the thermal resistances. Besides, the adaptive estimation algorithms are composed of an adaptive unscented Kalman filter (AUKF) and an adaptive Kalman filter (AKF), which adaptively update the state and noise covariances. Regarding the estimation results, the mean absolute errors (MAEs) of SOC and SOT estimation are controlled within 1% and 0.4 ​°C at two temperatures, indicating that the co-estimation method yields superior prediction performance in a wide temperature range of 5–35 ​°C.

Nowadays, utility grid experiences significant challenges due to increased adoption of renewable energy resources (RES). The intermittency of RES is ridden by using battery energy storage (BES). The system control often aims at maximizing self-consumption, neglecting its effect on effective life of BES. A power management strategy (PMS) is proposed in this work that helps to increase the extraction of maximum energy without compromising on operational robustness of BES for grid applications. Maximum depth of discharge (DOD) is limited to a certain mean state of charge (SOC) to improve the cycle life of BES. A lifecycle model is derived for BES to compute a compensating term for estimating the DOD limit after each cycle. An enhancement to existing Linear Kalman filtering (LKF) technique is also presented in this work for SOC estimation of BES. A significant reduction in root mean square error (RMSE) is achieved using improved LKF-based SOC estimation technique. A second-order generalized integrator with a pre-filter based frequency locked loop (SOGI-WPF-FLL) controls microgrid under abnormal utility grid and load conditions. Resynchronization of microgrid with utility grid is demonstrated using SOGI-WPF-FLL filter without causing any maloperations. System is simulated under various operating conditions in MATLAB/Simulink environment and validated on a real-time OP5700-based test-bench.

Accurate estimation of state-of-charge (SoC) is significant for monitoring the operation of LiFePO4 batteries. This paper addresses the challenges posed by the nonlinear characteristics of LiFePO4 batteries, which adversely affect the accuracy of SoC estimation. The proposed novel methodology for SoC estimation in LiFePO4 batteries involves a Support Vector Machine (SVM) assisted Artificial Neural Network (ANN) model incorporating principal component analysis (PCA) to efficiently interpret the input data. To improve the prediction accuracy, the non-linear characteristics of LiFePO4 are divided into three parts and each of these parts is used to train a separate ANN model. Further, an optimal number of hidden layers and neurons are selected for each ANN model to minimize the prediction errors. The usage of dedicated smaller datasets for each ANN model simplifies the structure. SVM classifies the battery operating regions and selects the most suitable ANN model for SoC estimation. PCA is applied to process the obtained experimental data resulting in three principal components serving as inputs for the SVM-assisted ANN model. Further, with PCA the input dimensions are reduced from four to three, thereby leading to computational simplicity. The input data comprises of current, voltage, open-circuit voltage and temperature of the battery. An experimental prototype, comprising a customized battery pack and sensing mechanisms, is developed for data collection for training SVM and ANN models. With the proposed SVM-assisted ANN involving PCA, the loss function is minimized and an average Root Mean Square Error (RMSE) of 0.3133 is achieved. This demonstrates the feasibility, accuracy and applicability of SoC estimation with the developed SVM-assisted ANN model for LiFePO4 batteries.

This paper proposes a novel filtering algorithm for simultaneous estimation of unknown inputs and states of a class of nonlinear discrete-time heterogeneous multi-agent systems. Based on the Taylor approximation of the nonlinear multi-agent system, a distributed semi-cooperative switch-mode filter is developed to achieve the minimum-variance unbiased (MVU) estimation of the unknown inputs and states. Compared with the conventional decentralized EKF-based unknown input filter, the proposed distributed filter has a more relaxed existence condition of the filter, which makes it more applicable in reality. This new type of filter is then successfully applied to the simultaneous estimation of state of charge (SOC) and temperature of a battery pack for battery management of electric vehicles and grid-tied energy storage systems.

Improving the accuracy of state-of-charge (SoC) estimation is crucial for electric vehicles (EVs) using Lithium-Ion batteries (LiBs). This helps users reliably predict driving range and optimize the charging process, thereby extending battery life and ensuring safety during use. However, due to temperature, driving mode, and charge-dependent electrochemical nonlinear dynamics, SoC estimation for LiB integrated with EVs remains a significant technical challenge. In particular, SoC estimation in the regions of SoC < 30% and SoC > 80% is often inaccurate due to nonlinearity and sensitivity to battery aging. Accurate estimation in these regions is crucial for making decisions regarding recharging and discharging to prolong battery life and prevent damage. To address this issue, this paper proposes a method for SoC estimation using a Long Short-Term Memory (LSTM) network, which is capable of retaining information on battery characteristics related to changes long term electrochemical parameter changes, such as the number of discharge cycles and the aging effects. The method utilizes practical data from 80,000 samples collected from pure electric vehicle testing under different driving modes, temperatures, and road conditions over a 30-day period. The LSTM network was optimized by adjusting the input data sequence and hidden size to minimize the number of hyperparameters. This makes it suitable for use on low-cost processors with moderate computing power. SoC estimation was evaluated across four SoC test regions: SoC < 30%, SoC > 80%, 30% ≤ SoC ≤ 80%, and 0% ≤ SoC ≤ 100%. The results were compared with feedforward neural network (FNN) and convolutional neural network (CNN). Despite having a configuration with a hidden size of 96 and a single layer, the LSTM model achieved estimation accuracy with RMSE = 0.0106, MAE = 0.0077, and MAPE = 1.4116%.

