Lithium Battery Charging Research Articles

State of Charge Estimation Based on Improved AILS-CSO Parameter Identification Algorithm

Abstract This paper introduces an adaptive inertial local search-cat-swarm optimization algorithm. The identification of parameters and estimation of charge state for lithium power batteries under diverse operational conditions were undertaken. In this approach, the equivalent circuit model of lithium power batteries is constructed using a second-order RC equivalent circuit model. Then, the cat swarm optimization algorithm is augmented with an adaptive adjustment algorithm for the inertial weight, thereby enhancing the accuracy of the identified battery parameters. Subsequently, the estimated voltage is then compared with the actual voltage in order to validate the feasibility of the algorithm. The root mean square error (RMSE) is less than 0.56%, and the maximum absolute error is 0.116 V. In conclusion, the state of charge of lithium batteries is estimated online under disparate working conditions via the integration of the extended Kalman filter algorithm, and a comparison is conducted. The maximum error is less than 1.74%, and the RMSE is less than 0.5%, indicating that the algorithm exhibits superior stability and estimation accuracy compared to the traditional recursive least squares method.

State of Charge Estimation of Lithium Battery Utilizing Strong Tracking H-Infinity Filtering Algorithm

The ability to quickly and accurately estimate the state of charge (SOC) of lithium batteries is a key function of the battery management system (BMS). To enhance the accuracy of SOC estimation for lithium batteries, we propose a method that combines the dynamic factor recursive least squares (DFFRLS) algorithm and the strong tracking H-infinity filtering (STF-HIF) algorithm. To address the issue of fixed forgetting factors in recursive least squares (RLS) that struggle to maintain both fast convergence and stability in battery parameter identification, we introduce dynamic forgetting factors. This approach adjusts the forgetting factor based on the residuals between the model’s estimated and actual values. To improve the H-infinity filtering (HIF) algorithm’s poor performance in tracking sudden state changes, we propose a combined STF-HIF algorithm, integrating HIF with strong tracking filtering (STF). Simulation experiments indicate that, compared to the HIF algorithm, the STF-HIF algorithm achieves a maximum absolute SOC estimation error (MaxAE) of 0.69%, 0.72%, and 1.22%, with mean absolute errors (MAE) of 0.27%, 0.25%, and 0.38%, and root mean square errors (RMSE) of 0.33%, 0.30%, and 0.46% under dynamic stress testing (DST), federal urban driving schedules (FUDS), and Beijing dynamic stress testing (BJDST) conditions, respectively.

Multi-innovation adaptive Kalman filter algorithm for estimating the SOC of lithium-ion batteries based on singular value decomposition and Schmidt orthogonal transformation

The state of charge (SOC) of lithium battery is a key parameter for effective management of battery management systems (BMS). To address the problems of low precision, complex computation and poor robustness of traditional charging state estimation methods, an enhanced algorithm based on Unscented Kalman filter (UKF) is proposed. The singular value decomposition (SVD) method is adopted to ensure the normal operation of the Unscented Kalman Filter (UKF) algorithm even when the matrix P lacks positive semi-definiteness from a mathematical perspective. This enhancement significantly improves the theoretical robustness of UKF. Schmidt orthogonal transformation is concurrently used to reduce the computational complexity in the sampling point selection process, and the multi-innovation theory is combined with adaptive noise control to further improve the accuracy of SOC estimation. The algorithm is validated using the Urban Dynamometer Driving Schedule (UDDS) condition. The simulation results are excited using Singular value decomposition-multi-innovation adaptive Schmidt orthogonal unscented Kalman filter method (SVD-MIASOUKF). 0.95 % and 1.29 % of maximum errors are obtained at 25 °C and −10 °C, while the maximum errors are 2.46 %–2.99 % using SVD-UKF and UKF. The proposed approach shows faster convergence speed and higher estimation accuracy in comparison to traditional algorithms.

