State Of Charge Estimation Research Articles

Support vector regression-based state of charge estimation for batteries: cloud vs non-cloud

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.

Short Term Charging Data Based Battery State of Health and State of Charge Estimation Using Feature Pyramid

Short Term Charging Data Based Battery State of Health and State of Charge Estimation Using Feature Pyramid

Estimation of SOC in Lithium-Iron-Phosphate Batteries Using an Adaptive Sliding Mode Observer with Simplified Hysteresis Model during Electric Vehicle Duty Cycles

This paper develops a model for lithium-ion batteries under dynamic stress testing (DST) and federal urban driving schedule (FUDS) conditions that incorporates associated hysteresis characteristics of 18650-format lithium iron-phosphate batteries. Additionally, it introduces the adaptive sliding mode observer algorithm (ASMO) to achieve robust and swiftly accurate estimation of the state of charge (SOC) of lithium-iron-phosphate batteries during electric vehicle duty cycles. The established simplified hysteresis model in this paper significantly enhances the fitting accuracy during charging and discharging processes, compensating for voltage deviations induced by hysteresis characteristics. The SOC estimation, even in the face of model parameter changes under complex working conditions during electric vehicle duty cycles, maintains high robustness by capitalizing on the easy convergence and parameter insensitivity of ASMO. Lastly, experiments conducted under different temperatures and FUDS and DST conditions validate that the SOC estimation of lithium-iron-phosphate batteries, based on the adaptive sliding-mode observer and the simplified hysteresis model, exhibits enhanced robustness and faster convergence under complex working conditions and temperature variations during electric vehicle duty cycles.

Estimating the Optimal State of Charge for Electric Car Batteries Using an Extended Kalman Filter

The efficient management of battery state of charge (SOC) is crucial for maximizing the performance, range, and longevity of electric vehicle (EV) batteries. This paper presents a novel approach for estimating the optimal state of charge of electric car batteries using an Extended Kalman Filter (EKF). The EKF is a recursive algorithm that combines measurements from various sensors with a dynamic battery model to estimate the current SOC and predict future SOC values with high accuracy. The paper provides a detailed explanation of the EKF algorithm and its application to battery SOC estimation, highlighting its ability to handle nonlinearities, uncertainties, and measurement noise inherent in battery systems. Furthermore, this research presents a simulation-based validation of the proposed EKF approach using real-world driving data from electric vehicles. The simulation results demonstrate the effectiveness of the EKF algorithm in accurately estimating the SOC of electric car batteries under various operating conditions, including different driving patterns, temperatures, and battery degradation scenarios.

State of Charge Estimation for Lithium Battery Based on Fractional Order Square Root Cubature Kalman Filter and Adaptive Multi-innovation Unscented Kalman Filter

Accurate state of charge (SOC) estimation of batteries is of great significance for electric vehicles. A SOC estimation method based on a fractional order square root cubature Kalman filter (FOSRCKF) and an adaptive multi-innovation unscented Kalman filter (AMIUKF) is proposed. The battery is modelled using fractional order calculus theory and the model parameters are identified by adaptive genetic algorithm. The FOSRCKF estimates the battery SOC, while the AMIUKF online updates the internal resistance in the model, and there exchanges information between two filters. The experimental results under the Urban Dynamometer Driving Schedule (UDDS) and the US06 Highway Driving Schedule show that the proposed method has lower SOC estimation error and lower terminal prediction error compared with the traditional SRCKF method based on integer order models, which demonstrates the effectiveness, accuracy and robustness of the proposed method.

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.

Multi-interest adaptive unscented Kalman filter based on improved matrix decomposition methods for lithium-ion battery state of charge estimation

Accurately predicting the state of charge (SOC) is crucial to improving Li-ion battery performance. However, available model-based estimation approaches still face challenges in handling model uncertainty and measurement noise effects on parameter identification. Besides, the widely used unscented Kalman filter (UKF) algorithm has limitations in electric vehicles as it requires the error covariance matrix to maintain positive definiteness, limiting its applicability under certain conditions. This study introduces the bias-compensated forgetting factor recursive least squares (BCFFRLS) method for parameter estimation within the second-order RC equivalent circuit model specific to the INR18650-20R battery. Furthermore, we propose a novel algorithm named the optimization multi-interest adaptive unscented Kalman filter (O-MIAUKF). This algorithm is designed to address stability and robustness issues with traditional UKF encounters in dynamic environments. Experimental validation demonstrates that the O-MIAUKF algorithm excels in maintaining strong stability and robustness in various working conditions, accurately estimating SOC even with a non-positive covariance matrix. The SOC estimation error remains stable at 0.8 %, which is lower than that of the current Extended Kalman Filter (EKF), UKF, and Dual Extended Kalman Filter (DEKF).

