Charge Estimation Of Lithium-ion Batteries Research Articles

State of Charge Estimation for Lithium-Ion Batteries Using Optimized Model Based on Optimal HPPC Conditions Created Using Taguchi Method and Multi-Objective Optimization

This study proposes a comprehensive methodology for accurate State of Charge (SOC) estimation in lithium-ion batteries by optimizing equivalent circuit model (ECM) parameters under varying temperature conditions using the Taguchi method. Analysis of Variance (ANOVA) was employed to evaluate the influence of these parameters on ECM accuracy. Experiments were conducted at −10 °C, 25 °C, and 40 °C to evaluate the effects of pulse time gap, discharge pulse time, and C-rate on SOC estimation accuracy. A genetic algorithm-based multi-objective optimization technique was employed to minimize RMSE in the extended Kalman filter (EKF) SOC estimation process. The results showed that temperature significantly impacts SOC prediction, with deviations most pronounced at low (−10 °C) and high (40 °C) temperatures. When assessments are conducted for different SOC levels (SOC90, SOC50, SOC30), the key results highlight the substantial influence of pulse time gap and discharge pulse time on model accuracy. Also, it was observed that there is a significant reduction in RMSE, indicating improved performance under optimized conditions. The findings are particularly relevant for real-time applications, such as electric vehicles, where accurate SOC estimation is crucial for battery management.

Self-attention network-based state of charge estimation for lithium-ion batteries with gapped temperature data

The accurate estimation of the state of charge (SOC), a critical indicator of the energy stored in lithium-ion batteries, is essential for ensuring reliable and safe battery management. The influence of temperature on the battery characteristics substantially affects the SOC estimation accuracy. Owing to the broad operational temperature range of batteries, it is vital to address the various temperature conditions. This study proposes a model structure for data-driven SOC estimation to enhance accuracy under diverse temperature conditions. The model leverages the analysis of the SOC characteristics derived from the measured data. The proposed structure incorporates parallel-connected self-attention and long-short-term memory modules, thus providing an innovative approach for effectively capturing intricate features in SOC estimation. This study primarily focused on evaluating the capability of the proposed model to achieve satisfactory SOC estimation for untrained temperature conditions when trained with gapped temperature data, thus emphasizing its practicality. To assess the feasibility of the proposed method, experiments were performed under a broad range of fixed and varying temperature conditions, including seasonal and daily changes. The experimental results demonstrated that the root-mean-square errors of the estimated SOC were 0.4101% and 1.5611% at fixed and time-varying temperatures, respectively, including the subzero ranges. These results highlight the robustness of the proposed model under various temperature conditions and its applicability to real-world battery operational temperatures.

State of Charge Estimation of Lithium-Ion Batteries for Electric Vehicle Application Using Gaussian Process Regression Approach

For the purpose of ensuring a secure, dependable and affordable performancealong with clean energy in electric vehicles, the estimation of the precise state of charge of LIB is very important. In this article, Gaussian Process Regression with different kernel functions-based SOC prediction is proposed and their performance with good health and well-beingare evaluated and analyzed. A useful benefit of employing GPR is the ability to quantify and estimate uncertainties, allowing for the evaluation of the SOC estimate's dependability. The kernel function serves as a crucial hyperparameter that improves GPR performance. GPR considers the temperature and voltage of the battery, which are independent of one another, as their respective input parametersthat relates Industry, innovation and infrastructure where target-dependent variable is battery SOC. Initially, the training process involves determining the ideal hyperparameters of a kernel function to accurately represent the characteristics of the data. The accuracy of predicting SOC of the battery is evaluated using test data. According to the simulation outcomes, the squared exponential kernel function-based GPR estimates SOC with high accuracy and lower RMSE and MAE which ensures energy efficiency and quality education.

Adaptive optimal observer for real-time state of charge estimation of lithium-ion batteries in robotic systems

PurposeThis study aims to address the challenge of the real-time state of charge (SOC) estimation for lithium-ion batteries in robotic systems, which is critical for monitoring remaining battery power, planning task execution, conserving energy and extending battery lifespan.Design/methodology/approachThe authors introduced an optimal observer based on adaptive dynamic programming for online SOC estimation, leveraging a second-order resistor–capacitor model for the battery. The model parameters were determined by fitting an exponential function to the voltage response from pulse current discharges, and the observer's effectiveness was verified through extensive experimentation.FindingsThe proposed optimal observer demonstrated significant improvements in SOC estimation accuracy, robustness and real-time performance, outperforming traditional methods by minimizing estimation errors and eliminating the need for iterative steps in the adaptive critic and actor updates.Originality/valueThis study contributes a novel approach to SOC estimation using an optimal observer that optimizes the observer design by minimizing estimation errors. This method enhances the robustness of SOC estimation against observation errors and uncertainties in battery behavior, representing a significant advancement in battery management technology for robotic applications.