Generating comprehensive lithium battery charging data with generative AI

In optimizing the performance and extending the lifespan of lithium batteries, accurate state prediction is crucial. Traditional regression and classification methods have achieved some success in predicting battery states. However, the effectiveness of these data-driven approaches largely depends on the availability and quality of public datasets. Additionally, generating electrochemical data primarily through battery experiments is a lengthy and costly process, making it extremely difficult to obtain high-quality data. This difficulty, combined with data incompleteness, significantly affects prediction accuracy. To address these challenges, this study introduces End of Life (EOL) and Equivalent Cycle Life (ECL) as supervised conditions for generative AI models. By integrating an embedding layer into the CVAE model, we developed the Refined Conditional Variational Autoencoder (RCVAE). Through preprocessing data into a quasi-video format, coupled with customized training and inference algorithms, the RCVAE generates the required charging data in real time based on the battery’s ECL and EOL. This avoids storing irrelevant information, saving space and resources. RCVAE uses a lightweight architecture, enabling fast generation of necessary voltage, current, temperature, and charging capacity data. This approach provides users with a comprehensive electrochemical dataset, pioneering a new research domain for the artificial synthesis of lithium battery data. Furthermore, based on the detailed synthetic data, various battery state indicators can be calculated, offering new perspectives and possibilities for lithium battery performance prediction.

Development of an emulator of the sustainable energy harvesting pad system on a bike lane for charging lithium batteries

In response to the urgent imperative of combating global warming and advancing sustainable energy solutions, an innovative approach has emerged, capitalizing on bicycles and road bike lane infrastructure. This solution integrates a Smart Lithium Battery Charging System with a Sustainable Energy Harvesting Pad (SEHP) designed for cyclists. The SEHP harnesses piezoelectric energy from mechanical vibrations and kinetic energy from lightweight vehicles. It produces clean, renewable electricity as an alternative to traditional power sources. Comprehensive assessments of the SEHP's energy generation performance at various proficiency levels have revealed impressive capabilities. An electronic emulator system is developed to support academic and research communities, simulating scenarios on bike lanes to efficiently charge 36.36 Wh lithium batteries at various cycling proficiency levels. The study involved specific circuit design, seamless integration with the custom Smart Lithium Battery Charging System, and optimization using Microcontroller hardware and software solutions. Practical prototypes verified the emulator's functionality and real-world applicability, making it an authentic replica of the SEHP's outcomes. This innovative technology enhances our understanding of SEHP and enables comparative analysis against other energy sources, contributing to a more sustainable future.

A Study of Electric Bicycle Lithium Battery Charging Monitoring Using CNN and BiLSTM Networks Model with NILM Method

Electric bicycles offer convenient short-distance travel, but improper battery charging poses a fire risk, especially indoors, potentially causing significant accidents, property damage, and even threats to life. Recognizing the charging state of electric bicycle batteries is crucial for safety. This paper proposes a novel method to identify the charging process of lithium batteries in electric bicycles. Methods that do not require physical alterations to the equipment are used to acquire users’ electricity consumption data, with current signals preprocessed and input into a combined model integrating convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) networks. The proposed model captures complex patterns and features in the charging data, effectively identifying the charging characteristics of lithium batteries. Validation using NASA’s lithium battery dataset and real experimental data shows that the combined model achieves recognition accuracy of 96% and 97% on training data and 93% and 94% on validation data. Further validation under multiple device loads and comparison with other models indicate that the proposed method is highly accurate, outperforming traditional CNN and LSTM models by 4–9%. This research enhances the safety and regulation of electric bicycle battery charging and provides a reliable method for non-intrusive load identification in smart monitoring systems, contributing to improved safety measures and energy management in residential environments.

Research on precise lithium battery state of charge estimation method based on CALSE-LSTM model and pelican algorithm