Study on the estimation of the state of charge of lithium-ion battery

Lithium-ion battery (LIB) occupies a major position in rechargeable batteries. However, if the state of charge (SOC) estimation of LIB is not accurate, it will lead to the decline of battery life. SOC is often estimated by the extended Kalman filter (EKF) algorithm, but this method depends on the accuracy of model parameters. The sampling point interval time can affect the accuracy of SOC estimation. In this paper, the first-order RC model parameters are identified by two methods and the SOC is estimated by the EKF algorithm. Using this method, the battery parameters are received more accurate and the SOC estimation error is smaller. When the sampling interval is 0.03 s, the SOC estimation error is the smallest. And the second-order RC model can receive the same result. When the sampling interval is 0.03 s, the SOC estimation error of the 1RC-1 method is less than 1.4 %, the SOC estimation error of the 1RC-2 method is less than 0.017, and the root mean square error (RMSE) is 0.0087 and 0.01084. The conclusion is also used in the second-order RC model and contributes to the improvement of the SOC estimation accuracy. An effective method to estimate the battery SOC range is also proposed. This paper provides an effective method for obtaining more accurate SOC.

Multi-Cell-to-Multi-Cell active equalization method based on k-means clustering and battery pack SOC estimation

In this paper, a MC2MC (Multi-Cell-to-Multi-Cell) battery pack active equalization scheme based on reconfigurable circuits and Buck-Boost converters is proposed and validated for the problem of inconsistent single cell power in battery packs. This scheme can simultaneously equalize multiple non-adjacent cells in a single equalization process, improving the equalization time efficiency. Then, based on the relationship between duty cycle and voltage difference, the MC2MC battery pack active equalization control strategy based on adaptive duty cycle is designed. In addition, a k-means based clustering algorithm is used to cluster and reorganize batteries with different charges, and the SOC (State of charge) estimation algorithm of the battery pack is improved and introduced into the equalization control among multiple batteries. Finally, AC2AC (Any-Cell-to-any -Cell), MC2MC, and MC2MC simulations considering the SOC estimation of the battery pack are validated and analyzed for the unbalanced battery packs under MATLAB/Simulink environment, respectively. On the basis of verifying the accuracy of the simulation process, the effectiveness of the MC2MC equalization control method considering the SOC estimation of the battery pack is verified through the comparative analysis of four aspects, namely, equalization time, computational workload, resultant variance, and energy efficiency.

A correlated multi-observable assessment for vanadium redox flow battery state of charge estimation — Empirical correlations and temperature dependencies

The efficient operation of vanadium redox flow batteries requires the half cell specific monitoring of the state of charge (SOC). Monitoring of the SOC is affected by temperature fluctuations, which need to be distinguished from the SOC signal. Several SOC monitoring approaches have been proposed in the literature and either been evaluated individually or compared to one other approach. The aim of this study is to contribute to SOC sensor development for VRFB by providing a first correlated assessment of several established and new SOC monitoring methods. We perform multi observable measurements of: half cell electrolyte potentials, electrolyte densities, electrolyte volumes, electrolyte viscosity related pressure drops, pH related potentials and UV/Vis absorbances. We determine SOC correlations of these observables in full charge/discharge cycles at constant temperature and derive temperature corrections in additional measurements over a range of 12°C to 32°C at 25%, 50% and 75% SOC. We compare the SOC and temperature sensitivity as well as the accuracy of these monitoring methods at constant and varied temperature. We find that the established half cell electrolyte potentials and UV/Vis absorbances can estimate the SOC at constant temperature with measurement errors of 1.7% and 4%, respectively and with slightly higher errors at varying temperatures. The viscosity related pressure drop and electrolyte density are both suitable for SOC estimation if temperature corrections are included. The pH related potentials for the positive half cell and both half cell electrolyte volumes could be used for a rough SOC estimate, but need accurate temperature corrections and further long term stability evaluation.