A generic fusion framework integrating deep learning and Kalman filter for state of charge estimation of lithium-ion batteries: Analysis and comparison

Lithium-ion batteries (LIBs) are extensively utilized in power and energy storage systems. Accurately estimating state of charge (SOC) of LIBs using single methods is still challenging to fully address the problem associated with SOC. This paper proposes a novel generic fusion framework that integrates deep learning (DL) and Kalman filter (KF), characterized by an effective fusion of the results obtained by multiple-methods. Within the DL-based method, a spatial-temporal awareness model and an improved dung beetle optimizer are proposed to enhance the capture of spatial-temporal features and optimize hyperparameters. Additionally, three adaptive nonlinear KFs are employed for SOC estimation based on a simplified electrochemical model, with the adaptive cubature KF emerging as the most suitable following comparative analysis. Subsequently, the results from both approaches are integrated to derive the final result using various fusion methodologies. The effectiveness of the fusion framework is validated using experimental data, with kernel ridge regression emerging as the optimal fusion method. The best fusion results yield a mean absolute error of 0.2341% and a root mean square error of 0.3072%, demonstrating that proposed framework can effectively process and utilize multiple-source information, thereby enhancing accuracy and robustness compared to single methods and exhibiting adaptability to complex situations.

An improved multi-innovation error compensation-long-short-term memory network modeling method for high-precision state of charge estimation of lithium-ion batteries

An improved multi-innovation error compensation-long-short-term memory network modeling method for high-precision state of charge estimation of lithium-ion batteries

State of charge estimation for lithium-ion batteries based on gate recurrent unit and unscented Kalman filtering

State of charge estimation for lithium-ion batteries based on gate recurrent unit and unscented Kalman filtering

A Quantum Particle Swarm Optimization Extended Kalman Quantum Particle Filter approach on state of charge estimation for lithium-ion battery

The accurate estimation of battery state of charge serves as the foundation for estimating state of health, remaining useful life, and safety, making it an indispensable part of battery management system. Based on the lithium-ion battery parameter identification using quantum particle swarm optimization, this paper presents a state of charge estimation method utilizing the Extended Kalman Quantum Particle Filter. A Quantum Particle Swarm Optimization algorithm is used for second-order equivalent circuit model parameter identification. By combining Extended Kalman Filter with Particle Filter and employing Grover search algorithm for resampling of quantized particles, the Extended Kalman Quantum Particle Filter is designed for estimation. Validation is conducted under standard operating conditions, and the Quantum Particle Swarm Optimization Extended Kalman Quantum Particle Filter algorithm demonstrates better accuracy and adaptability compared to Extended Kalman Filter, Extended Kalman Particle Filter and other popular algorithms under FUDS, UDDS and DST working conditions by two types of battery. Both theoretical and experimental results indicate good performance of Quantum Particle Swarm Optimization Extended Kalman Quantum Particle Filter in state of charge estimation.

State of Charge Estimation of Lithium-Ion Batteries Based on Fractional-Order Model with Mul-ti-Innovations Dual Cubature Kalman Filter Method

An accurate estimation of the lithium battery’s state of charge (SOC) is critical. The article proposes a dual fractional order multi-innovations cubature Kalman filter (DFOMICKF) algorithm for estimating lithium battery SOC. The algorithm adopts the idea of multiple time scales, where one of the FOMICKF is used to identify the circuit model parameters online in the macro time scale. Another FOMICKF is used to estimate the SOC in the micro time scale, and the circuit parameters updated online in real-time are passed into the estimation of the SOC filter to form an online joint estimation method of SOC and circuit parameters. Finally, multiple algorithms of DFOMICKF, FOMICKF, FOCKF, and CKF are compared and experimented under different working conditions to compare and analyze the estimated SOC errors. It is verified that the proposed algorithm can solve the problems of inaccuracy, poor convergence, and poor robustness of the traditional Kalman filtering algorithm for estimating SOC, which has good research value.

State of charge estimation for lithium-ion batteries with pre-set convergence time based on a comprehensive unobservable model

State of charge (SOC) is a critical metric for assessing the remaining capacity of lithium-ion batteries, while convergence time is an essential indicator for evaluating the performance of SOC estimation. To reduce the convergence time of SOC estimation, a novel strategy has been proposed. Initially, an accurate and comprehensive model is employed for lithium-ion batteries, which includes the nonlinear behavior of the open circuit voltage (OCV) with respect to SOC. However, the linear component of this model is unobservable. Then, the nonlinear relationship between OCV and SOC is approximated using a first-order piecewise fitting function. This approach ensures accuracy while reducing computational complexity compared to non-linearization methods. Next, the unobservable model is decomposed into observable and unobservable subsystems, addressing the challenge of designing observers for unobservable models. Furthermore, a finite time observer is designed based on the observable subsystem, enabling SOC estimation to be achieved within any pre-set convergence time. Finally, theoretical analysis and extensive experimental results validate the feasibility and reliability of the proposed estimation strategy, demonstrating its superiority over traditional methods.