Abstract To improve the discharge equalization efficiency of the battery and prevent the occurrence of overdischarge, in this paper, the 18,650 ternary lithium battery is taken as the object of investigation, and an alternating equalization control system for the discharge process of serial cells is proposed. The system implements the alternating discharge of serial cells by switching on and off, using state of charge (SOC) as the equalization variable, and eventually completes the equalization control of the entire battery pack. Discharge simulations were performed in Matlab/Simulink for faulty and normal operating conditions of the battery pack, respectively. The findings indicate that even in the presence of a malfunction, the battery pack can continue to operate continuously for a while; in contrast, under ideal circumstances, the battery pack is capable of maintaining SOC balance throughout the discharge process. Eventually, five batteries are used to construct the experimental platform for the alternating equalization system. The battery pack can still perform selective discharge under fault conditions until the battery pack reaches the discharge cutoff condition. Under normal conditions, the maximum SOC difference of all five batteries can stabilize at about 1%. The experimental results show that the proposed equalization control system can achieve the equalization of battery discharge and prolong the discharge time, and can prevent the occurrence of battery over discharge.

This study aims to implement the digital twin of a Li-ion battery by using real measurement data and to create a deep learning-based SOC (state of charge) estimation solution. In the case of the SOC estimator, a special type of deep learning, so-called long short-term memory (LSTM), was used to increase the capabilities of the estimator. The digital twin and the SOC estimator were created by using MATLAB and MATLAB/Simulink. As a result, the implemented system can accurately simulate the non-linearities of the Li-ion battery and provide a satisfactory estimation of the SOC of the battery.

As a new generation of high-performance batteries, lithium-ion batteries have found extensive applications in electric cars, as well as energy storage systems and various other industries. State of charge (SOC) estimation is one of the most important indicators. SOC estimation model of lithium-ion battery based on deep learning neural networks employs diverse external measurement parameters and internal battery parameters as input information, and adopts feed-forward neural network (FNN), convolutional neural network (CNN) and long short-term memory network (LSTM) as predictors to realize the accurate SOC estimation. The model based on deep learning neural networks takes into account the influence of various input parameters and can understand the state of the battery more comprehensively. By using FNN, CNN and LSTM networks, the influence of noise and instability of battery data on SOC estimation can be effectively avoided. After many times of training and verification, the high accuracy and stability of the model can meet the need of SOC estimation for lithium-ion batteries. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper proposes a new method for assessing the state of charge (SoC) and identifying the types of different lithium-ion cells used in the battery systems of light electric vehicles. A particular challenge in the development of this method was the SoC estimation time, as the method is intended for implementation in the control system of a bicycle charging station, where the state of charge must be determined immediately after the bicycle is plugged in in order to start the charging process as quickly as possible according to the appropriate charging algorithm. The method is based on the identification of the transfer function, i.e., the dynamic response of the battery voltage to the current pulse. In the learning phase of this method, a database of reference transfer functions and corresponding SoCs for a specific type of battery cell is created. The transfer functions are described by coefficients determined through the optimization procedure. The algorithm for estimating the unknown battery cell SoCs is based on the comparison of the measured voltage response with the responses of the reference transfer functions from the database created during the learning process to the same current signal. The comparison is made by calculating the integral of the square error (ISE) between the response of the specific reference transfer function and the measured voltage response of the battery cell. Each transfer function corresponds to a specific SoC and cell type. The specific SoC of the unknown battery is determined by quadratic interpolation of the SoC near the reference point with the smallest ISE for each battery type. The cell type detection algorithm is based on the fact that the integral squared error criterion near the actual SoC for the actual cell type changes less than the squared error criterion for any other battery cell type with the same SoC. An algorithm for estimating the SoC and cell type is described and tested on several different cell types. The relative error between the estimated SoC and the actual SoC was used as a measure of the accuracy of the algorithm, where the actual SoC was calculated using the Coulomb counting method. [ABSTRACT FROM AUTHOR]