State-of-charge estimation of lithium-ion battery: Joint long short-term memory network and adaptive extended Kalman filter online estimation algorithm

With the widespread use of lithium-ion batteries, estimating the State of Charge (SOC) has been one of the most critical tasks in the battery management system (BMS). In this paper, a joint long short-term memory recurrent neural network (LSTM-RNN) and adaptive extended Kalman filter (AEKF) algorithm is proposed to achieve accurate SOC estimation. The proposed algorithm generates an initial SOC estimation value through a trained LSTM-RNN with a sliding time window signal sequence as input. The initial estimation value is then used as a feedback input to the AEKF. The results show that only a 30-s time series as the input of the LSTM-RNN can achieve satisfactory estimation results. Compared with traditional adaptive extended Kalman filter and unscented Kalman filter (UKF), the proposed algorithm has a better performance of estimation accuracy under various discharging conditions, especially when applied at low temperatures. Finally, the SOC estimation results under four operating conditions and various temperatures show that the maximum error is less than 2.5 %, in particular, the root mean square error (RMSE) and the mean absolute error (MAE) are below 1.01 % and 0.64 %. The proposed algorithm is an effective SOC estimation method with good robustness and high estimation accuracy.

Second-Order Central Difference Particle Filter Algorithm for State of Charge Estimation in Lithium-Ion Batteries

The estimation of the state of charge (SOC) in lithium-ion batteries is a crucial aspect of battery management systems, serving as a key indicator of the remaining available capacity. However, the inherent process and measurement noises created during battery operation pose significant challenges to the accuracy of SOC estimation. These noises can lead to inaccuracies and uncertainties in assessing the battery’s condition, potentially affecting its overall performance and lifespan. To address this problem, we propose a second-order central difference particle filter (SCDPF) method. This method leverages the latest observation data to enhance the accuracy and noise adaptability of SOC estimation. By employing an improved importance density function, we generate optimized particles that better represent the battery’s dynamic behavior. To validate the effectiveness of our proposed algorithm, we conducted comprehensive comparisons at both 25 °C and 0 °C under the new European driving cycle condition. The results demonstrate that the SCDPF algorithm exhibits a high accuracy and rapid convergence speed, with a maximum error which never exceeds 1.30%. Additionally, we compared the SOC estimations with both Gaussian and non-Gaussian noise to assess the robustness of our proposed algorithm. Overall, this study presents a novel approach to enhancing SOC estimation in lithium-ion batteries, addressing the challenges posed by the process itself and measurement noises.

State of charge estimation of electrical vehicle battery by Using Box Particle Filter Signal Tracking Method

ABSTRACT This paper concentrates on the effective study of the online state of charge (SoC) estimation of lithium-ion batteries used in electric vehicles (EVs). Box Particle filters (BPF) are commonly used in the estimation of multivariate systems under noisy environments. In this study, a Joint-estimator is proposed which uses the forgetting factor recursive least square (FF-RLS) method for the 1-RC battery model online parameter estimation and the lithium-ion battery SoC is estimated using battery terminal voltage signal tracking with (BPF). The implemented method is compared with a Sequential Importance Resampling Particle Filter (SIR-PF) which shows that the proposed BPF has reduced the number of particles and computational time and maintained the efficiency comparable to the SIR-PF. The BPF performance is further evaluated at assorted voltage signal tracking accuracy, the number of boxes and the initial dimension of the boxes. The BPF method requires less number of boxes as compared to the SIR-PF with compatible efficiency. Therefore, BPF can be used as an alternative to the complex algorithms used in the SoC estimation in battery management systems (BMS) of electric vehicles.