A hybrid model for state of charge estimation of lithium-ion batteries utilizing improved adaptive extended Kalman filter and long short-term memory neural network

With lithium-ion batteries being utilized in all aspects of life, accurately estimating the state of charge (SOC) of a battery has become a key issue in battery management systems. In this paper, an improved hybrid model based on adaptive extended Kalman filter (AEKF) and improved long short-term memory (ILSTM) neural network is proposed. The proposed model is based on a second-order RC equivalent circuit model, and the dynamic forgetting factor recursive least squares (DFFRLS) and AEKF algorithms are utilized to obtain the initial SOC estimates. And the estimation error in the AEKF algorithm due to neglecting the higher order terms of the Taylor expansion equations is compensated by the improvement of the LSTM network. The results under different working conditions indicate that the SOC estimation of the hybrid model has good convergence and high system robustness. The maximum error (MAX) of this algorithm is less than 2.3 %, especially the root mean square error (RMSE) and mean absolute error (MAE) are less than 0.84 % and 0.65 %, respectively.

A Physics-Based Equivalent Circuit Model and State of Charge Estimation for Lithium-Ion Batteries

A Physics-Based Equivalent Circuit Model and State of Charge Estimation for Lithium-Ion Batteries

Efficient state of charge estimation of lithium-ion batteries in electric vehicles using evolutionary intelligence-assisted GLA–CNN–Bi-LSTM deep learning model

The battery's performance heavily influences the safety, dependability, and operational efficiency of electric vehicles (EVs). This paper introduces an innovative hybrid deep learning architecture that dramatically enhances the estimation of the state of charge (SoC) of lithium-ion (Li-ion) batteries, crucial for efficient EV operation. Our model uniquely integrates a convolutional neural network (CNN) with bidirectional long short-term memory (Bi-LSTM), optimized through evolutionary intelligence, enabling an advanced level of precision in SoC estimation. A novel aspect of this work is the application of the Group Learning Algorithm (GLA) to tune the hyperparameters of the CNN–Bi-LSTM network meticulously. This approach not only refines the model's accuracy but also significantly enhances its efficiency by optimizing each parameter to best capture and integrate both spatial and temporal information from the battery data. This is in stark contrast to conventional models that typically focus on either spatial or temporal data, but not both effectively. The model's robustness is further demonstrated through its training across six diverse datasets that represent a range of EV discharge profiles, including the Highway Fuel Economy Test (HWFET), the US06 test, the Beijing Dynamic Stress Test (BJDST), the dynamic stress test (DST), the federal urban driving schedule (FUDS), and the urban development driving schedule (UDDS). These tests are crucial for ensuring that the model can perform under various real-world conditions. Experimentally, our hybrid model not only surpasses the performance of existing LSTM and CNN frameworks in tracking SoC estimation but also achieves an impressively quick convergence to true SoC values, maintaining an average root mean square error (RMSE) of less than 1 %. Furthermore, the experimental outcomes suggest that this new deep learning methodology outstrips conventional approaches in both convergence speed and estimation accuracy, thus promising to significantly enhance battery life and overall EV efficiency.

Improved adaptive feedback particle swarm optimization-multi-innovation singular decomposition unscented Kalman filtering for high accurate state of charge estimation of lithium-ion batteries in energy storage systems

Improved adaptive feedback particle swarm optimization-multi-innovation singular decomposition unscented Kalman filtering for high accurate state of charge estimation of lithium-ion batteries in energy storage systems

State of Charge Estimation of Lithium-ion Batteries Based on Online OCV Curve Construction

The open-circuit voltage (OCV) curve has a significant influence on the accuracy of the state of charge (SOC) estimation based on equivalent circuit models (ECMs). However, OCV curves are tested through offline experiments and are hard to be very accurate because they constantly change with the test method’s ambient temperature and aging status. Recently, researchers have attempted to improve the accuracy of OCV curves by increasing the volume of sample data or updating/reconstructing the curve combined with practical operation data. Still, prior offline tests are essential, and experimental errors inevitably exist. Consequently, a SOC estimation method without any offline OCV tests might be an efficient route to improve the accuracy of SOC. According to this idea, this paper presents a novel method for SOC estimation, which is based on online OCV curve construction. Meanwhile, a stepwise multi-timescale parameter identification algorithm is designed to improve the interpretability and precision of the estimated ECM parameters. The results demonstrate that the maximum SOC estimation error is only 0.05% at 25 °C, indicating good robustness under various ambient temperatures and operational conditions.

