SOC Estimation Research Articles

State of charge estimation of Li-ion batteries based on deep learning methods and particle-swarm-optimized Kalman filter

The estimation of SOC is a key issue for the high-efficient and reliable operation of Li-ion batteries, thus has been increasingly concerned in current years with the development of electric vehicles. During dynamic test cycles, the accurate estimation of SOC is more difficult than steady operation conditions due to the fierce oscillations of the input signals. In this paper, a hybrid method of deep learning method and Kalman filter was proposed for the estimation of SOC. First, convolutional neural network or temporal convolutional network was combined with different variants of recurrent neural network, including long short term memory, gated recurrent unit, peeple hole long short term memory and bidirectional long short term memory, to achieve the estimation of SOC by capturing the spatial and temporal characteristics of input signals. Afterwards, the deep learning method was integrated with Kalman filter to eliminate the effects of transient signal oscillations and further improve the accuracy for the estimation of SOC. The results indicated that estimation accuracy and estimation time could be improved by less than 20 % by varying deep learning methods while after integrating deep learning method with Kalman filter, more than 45 % improvements in test accuracy could be achieved without obvious sacrifices in estimation time.

An Adaptive Cutoff Frequency Design for Butterworth Low-Pass Filter Pursuing Robust Parameter Identification for Battery Energy Storage Systems

Energy storage systems are key to propelling the current renewable energy revolution. Accurate State-of-Charge estimation of the lithium-ion battery energy storage systems is a critical task to ensure their reliable operations. Multiple advanced battery model-based SOC estimation algorithms have been developed to pursue this objective. Nevertheless, these battery model-based algorithms are sensitive to measurement noises since the measurement noises affect the accuracy of battery model identification, thus leading to inaccurate battery SOC estimation consequently due to modeling error. The Butterworth low-pass filter has proven effectiveness in measurement noise filtering for accurate parameter identification, while the cutoff frequency design relies on prior knowledge of lithium-ion batteries, making its capability limited to general cases. To overcome this issue, this paper proposes an adaptive cutoff frequency design algorithm for the Butterworth low-pass filter. Simulation results show that the low-pass filter functions properly in the presence of multiple scales of measurement noises adopting the proposed work. Consequently, the parameters of the battery model and the SOC of the battery are both identified and estimated accurately, respectively. In detail, the parameters: R0, R1, C1, and the time constant τ are all identified accurately with low relative identification errors of 0.028%, 11.12%, 6.21%, and 5.94%, respectively, in an extreme case. Furthermore, the SOC of the battery can thus be estimated accurately, leaving a low of 0.081%, 0.97%, and 0.14% in the mean and maximum absolute SOC estimation error and the standard deviation, respectively.

State-of-charge estimation of lithium-ion batteries based on MCC-AEKF in non-Gaussian noise environment

The extended Kalman filter (EKF) has low estimation accuracy for lithium-ion battery SOC under non-Gaussian noise interference. To solve this problem, an adaptive EKF based on maximum correlation-entropy criterion (MCC-AEKF) was proposed. In this algorithm, the maximum correlation entropy criterion is used to obtain accurate estimation results by replacing the overall solution with the local optimal solution. Meanwhile, the adaptive covariance matrix is established to update the process noise variance to reduce the SOC estimation error. MCC-AEKF were used to estimate the SOC based on two kinds of battery data. The experiments show that, under non-Gaussian noise interference and at operating temperature of 25 °C, compared with EKF and MCC-EKF, the estimation accuracy of MCC-AEKF improved by 80.3% and 24.2% separately for battery 1. For battery 2, its estimation accuracy improved by 72.6% and 22.0% separately, and are also the highest at 10 °C and 40 °C. Given wrong initial SOC values, MCC-AEKF can converges to real value within 30s. All results present the good accuracy and robustness of the proposed method under different cases.