State of charge (SOC) estimation is critical for electric vehicles (EVs); the typical solution is the ampere-hour (Ah) counting strategy + open circuit voltage (OCV) strategy as they are straightforward and easy to implement. However, this solution makes a significant SOC estimation error if the driver needs to drive long distances or in winter. This article aims to optimize the Ah counting strategy and propose an online SOC calibration strategy. For the former, we evaluate the effects of temperature, initial SOC, and current on the Coulomb efficiency and the impact of temperature and discharge current on the battery capacity and take them into account when estimating the battery SOC; for the latter, we conduct theoretical analysis and argue that after a while of small-current fluctuations in the battery, the OCV of the battery can be obtained based on the battery voltage, current, and direct current (DC) resistance, and can be calibrated online. We designed experiments to validate the proposed strategy. The experimental results show that the optimized Ah counting strategy does not pull away from the standard Ah counting strategy at room temperature or high temperature, which is because the effects of the Coulombic efficiency and the battery capacity can be canceled out, but the optimized Ah counting strategy has a better performance at low temperature, and vice versa for standard Ah counting performs poorly; for SOC online calibration, the OCV estimated online by the proposed strategy differs from the reference OCV by only 2 mV, and its performance is excellent. The solution proposed in this article can be applied to EVs to obtain better performance. [ABSTRACT FROM AUTHOR]

Virtual power plants (VPPs) can realize the aggregation and coordinated optimization of distributed generation (DG), such as renewable energy (e.g. photovoltaic (PV), wind power) and energy storage systems (ESSs), which is considered an up-and-coming technology. The lithium-ion battery is widely used in VPPs as a high-quality energy storage. Meanwhile, the battery state of charge (SOC) estimation is fundamental, which characterizes the remaining energy, and obtaining an accurate battery SOC is essential for the safe and reliable operation of the ESS. This article proposes a SOC estimation technique for lithium-ion batteries in VPPs containing PV. Considering that the extended Kalman filtering (EKF) can obtain a more accurate battery SOC, it is further optimized based on the EKF to improve the accuracy of its SOC estimation. First, the principle of the general method and its deficiencies are analyzed, and then the proposed improved method is analyzed in detail. Finally, the advantages of the proposed method and the feasibility of applying it to PV-ESSs are analyzed in detail. A test platform is built for validation, and the results show that the SOC estimation errors of the proposed strategy are 1.8%, 2.1%, and 2.3%, respectively, with better performance. [ABSTRACT FROM AUTHOR]

Energy storage systems (ESSs) are critically important for the future of electric vehicles. Due to the shifting global environment for electrical distribution and consumption, energy storage systems (ESS) are amongst the electrical power system solutions with the fastest growing market share. Any ESS must have the capacity to regulate the modules from the system in the case of abnormal situations as well as the ability to monitor, control, and maximize the performance of one or more battery modules. Such a system is known as a battery management system (BMS). One parameter that is included in the BMS is the state-of-charge (SOC) of the battery. The BMS is used to enhance battery performance while including the necessary safety measures in the system. SOC estimation is a key BMS feature, and precise modelling and state estimation will improve stable operation. This review discusses the current methods used in BEV LIB SOC modelling and estimation. It also efficiently monitors all of the electrical characteristics of a battery-pack system, including the voltage, current, and temperature. The main function of a BMS is to safeguard a battery system for machine electrification and electric propulsion. The major responsibility of the BMS is to guarantee the trustworthiness and safety of the battery cells coupled to create high currents at high voltage levels. This article examines the advancements and difficulties in (i) cutting-edge battery technology and (ii) cutting-edge BMS for electric vehicles (EVs). This article's main goal is to outline the key characteristics, benefits and drawbacks, and recent technological developments in SOC estimation methods for a battery. The study follows the pertinent industry standards and addresses the functional safety component that concerns BMS. This information and knowledge will be valuable for vehicle manufacturers in the future development of new SOC methods or an improvement in existing ones. [ABSTRACT FROM AUTHOR]

To improve the discharge equalization efficiency of the battery and prevent the occurrence of overdischarge, in this paper, the 18,650 ternary lithium battery is taken as the object of investigation, and an alternating equalization control system for the discharge process of serial cells is proposed. The system implements the alternating discharge of serial cells by switching on and off, using state of charge (SOC) as the equalization variable, and eventually completes the equalization control of the entire battery pack. Discharge simulations were performed in Matlab/Simulink for faulty and normal operating conditions of the battery pack, respectively. The findings indicate that even in the presence of a malfunction, the battery pack can continue to operate continuously for a while; in contrast, under ideal circumstances, the battery pack is capable of maintaining SOC balance throughout the discharge process. Eventually, five batteries are used to construct the experimental platform for the alternating equalization system. The battery pack can still perform selective discharge under fault conditions until the battery pack reaches the discharge cutoff condition. Under normal conditions, the maximum SOC difference of all five batteries can stabilize at about 1%. The experimental results show that the proposed equalization control system can achieve the equalization of battery discharge and prolong the discharge time, and can prevent the occurrence of battery over discharge. [ABSTRACT FROM AUTHOR]