An improved log-cosine variation slime mold - simplified gated recurrent neural network for the high-precision state of charge estimation of lithium-ion batteries

Accurate estimation of SOC is crucial for the safe and efficient utilization of electric vehicles, portable devices and renewable energy systems. Neural networks have become a powerful tool in the field of SOC estimation, but they have a complex structure, difficult parameter tuning, and high performance requirements. For this reason, this paper proposes a simplified gated recurrent neural network (SGRN) that greatly simplifies the unitary network structure and parameters. A Nadam gradient optimizer is introduced to update the network weight parameters, and the network hyperparameters are optimized using the log-cosine variant slime mold algorithm (LVSM). The algorithm is validated under HPPC, BBDST, and DST operating conditions under 25 °C, 10 °C, and − 5 °C. The results show that the LVSM-SGRN algorithm has higher accuracy, lower performance consumption, and better temperature adaptability than the conventional LSTM algorithm as well as the EKF algorithm. This contributes to the integration and large-scale industrialization of the algorithm, and comprehensively improves the safety of lithium-ion batteries and accuracy of SOC estimation.

Self-Adaptive Neural Network-Based Fractional-Order Nonlinear Observer Design for State of Charge Estimation of Lithium-Ion Batteries

Self-Adaptive Neural Network-Based Fractional-Order Nonlinear Observer Design for State of Charge Estimation of Lithium-Ion Batteries

A novel hybrid optimized incremental relevance vector machine and filtering technique for state of charge estimation of lithium-ion batteries

Despite not requiring a comprehensive understanding of the intricate electrochemical reactions within the battery, the data-driven approach faces challenges regarding state of charge (SOC) fluctuations during abrupt changes in battery current. Currently, research on data-driven filtering methods predominantly focuses on neural network-based algorithms, with limited exploration of the relevance vector machine (RVM) algorithm that possesses advantages in handling small sample sizes, high sparsity, and probabilistic distributions. Drawing inspiration from this premise, the present manuscript introduces an innovative fusion algorithm, founded upon the framework of the adaptive Kalman filtering (AKF) algorithm, which intricately melds the optimized incremental RVM (OIRVM) with the Coulomb counting methodology, called Coulomb_AKF_OIRVM (CFR) algorithm. Experimental findings suggest that the CFR algorithm displays exceptional generalization capacity across different driving cycles and temperatures, exhibiting high estimation accuracy and robustness. The average root mean square error, determination coefficient, mean absolute error and maximum absolute error of the public datasets and experimental datasets are 0.25 %, 0.02 %, 0.20 %, and 0.85 %, respectively.

Analysis and Diagnosis of the Effect of Voltage and Current Sensor Faults on the State of Charge Estimation of Lithium-ion Batteries Based on Neural Networks

Analysis and Diagnosis of the Effect of Voltage and Current Sensor Faults on the State of Charge Estimation of Lithium-ion Batteries Based on Neural Networks

A novel suppressing Kalman filter divergence method for the state of charge estimation of lithium-ion batteries under complex conditions

The estimation method for state of charge (SOC) of lithium-ion batteries based on the Extended Kalman Filter (EKF) is prone to losing the ability to track sudden changes under complex driving conditions. Thus, this method cannot guarantee estimation accuracy and robustness. To overcome this deficiency, fading factors are introduced into the error covariance to enhance the effect of newly generated measurement data on the current filtering estimation. A Suboptimal Multiple Fading EKF (SMFEKF) SOC estimation method is proposed herein. The fading factors are solved by forcing the new error sequence of the predicted and actual terminal voltage values to remain orthogonal. The new error sequence that remains orthogonal undergoes transitions from strong autocorrelation in the original stable state to weak autocorrelation. Through real-time adjustment of the Kalman gain matrix, the effective tracking ability of the system in the event of sudden changes gets enhanced. To verify the effectiveness of the proposed method, first, a battery dynamic condition test library covering multiple driving scenarios (such as cities, suburbs, and highways) is established through ADVISOR advanced vehicle simulator driving condition tests. Second, a second-order equivalent circuit model is established and the recursive least squares method is used for parameter identification. Finally, a detailed comparison is made between SMFEKF and EKF in terms of SOC estimation accuracy and robustness under driving conditions with different levels of complexity. It is found that four operating conditions are the main reasons for filtering divergence in EKF. The results show that the root mean square errors of SMFEKF are 63.72 %, 54.20 %, and 21.30 % those of EKF under the three complex conditions, respectively. The new method effectively reduces SOC estimation error and solves the problem of EKF filtering divergence under complex conditions.