This paper presents an innovative fusion model called “CALSE-LSTM,” which integrates Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), self-attention mechanisms, and squeeze-and-excitation attention mechanisms to optimize the estimation accuracy of the State of Charge (SoC). The model incorporates battery historical data as input and employs a dual-attention mechanism based on CNN-LSTM to extract diverse features from the input data, thereby enhancing the model's ability to learn hidden information. To further improve model performance, we fine-tune the model parameters using the Pelican algorithm. Experiments conducted under Urban Dynamometer Driving Schedule (UDDS) conditions show that the CALSE-LSTM model achieves a Root Mean Squared Error (RMSE) of only 1.73 % in lithium battery SoC estimation, significantly better than GRU, LSTM, and CNN-LSTM models, reducing errors by 31.9 %, 31.3 %, and 15 %, respectively. Ablation experiments further confirm the effectiveness of the dual-attention mechanism and its potential to improve SoC estimation performance. Additionally, we validate the learning efficiency of CALSE-LSTM by comparing model training time with the number of iterations. Finally, in the comparative experiment with the Kalman filtering method, the model in this paper significantly improved its performance by incorporating power consumption as an additional feature input. This further verifies the accuracy of CALSE-LSTM in estimating the State of Charge (SoC) of lithium batteries.

The lithium battery state of charge estimation based on the fractional order unscented kalman filter algorithm

Abstract In the assessment of the state of charge (SOC) of lithium batteries, it has been observed that the conventional integer-order second-order resistance-capacitance (RC) network falls short in accurately replicating the non-linear attributes and the precision of SOC estimation influenced by the internal polarization reaction within the battery. Thus, we present the incorporation of constant-phase element (CPE) and Warburg components to achieve a model with superior recognition accuracy compared to the conventional second-order equivalent circuit model(ECM). The parameterization is carried out using the particle swarm optimization algorithm, resulting in enhanced robustness of parameter identification. Later on, when we mixed the regular Unscented Kalman Filter (UKF) algorithm with the fractional-order model (FOM), we obtained an algorithm to estimate the SOC of lithium battery in real time, named FOUKF, and compared FOUKF with the integer-order Extended Kalman Filter (EKF) and UKF algorithms. This confirms that the model is reliable and the algorithm’s estimation is accurate.

Research on non-intrusive load decomposition model based on parallel multi-scale attention mechanism and its application in smart grid

This paper presents the MUSENILM model, a non-intrusive load decomposition model incorporating a parallel multi-scale attention mechanism to enhance energy monitoring and management in smart grids. The core innovation of the proposed model is its ability to extract multi-scale features, enhancing the model's understanding of time series data and achieving significant performance improvements on the UK-DALE and REDD public datasets. Specifically, when MUSENILM identifies the fridge electricity consumption pattern on the UK-DALE public dataset, compared to previous models, the accuracy improves from 88% to 91%, and the F1 score increases from 87% to 90%; on the load decomposition tasks of the remaining four appliances, the F1 scores are all improved, while the mean absolute error (MAE) and cumulative absolute error (SAE) for the five appliances are also reduced. Additionally, it shows better results compared to previous models on the REDD dataset. Moreover, when the MUSENILM model is transplanted to embedded devices and applied in smart grids, it can effectively identify illegal lithium battery charging events of electric bicycles in different scenarios, which is crucial for ensuring grid security and optimizing energy distribution. This research not only provides an efficient method for the field of NILM but also offers practical solutions for violation monitoring and management in smart grids.

Online estimation of lithium battery SOC based on fractional order FOUKF‐FOMIUKF algorithm with multiple time scales

AbstractAiming at the matter of poor precision in predicting the charge of lithium battery by applying conventional integer‐order models and offline parameter identification, this paper proposes a joint fractional‐order multi‐innovations unscented Kalman filter (FOUKF‐FOMIUKF) algorithm for predicting the cells' state of charge (SOC) online and uses the theory of singular‐value decomposition to tackle the issue of failure of the traceless transformation. Initially, the circuitry model of fractional order is built. The parameters of the model are recognized online by fractional‐order unscented Kalman filtering (FOUKF), and the obtained parameters are then transmitted to the method known as the fractional order multi‐innovations unscented Kalman filter (FOMIUKF) to calculate the SOC of the cell. The algorithm was validated under four working conditions such as FUDS (US Federal Urban Driving Distance), BJDST (Beijing Dynamic Stress Test), DST (Dynamic Stress Test), and US06 (Highway Driving Distance Test), respectively, and compared with the FOMIUKF, MIUKF, and FOUKF algorithms for offline identification. The conclusions demonstrate that the SOC estimated by the FOUKF‐FOMIUKF method is controlled within 0.5% of the mean absolute error under the four conditions and the root‐mean‐square error is controlled within 0.8%. It is not difficult to find that the FOUKF‐FOMIUKF algorithm estimates SOC with higher accuracy and robustness.