A novel state-of-energy simplified estimation method for lithium-ion battery pack based on prediction and representative cells

Accurate online state of energy (SOE) estimation of a series-connected lithium-ion battery pack is very important for the driving range estimation of electric vehicles, which is still an urgent problem to be solved due to the high computational complexity caused by cell-to-cell inconsistency under practical application of the existing battery management system. In the manuscript, a novel low-complexity SOE simplified estimation method for lithium-ion battery pack based on prediction and representative cells is proposed. Firstly, two cells in the battery pack, one of which has the smallest remaining discharge capacity and the other of which reaches at or closest to the lower cut-off voltage during the discharge process, are selected as representative cells. Secondly, after the EKF algorithm is applied to achieve SOC estimation, the future states of the representative cells are accurately predicted to obtain the cut-off discharge time and the discharge capacity based on the thermoelectric coupling equivalent circuit model under the condition that the future output current of the battery pack has been predicted. The output energy of the representative cells during prediction is divided into three output energy generation parts contributed by OCV, ohmic resistance and polarization resistance respectively, and the three parts are cumulatively calculated and summed to obtain the remaining discharge energy of the representative cells. Thirdly, the approximately proportional relationships in three output energy generation parts between the representative and the non-representative cells are established based on the approximately proportional expressions of the three parts of all cells, and a simplified calculation method is subsequently applied to obtain the remaining discharge energy of non-representative cells based on the results of representative cells. Finally, the remaining discharge energy and SOE of the battery pack can be obtained. The results verified by complex dynamic condition experiments at different temperatures show that the maximum absolute error of the SOE estimation results is less than 3% and more than 85% of the calculation time is saved, which demonstrates that the developed method can achieve accurate and simple SOE estimation of the battery pack.

ARWLS-AFEKE: SOC Estimation and Capacity Correction of Lithium Batteries Based on a Fusion Algorithm

Accuracy of battery charge status (SOC) estimation plays a significant role in the management of electric vehicle power batteries. However, recently, abrupt changes from SOC data often occurs in the actual operation of electric vehicles and some errors appear in the establishment of battery models and noise models, which give rise to the poorly adaptive and robust performance of traditional algorithms in the process of SOC estimation. The fusion algorithm proposed in this paper can effectively improve the accuracy of models and SOC estimation of lithium-ion batteries. Based on the second-order R-C network model, this method optimizes the accuracy of parameter identification by adopting the adaptive recursive weighted least square algorithm (ARWLS). In addition, the adaptive fading extended Kalman filter algorithm (AFEKF) is applied to estimate the SOC of lithium-ion batteries. Additionally, via introducing a fading factor, the optimal Kalman gain is updated in real-time, which can reduce the impact of data mutation on battery modeling. Compared with the offline AEKF algorithm and the EKF algorithm, the adaptive recursive weighted least square-adaptive fading extended Kalman filter (ARWLS-AFEKF) fusion algorithm had higher accuracy and adaptability, which can be adapted to the variable noise environment.

A comparative study of state of charge estimation methods of ultracapacitors for electric vehicles considering temperature characteristics

This paper systematically compares and evaluates three types SOC estimation methods for ultracapacitors (UCs) under different ambient temperatures. Firstly, based on the UC dynamic test data, the key parameters of Thevenin model are identified by genetic algorithm. Next, the polynomial fitting is employed to determine the relationship between parameters and ambient temperatures, and the temperature-varied model is established. At the same time, according to the extended Kalman filter (EKF), adaptive extended Kalman filter (AEKF) and unscented Kalman filter algorithm (UKF), three kinds UC SOC estimation methods are designed, which are suitable for different ambient temperatures. Then, the accuracy, robustness and self-correction ability of these SOC estimation methods are evaluated by using the urban dynamometer driving schedule test data at −10 °C, 10 °C, 25 °C and 40 °C. Finally, the results indicate that the AEKF algorithm is superior to the EKF and UKF, and its average absolute error and root mean square error are within 0.227 % and 0.318 % at different temperatures, respectively. In addition, the proposed temperature-varied model has a wider application.

Exploring the Hysteresis Effect in SOC Estimation of Li-ion Batteries

The global energy crisis and serious environmental pollution make governments attach importance to the development of regenerating green energy. As the best battery system at present, lithium-ion battery has developed rapidly into a new generation of energy storage power. SOC estimation is an important indicator in the development of lithium-ion batteries. If SOC can be properly estimated, it will be pivotal to the investigation and exploitation of batteries, and a number of algorithms for SOC estimation have already emerged. This paper first introduces several current SOC estimation methods, and then explains the influencing factors and hysteresis effect of SOC. Then the SOC estimation value was obtained by open circuit voltage method, based on the mean value calculated by the random forest output OCV-SOC curve to reduce hysteresis effect. The effect of temperature on SOC was analysed by plotting the OCV-SOC profiles for charging and discharging at different temperatures. Finally, this paper is summarized and puts forward the improvement.