A self-attention-based CNN-Bi-LSTM model for accurate state-of-charge estimation of lithium-ion batteries

In the quest for clean and efficient energy solutions, lithium-ion batteries have emerged at the forefront of technological innovation. Accurate state-of-charge (SOC) estimation across a broad temperature range is essential for extending battery longevity, and enduring effective management of overcharge and over-discharge conditions. However, prevailing challenges persist in achieving precise SOC estimates and generalizing across a wide temperature range, particularly at lower temperatures. Our comparative analysis reveals that, while a single-layer bidirectional LSTM model with a self-attention mechanism achieves remarkable SOC estimation accuracy at room temperature, the intricacies of SOC estimation at lower temperatures necessitate the incorporation of more hidden layers and more complex network architecture to capture intricate features influencing battery dynamics. Hence, we propose a deep learning model, based on convolutional neural networks integrating bidirectional long short-term memory and self-attention mechanism (CNN-Bi-LSTM-AM), specifically designed to tackle the challenges of achieving accurate SOC estimations across a wide temperature range. The proposed model demonstrates proficiency in capturing both spatial and temporal dependencies critical for lithium-ion battery SOC estimation. Furthermore, the integration of a self-attention mechanism enhances the model’s adeptness to discern pertinent features and patterns within the dataset, thereby improving its overall performance and robustness, even in sub-room temperature environments.

State of health estimation of lithium-ion battery using dual adaptive unscented Kalman filter and Coulomb counting approach

Several charging and discharging processes of lithium-ion batteries (LIBs) can lead to a battery fading and degradation effect. This may cause sudden faults, leakages, and explosions. As a result, it is highly important to estimate the state of health (SoH) of the battery to avoid any battery problems. This article proposes a novel hybrid dual adaptive unscented Kalman filter (DAUKF)-Coulomb counting approach (CCA) to efficiently state of charge (SoC) and SoH estimation of LIBs. The Gazelle optimization algorithm (GOA) is utilized in identifying LIB model parameters under various SoC conditions. It is used for minimizing the integral squared error between measured and estimated battery voltages. The battery model includes loading, fading, and various dynamic conditions. The GOA-based model has better results than other models by >8 %. The proposed hybrid DAUKF-CCA is compared with the dual adaptive extended Kalman filter (DAEKF)-CCA and other multiple algorithms. The fitness function consists of integral squared error between estimated and measured SoC of LIBs. The simulation results of the DAUKF-CCA are verified by a comparison with the measurement results performed using commercial Panasonic LiBs. The SoC results using the DAUKF-CCA are very close to the measurement results and the error is <1 %. The proposed DAUKF-CCA can ensure that the SoC and SoH estimation of LIBs is efficiently achieved.

The LiFePO4 battery lifespan SoC estimation using Ham-Informer and internal pressure

The goal of this study is to use the Ham-Informer to accurately and reliably estimate state of charge (SoC) throughout the LiFePO4 battery lifespan. First, Pearson heat map is used to study the correlation between different parameters and SoC, and found that the correlation between internal pressure is as high as 0.97. Hence， internal pressure is introduced to improve the accuracy of SoC estimation. The Informer model is refined using Hampel filter and Multi-head ProbeHamSparse to reduce the computational complexity and improve its feature extraction ability. The use of internal pressure and Ham-Informer can reduce the MAE (Mean Absolute Error) of the estimated SoC values of LFP batteries with light, mild, and severe degradation by 75 %, 74 %, and 86 %, respectively, compared to the absence of internal pressure. Compared with the unimproved Informer, the MAE of SoC estimates at different life stages decreased by 76 %, 68 %, and 84 %. The model exhibiting an average decrease of 89 % in MAE when compared to the LSTM model, and a 77 % decrease when compared to the GRU model. The incorporation of internal pressure and Ham-Informer can enhance the accuracy and reliability of battery lifespan SoC estimation. Compared with other methods, the computational complexity of Ham-Informer is smaller. Therefore, it can be applied to battery management systems, enabling accurate and dependable SoC estimation throughout the battery's lifespan.