SOC estimation of lithium battery based on the combination of electrical parameters and FBG non-electrical parameters and using NGO-BP model

Accurately estimating of the state of charge (SOC) of lithium batteries is of great significance for improving the service life and utilization efficiency of lithium batteries. In this paper, fiber Bragg grating (FBG) sensors are used to capture the offset of the FBG central wavelength caused by strain and temperature during the charging and discharging process of lithium batteries, and the current state of charge of lithium batteries is obtained through the combination of electrical parameters and FBG non-electrical parameters.A battery monitoring experimental system was established, and the comparison of wavelength offset collected by PDMS packaged FBG and bare FBG during the charging and discharging process of lithium batteries was discussed. The prediction results were compared by temperature compensating the central wavelength offset collected during the charging and discharging of lithium batteries, then introduced BP neural network and optimized NGO- BP neural network regression models for training to compare the prediction effects, the results of which point to the fact that in terms of regression model performance and improvement of FBG non-electrical parameters on model performance, the various indicators predicted by NGO-BP are stronger than those predicted by BP. After adding FBG non-electrical parameters, both the BP regression model and the NGO-BP regression model showed remarkable improvement in RMSE, MAE, and R2 compared to the purely electrical parameters, and the best prediction method for the NGO-BP non-purely electrical parameters, with RMSE of 0.0201 and MAE of 0.0154, which decreased by 77.94% and 73.12% respectively compared to the BP pure electrical parameter prediction method, and R2 of 0.9988, which increased by 5.88%, thus the joint accurate estimation of lithium battery SOC by electrical measurement characteristics and FBG non-electrical parameters is basically realized, which brings values for the design and development of battery management system.

Hybrid Estimation Method for the State of Charge of Lithium Batteries Using a Temporal Convolutional Network and XGBoost

Lithium batteries have recently attracted significant attention as highly promising energy storage devices within the secondary battery industry. However, it is important to note that they may pose safety risks, including the potential for explosions during use. Therefore, achieving stable and safe utilization of these batteries necessitates accurate state-of-charge (SOC) estimation. In this study, we propose a hybrid model combining temporal convolutional network (TCN) and eXtreme gradient boosting (XGBoost) to investigate the nonlinear and evolving characteristics of batteries. The primary goal is to enhance SOC estimation performance by leveraging TCN’s long-effective memory capabilities and XGBoost’s robust generalization abilities. We conducted experiments using datasets from NASA, Oxford, and a vehicle simulator to validate the model’s performance. Additionally, we compared the performance of our model with that of a multilayer neural network, long short-term memory, gated recurrent unit, XGBoost, and TCN. The experimental results confirm that our proposed TCN–XGBoost hybrid model outperforms the other models in SOC estimation across all datasets.

Analysis and detection of charge and discharge characteristics of lithium battery based on multi-sensor fusion

Analysis and detection of charge and discharge characteristics of lithium battery based on multi-sensor fusion

A Variable-Mode Bidirectional Isolated Power Converter for Wide Voltage-Transfer Ratio Applications

Lithium battery charging require a wide voltage gain range. Due to the limited voltage gain range of LLC converter, it is limited in battery storage application scenarios. In this paper, A interleaved parallel LLC converter is proposed to realize the charging and discharging processes of bidirectional battery energy storage systems. The converter extended range of voltage gain required for battery recovery area by mode switching control. For the interleaved parallel mode power equalization problem, a gain compensation control strategy is proposed to solve the power equalization problem of the converter in this mode. This results in more uniform thermal and current stresses on the charger, as well as smaller input and output ripple. Moreover, with the proposed variable-mode control strategy, the efficiency of the charging process remains above 90%. A 1.5-kW experimental platform was developed to validate the performance of the proposed converter.