Estimation of State of Charge and State of Health of Marine Lithium Battery based on BP Neural Network

For the sake of better ensure the safety of a marine system reliability, economy, and power, on the basis of marine lithium battery discharge data, BP neural network is able to grasp the state of charge and health in this battery module. Owing to some stochastic factors of BP neural network weights and limens, the error fluctuates greatly, so a genetic algorithm is lead into improve its results. The improved GA-BP algorithm reduces the error of SOC estimation from 4.2% to 2.3% and of SOH from 0.52% to 0.24%. It indicates that the GA-BP neural network method for forecasting SOC and SOH of the marine battery management system has minor errors and high stability, which is feasible.

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

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

Practical Evaluation of Lithium-Ion Battery State-of-Charge Estimation Using Time-Series Machine Learning for Electric Vehicles

This paper presents a practical usability investigation of recurrent neural networks (RNNs) to determine the best-suited machine learning method for estimating electric vehicle (EV) batteries’ state of charge. Using models from multiple published sources and cross-validation testing with several driving scenarios to determine the state of charge of lithium-ion batteries, we assessed their accuracy and drawbacks. Five models were selected from various published state-of-charge estimation models, based on cell types with GRU or LSTM, and optimisers such as stochastic gradient descent, Adam, Nadam, AdaMax, and Robust Adam, with extensions via momentum calculus or an attention layer. Each method was examined by applying training techniques such as a learning rate scheduler or rollback recovery to speed up the fitting, highlighting the implementation specifics. All this was carried out using the TensorFlow framework, and the implementation was performed as closely to the published sources as possible on openly available battery data. The results highlighted an average percentage accuracy of 96.56% for the correct SoC estimation and several drawbacks of the overall implementation, and we propose potential solutions for further improvement. Every implemented model had a similar drawback, which was the poor capturing of the middle area of charge, applying a higher weight to the voltage than the current. The combination of these techniques into a single custom model could result in a better-suited model, further improving the accuracy.

A State-of-Charge estimation method of Lithium battery based on BP neural network

In the proposal of Dual-Carbon plan and the rapid development of clean energy generation, the energy-storage has become an important development direction in the power field. In this paper, the key parameter of the lithium battery-based energy-storage system—SOC estimation method are studied, the second-order resistance-capacitance model of the lithium battery is analysed. The equivalent circuit model of the lithium battery is established. The calculation basis of SOC in lithium battery is determined. The SOC estimation method of the battery is realized by BP neural network. The availability and performance of the advanced method is proved by the simulation analysis results based on the public lithium battery data.

State of Charge Estimation of LiFePO4 in Various Temperature Scenarios

The state estimation of a battery is a significant component of a BMS. Due to the poor temperature performance and voltage plateau phase in LiFePO4 batteries, the difficulty of state estimation is greatly increased. At the same time, the ambient temperature in which the battery operates is changeable, and its parameters will vary with the temperature. Therefore, it is extremely challenging to estimate the state of LiFePO4 batteries under variable temperatures. In an effort to accurately estimate the SOC of LiFePO4 batteries at different and variable temperatures, as well as its capacity at low temperature, the characteristics of LiFePO4 batteries at different temperatures are first tested. In addition, a variable temperature OCV experiment is designed to obtain the OCV of the full SOC range. Then, the ECM considering temperature is established and all parameters are identified by PSO. Finally, an improved EKF algorithm is presented to accurately estimate the SOC of LiFePO4 batteries at different and variable temperatures. Meanwhile, the battery capacity at low temperature is further estimated based on the estimated SOC result. The results show that SOC estimation errors at variable temperature are all within 3%, and the capacity estimation errors at low temperature are all within 1%.

Research on SOC estimation of residual power of lithium-ion batteries for electric vehicles based on extended Kalman filtering algorithm

Abstract As the energy carrier of electric vehicles, how to accurately estimate the remaining power (SOC) of the battery is one of the key technologies in the field of electric vehicle design. Effective estimation of SOC can bring accurate continuous mileage information to the driver, theoretically avoid overcharging and discharging the battery, and also protect the driver's driving safety. In the research of SOC estimation method, constructing a suitable battery model is an important means to improve SOC estimation and to improve the prediction accuracy. In order to obtain a higher response accuracy of the model, this paper proposes an electric vehicle SOC model based on the extended Kalman filter algorithm. Based on the actual data of lithium-ion power battery, SOC estimation research is carried out. The research shows that: when the internal temperature of the battery is the same as the ambient temperature, and both are 25 °C, the model is accurate, the terminal voltage difference is small, and the average voltage difference is 9mV respectively; at room temperature, the extended Kalman filter algorithm has a significant effect on the recovery percentage of SOC voltage. The average is over 73%, and the accuracy is high. The extended Kalman algorithm in this paper we use to estimate the SOC current waveform. The simulation results show that the SOC discharge current is 4A, which has high estimation accuracy and strong applicability.