An improved Cauchy robust correction-sage Husa extended Kalman filtering algorithm for high-precision SOC estimation of Lithium-ion batteries in new energy vehicles

The accurate estimation of battery State of Charge (SOC) is a key technology in the research of electric vehicle battery management systems. In order to solve the problem of inaccurate noise estimation in nonlinear systems, an improved Cauchy robust correction-Sage Husa extended Kalman filtering (CRC-SHEKF) algorithm is proposed for high-precision SOC estimation of lithium-ion batteries in new energy vehicles. Considering the polarization effect of the battery, the FFRLS algorithm is used for online parameter identification of the Dual Polarization model. Using robust data correction methods, the Cauchy robust function is simplified for real-time correction of the covariance matrix Q of system state noise and the covariance matrix R of the observed noise in the filtering process and combined with SHEKF for SOC estimation. The experimental results show that under different temperature conditions and complex working conditions, the proposed CRC-SHEKF algorithm has the minimum mean absolute error (MAE), root mean square error (RMSE), and maximum error (MAX). Under the condition of the Beijing bus dynamic stress test (BBDST) at 15 °C, the MAE, RMSE, and MAX of the CRC-SHEKF algorithm are 0.392 %, 0.716 %, and 0.945 %, with the computing time of only 4.839 s. The algorithm proposed in this article has high accuracy and robustness, and has practical application value, providing a reference for the application of lithium battery condition monitoring.

State-of-charge estimation for lithium-ion batteries based on modified unscented Kalman filter using improved parameter identification

The full and single hybrid pulse power characterization (HPPC) experiments are conducted on nickel cobalt manganese (NCM) and lithium iron phosphate (LFP) batteries to obtain the accurate relationship between state of charge (SOC) and open circuit voltage (OCV). The pseudo random number generated by the RAND function is used as the initial value of the parameter identification equation for the second-order equivalent circuit model (ECM), and the parameter identification of lithium-ion batteries is achieved through least squares fitting. The unscented Kalman filter (UKF) simulation model is modified by the first order low pass filtering (FOLPF) algorithm to improve the accuracy of SOC estimation of batteries. The results show that the goodness of fit between the real and estimated HPPC pulse voltage values at different SOC values is above 0.99 for both NCM and LFP batteries. Based on the parameter identification results, the maximum error of the HPPC voltage estimated by the second-order ECM is within 0.045 V under both full pulse and single pulse testing conditions while the maximum error of SOC estimation is within 0.025. The UKF model modified by the FOLPF algorithm provides a reference for the accurate SOC estimation of batteries.

Revealing the electrochemical-mechanical correspondence between electrode films and 20 Ah prismatic Li-ion batteries via optical fiber monitoring

Revealing the connections and corresponding relationships between changes in the electrodes and cell-level strain variation can enhance the accurate estimation of state of charge (SOC), state of health (SOH), cycling life, safety and other key factors of the large capacity prismatic batteries. By utilizing Fiber Bragg Grating (FBG) sensors and a “Zero-strain” lithium titanate (LTO) electrode, we directly monitored the strain behaviors of LiFePO4 (LFP) and graphite electrodes. These strain measurements were further connected to the non-monotonic strain variation observed in a 20 Ah LFP||Graphite prismatic cell monitored by surface-implanted FBGs, whicih confirms that the strain variation results from electrode deformation. Furthermore, we demonstrate the feasibility of accurately estimating SOC and SOH of the prismatic cell based on strain profiles, achieving an absolute error below 3% and a relative error below 10% for SOC estimation, as well as the ability to indicate capacity degradation method for SOH monitoring. These results encourage the utilization of FBG strain monitoring in actual engineering applications.

The Estimation of Battery State of Charge using Corny Network

State of charge (SOC) estimation of lithium-ion batteries has been extensively studied and the estimation accuracy was mainly investigated through the development of various battery models and dynamic estimation algorithms. All battery models, however, contain inherent model bias due to the simplifications and assumptions, which cannot be effectively addressed through the development of various conventional computation and intelligent computation. Consequently, some existing methods performed battery SOC estimation using conventional and intelligent computation have not very accurate to predict the SOC battery characteristics. There some drawbacks in employment deep learning to estimate SOC battery, such as complicated algorithm or network, over fitting and so on. The proposed method, the Corny architecture has narrow layers design. This design has low cost computation and prevent over fitting. The result shows the accuracy of method is very high. The predicted and targeted values are almost merged in a single line. The RMSE and MAX error indexes are very low. That the accuracy of the model is acceptable. The electric vehicle battery can estimate to life longer and more reliable to perform mobility task. Finally, this method also show the accuracy of estimation SOC battery of electric vehicle can be solved by narrow learning layers.