Co-estimation of state of charge and internal temperature of pouch lithium battery based on multi-parameter time-varying electrothermal coupling model

Accurate estimation of battery state of charge (SOC) and internal temperature can improve battery performance and safety. In this process, the accuracy of the battery's electrical model and thermal model and the applicability and robustness of the estimation algorithm are key. In this paper, a multi-parameter time-varying electrothermal coupling model for pouch batteries is established. The model takes into account the coupling relationship between the battery SOC and temperature changes, as well as the heat exchange between multiple surfaces of the battery and the external environment. And then through the improved hybrid pulse power characterization (HPPC) test to realize the identification of model parameters, and the changing external heat transfer conditions could be identified online. At last, an adaptive strong tracking square root cubature Kalman filter (AST-SRCKF) algorithm was designed to estimate SOC and internal temperature simultaneously. The results were verified by three dynamic conditions. The conclusion is that the AST-SRCKF has high robustness and can be quickly corrected when the initial values of SOC and temperature are unknown. And the estimation accuracy is higher than extended Kalman filter (EKF) and dual Kalman filter (DEKF). Furthermore, the simulation efficiency is improved by more than 6 % compared with EKF, and by more than 11 % compared with DEKF, which is more conducive to online real vehicle applications.

State-of-Charge and State-of-Health Joint Estimation of Lithium-Ion Battery Based on Iterative Unscented Kalman Particle Filtering Algorithm With Fused Rauch–Tung–Striebel Smoothing Structure

Abstract Traditional particle filtering has a large estimation error in the state of charge and Lithium-ion battery health of electric vehicle lithium batteries. For the above-mentioned problems, the lithium battery second-order resistance capacitance (RC) equivalent circuit model is established, and then, the model parameters are identified using the multi-innovation least square algorithm (MILS). Finally, an iterative unscented Kalman particle filtering algorithm with fused Rauch–Tung–Striebel Smoothing Structure (RTS-IUPF) applied to Li-ion battery state-of-charge (SOC) and state-of-health (SOH) joint estimation is proposed. The algorithm is based on the identification of battery parameters; the controller reads the sensor data and predicts the state results. RTS smoothing structure can do posterior estimation, and a significant probability density function is generated to select the optimal particle, and unscented Kalman algorithm regularized particles. The algorithm reduces the effect of the process noise covariance matrix and the measured noise covariance matrix on the filter accuracy and response time in traditional unselected Kalman filters. The algorithm proposed in the paper improves particle degradation and increases the estimation accuracy. Finally, the RTS-IUPF algorithm performs simulation analysis in Pulse current discharge condition and dynamic current condition (NEDC), respectively. The pulse current experimental results show that the mean absolute value error of UKF and particle filter (PF (number of particles N is 300)) is 1.26% and 1.24%, respectively, while the error of the RTS-IUPF is 0.748%. The root-mean-square error (RMSE) of the RTS-IUPF is reduced by 66.5% and 77.8% compared with UKF and PF. Furthermore, the error of joint estimation using this algorithm is smaller than that of single estimation. The RMSE of the RTS-IUPF joint is reduced by 27.4% compared with RTS-IUPF. The feasibility and effectiveness of the algorithm for the joint estimation of SOC and SOH of lithium batteries were verified.

SOC Estimation of Li-ion Battery using on Variational Mode Decomposition and Transformer-Generative Adversarial Network

Accurate estimation of the state of charge (SOC) of lithium battery is crucial to improve the dynamic performance and energy utilization of batteries. The method, the existing neural network are used to estimate SOC, has the problems of low accuracy and poor stability under complex working conditions. A new algorithm are proposed to estimate the SOC, which combines Transformer and Generative Adversarial Network (GAN), and the Variational Modal Decomposition (VMD). Firstly, as the excellent prediction ability of Transformer, Transformer is used as the generative network of GAN. Secondly, VMD is used to decompose the SOC historical data into six subsets to increase the input features. Finally, DST work data from the University of Maryland CALCE dataset is used for model training, and the VMD-Transformer-GAN algorithm is compared with LSTM, GRU, and BiLSTM algorithms for experiments. The experimental results show that the VMD-Transformer-GAN algorithm algorithmic estimation model has high stability and accuracy, which verifies the feasibility of the improved scheme.