Robust Estimation of State of Charge in Lithium Iron Phosphate Cells Enabled by Online Parameter Estimation and Deep Neural Networks

This paper addresses the state of charge estimation problem in lithium iron phosphate (LFP) battery cells. LFP cells are particularly challenging because their fat open circuit voltage (OCV) curve means OCV-based battery models are weakly observable. This means standard methods for SOC estimation don't easily converge to the true SOC. Additionally, in practice, estimates must be accurate in the face of biased noise on current input, as well as mean-zero noise on measurements. As such, we aim to create an estimator that is accurate when facing these types of noise. We accomplish this with a three-layer estimation technique that uses an adaptive Kalman filter, a Neural Network, and a Kalman Filter to estimate the state of charge. This method achieves an SOC estimation with an RMSE of 2.248%, even in the presence of a 0.2A current measurement bias and 5mA and 5mV random measurement noise. Notably, the proposed approach outperforms state-of-the-art methods like the extended Kalman filter.

Dynamic Cell Modeling for Accurate SOC Estimation in Autonomous Electric Vehicles

Dynamic Cell Modeling for Accurate SOC Estimation in Autonomous Electric Vehicles

EIS-Based SoC Estimation: A Novel Measurement Method for Optimizing Accuracy and Measurement Time

EIS-Based SoC Estimation: A Novel Measurement Method for Optimizing Accuracy and Measurement Time

MACHINE LEARNING AND IOT-BASED LI-ION BATTERY CLOUD MONITORING SYSTEM FOR 5G BASE STATIONS

With the accelerated construction of 5G and IoT, more and more 5G base stations are erected. However, with the increase of 5G base stations, the power management of 5G base stations becomes progressively a bottleneck. In this paper, we solve the problem of 5G base station power management by designing a 5G base station lithium battery cloud monitoring system. In this paper, first, the lithium battery acquisition hardware is designed. Second, a new communication protocol is established based on Modbus. Third, the windows desktop upper computer software and the cloud-based monitoring system are designed. Finally, this paper designs the improved ResLSTM algorithm which is fused with ResNet algorithm based on Stacked LSTM. The algorithm designed in this paper is tested in comparison with SVM and LSTM. The performance of the algorithm designed in this paper is better than SVM and LSTM. Furthermore, the communication test, as well as the training and testing of the ResLSTM algorithm are outstanding. The 5G base station lithium-ion battery cloud monitoring system designed in this paper can meet the requirements. It has great significance for engineering promotion. More importantly, the ResLSTM algorithm designed in this paper can better guide the method of lithium-ion battery SOC estimation.

Low-Cost Adaptive LS-DEKF for SOC Estimation and RDT Prediction With SFP Model

Low-Cost Adaptive LS-DEKF for SOC Estimation and RDT Prediction With SFP Model

A novel method for SoC estimation of lithium-ion batteries based on previous covariance matrices and variable ECM parameters

A novel method for SoC estimation of lithium-ion batteries based on previous covariance matrices and variable ECM parameters

Evaluation of Soil Organic Carbon Storage of Atillo in the Ecuadorian Andean Wetlands

Identifying the SOC levels and revealing the potential of SOC storage of ecosystems difficult to sample and study are necessary contributions to the understanding of the global reserves of SOC. Wetlands store large amounts of SOC within their soils. They have an important role in water regulation and have great biological and floristic diversity. Therefore, this study aimed to assess the SOC stock in Atillo micro-watershed in the Ecuadorian Andean wetlands at two soil depths (0–30 cm and 30–60 cm below ground) and to assess the importance of the ecosystem and its conservation in favor of reducing emissions due to degradation processes. For that, we sampled the study zone with 101 composite samples of soil to obtain the SOC storage for each sample point in Mg/ha. A SOC estimation to evaluate its spatial distribution was performed using the geostatistical method Kriging. The results show a high storage capacity of the study zone with SOC values of 126 to 454 Mg/ha in the 0–30 cm soil profile and 148 to 350 Mg/ha in the 30–60 cm soil profile. The preservation and protection mechanisms of high SOC reserves should be taken into account to prevent the emission of CO2.