Deep Learning in the State of Charge Estimation for Li-Ion Batteries of Electric Vehicles: A Review

As one of the critical state parameters of the battery management system, the state of charge (SOC) of lithium batteries can provide an essential reference for battery safety management, charge/discharge control, and the energy management of electric vehicles (EVs). To analyze the application of deep learning in electric vehicles’ power battery SOC estimation, this study reviewed the technical process, common public datasets, and the neural networks used, as well as the structural characteristics and advantages and disadvantages of lithium battery SOC estimation in deep learning methods. First, the specific technical processes of the deep learning method for SOC estimation were analyzed, including data collection, data preprocessing, feature engineering, model training, and model evaluation. Second, the current commonly and publicly used lithium battery dataset was summarized. Then, the input variables, data sets, errors, and advantages and disadvantages of three types of deep learning methods were obtained using the structure of the neural network used for training as the classification criterion; further, the selection of the deep learning structure for SOC estimation was discussed. Finally, the challenges and future development directions of lithium battery SOC estimation using the deep learning method were explained. Over all, this review provides insights into deep learning for EVs’ Li-ion battery SOC estimation in the future.

Analysis of the Influence of Slope Sliding on the Stability of Underground Diaphragm Wall Bridge Foundation Based on Wireless Sensor Network

Real-time monitoring, condition assessment, early warning processing, and damage identification of underground diaphragm wall bridge structures are the current research trends. Based on the wireless sensor network theory, this paper constructs the stability model of the slope sliding on the underground diaphragm wall bridge foundation. The model builds a complete set of underground diaphragm wall bridge vibration signal acquisition and monitoring platform through wireless sensor network node data acquisition and solves the problem of data accuracy measurement by using theoretical analysis, software and hardware design, software simulation, and experimental verification methods. During the simulation process, experiments were designed such as slope sliding vibration signal acquisition data accuracy test, sensor node wireless charging power test and modeling, lithium battery charging power test and modeling, wireless rechargeable sensor node system work test, and other experiments. The experimental results show that the accuracy of the collected vibration signals of the underground diaphragm wall bridge is high, the main working frequency bands are 868 MHz, 915 MHz, and 2.4 GHz, the maximum data transmission rate is 250 Kbps, and the communication distance reaches 100 m, which can meet the requirements of the underground diaphragm wall. The power of wireless charging of lithium batteries reaches 4.5 mW, which effectively improves the stability measurement accuracy of underground diaphragm wall bridge foundations.

A novel high-performance two poles and two zeros digital compensation control strategy for electric vehicle lithium battery charging systems

This study proposes a 1 kW power converter of the lithium battery charging system for electric vehicles. Its proposed architecture comprises an interleaved boost power factor corrector (PFC) converter and a full-bridge phase-shift (FBPS) converter with a new two poles and two zeros (TPTZ) digital compensation function. Unlike the traditional one pole and one zero compensation control, the proposed TPTZ compensation control considers both the output voltage and the output current feedback, effectively controlling the gain bandwidth fc and phase margin PM and improving system stability and performance. The interleaved boost PFC converter can adjust the power factor close to 1.0. On the other hand, the FBPS converter adopts an average switching model and the duty cycle loss factor to derive the small-signal model of the full-bridge phase-shift converter. The model can obtain the transfer function of the output voltage and current to the duty cycle. The corresponding digital compensation control loop was designed and applied to the converters using the derived transfer function. Further, the charge system's stability with MATLAB simulation was verified. Finally, the proposed architecture with the proposed TPTZ control method was compared with the traditional battery charger architecture, in which the PFC converter was connected with an FBPS converter in series. The study found that the proposed battery charging system performs better than the traditional system under the test conditions (25 %, 50 %, and 100 % load). In addition, the proposed TPTZ method has 95 % and 96 % efficiency when charging with constant voltage mode and constant current mode at 100 % load, respectively. Furthermore, this study proposes a battery charging system with a TPTZ digital compensation function that not only improves system performance but also reduces circuit size, suitable for charging lithium batteries in electric vehicles.