SOC Estimation for Energy Storage Battery based on Adaptive Capacity Considering Discharge Rate

The estimation of state of charge (SOC) is a critical issue in the energy management of electric vehicle (EV) power batteries. However, the current accuracy of SOC estimation methods does not meet the requirements of practical applications. Therefore, this study proposes an improved lithium-ion battery SOC estimation method that combines deep residual shrinkage network (DRSN) and bidirectional gated recurrent unit (BiGRU) to enhance the SOC estimation accuracy. First, we insert the bidirectional gated recurrent unit neural network between the global average pooling layer and the output fully connected layer of the deep residual shrinkage network. This improvement enhances the model’s expressiveness, robustness, and data learning effect. Second, we develop a new activation function called “∂_swish” to replace the original ReLU activation function in the deep residual shrinkage network. The ∂_swish activation function improves the accuracy of the deep network model and reduces the risk of overfitting by utilizing its regularization effect. Finally, we conduct experimental tests at three different temperatures using the FUDS driving cycle dataset and the DST-US06-FUDS continuous driving cycle dataset. The algorithm model’s convergence speed is verified by comparing it with other models. The results show that compared to other models, the proposed method significantly improves SOC estimation accuracy at three different temperatures. In addition, the method demonstrates a high convergence speed.

Battery is one of the main parts in Electrical vehicles as it serves as energy source. In order to prolong battery lifetime and optimally operate the battery, battery performance needs to be monitored and controlled using Battery Management System (BMS). One among important BMS functions is to determine the amount of remaining energy capacity available in a battery, represented by State of Charge (SOC), through estimation. The SOC estimation accuracy is important since it is used as indicator to prevent battery from failures due to overcharging and over discharging. One of the SOC estimation methods is Coulomb counting (CC) which is easy to implement. However, a false initial value of the SOC in this method will degrade the accuracy of the SOC estimation. In this paper an SOC estimation method is proposed by using CC method combined with recursive least square (RLS) algorithm and Proportional Integral (PI) controller to correct the error of SOC estimation due to false initial value of SOC. The result indicates that the proposed method is able to correct the error of SOC estimation due to false initial value of SOC with average of SOC estimation error equals 0.498%.

In order to improve the accuracy of the battery state of charge (SOC) estimation, in this paper we take a lithium-ion battery as an example to study the adaptive Kalman filter based SOC estimation algorithm. Firstly, the second-order battery system model is introduced. Meanwhile, the temperature and charge rate are introduced into the model. Then, the temperature and the charge rate are adopted to estimate the battery SOC, with the help of the parameters of an adaptive Kalman filter based estimation algorithm model. Afterwards, it is verified by the numerical simulation that in the ideal case, the accuracy of SOC estimation can be enhanced by adding two elements, namely, the temperature and charge rate. Finally, the actual road conditions are simulated with ADVISOR, and the simulation results show that the proposed method improves the accuracy of battery SOC estimation under actual road conditions. Thus, its application scope in engineering is greatly expanded.

An optimized long short-term memory-weighted fading extended Kalman filtering model with wide temperature adaptation for the state of charge estimation of lithium-ion batteries

State of charge estimation for lithium-ion batteries based on adaptive dual Kalman filter

The state of charge (SoC) estimation is the safety management basis of the packing lithium‐ion batteries (LIB), and there is no effective solution yet. An improved splice equivalent modeling method is proposed to describe its working characteristics by using the state‐space description, in which the optimization strategy of the circuit structure is studied by using the aspects of equivalent mode, analog calculation, and component distribution adjustment, revealing the mathematical expression mechanism of different structural characteristics. A novel particle adaptive unscented Kalman filtering algorithm is introduced for the iterative calculation to explore the working state characterization mechanism of the packing LIB, in which the incorporate multiple information is considered and applied. The adaptive regulation is obtained by exploring the feature extraction and optimal representation, according to which the accurate SoC estimation model is constructed. The state of balance evaluation theory is explored, and the multiparameter correction strategy is carried out along with the experimental working characteristic analysis under complex conditions, according to which the optimization method is obtained for the SoC estimation model structure. When the remaining energy varies from 10% to 100%, the tracking voltage error is <0.035 V and the SoC estimation accuracy is 98.56%. The adaptive working state estimation is realized accurately, which lays a key breakthrough foundation for the safety management of the LIB packs.

This paper proposed a battery state of charge (SOC) estimation methodology utilizing the Extended Kalman Filter. First, Extended Kalman Filter for Li-ion battery SOC was mathematically designed. Next, simulation models were developed in MATLAB/Simulink, which indicated that the battery SOC estimation with Extended Kalman filter is much more accurate than that from Coulomb Counting method. This is coincident with the mathematical analysis. At the end, a test bench with Lithium-Ion batteries was set up to experimentally verify the theoretical analysis and simulation. Experimental results showed that the average SOC estimation error using Extended Kalman Filter is <;1%.

Microgrids make it easier to integrate Renewable Energy Sources (RESs) and Energy-Storage Systems (ESSs) at the consumer level, with the intent of enhancing power quality, reliability, and efficiency. This microgrid concept at the nano grid level is championed by a low voltage DC (LVDC) grid, facilitating the direct integration of several distributed generators, storage and loads that are almost DC source/load. Since the State of Charge (SOC) of the battery is an essential parameter for building energy management systems, careful monitoring of SOC is essential. The SOC of a battery directly reflects its performance; thus, straightforward, reasonably accurate, and timely estimation of SOC protects against overcharging or discharging issues, with fewer processor requirements and computations. A novel SOC estimation method is proposed based on the literature assessment of different existing SOC estimation methods. In regions with a tropical climate, the temperature range over the year is small, and the sunshine is relatively intense and fairly even throughout the year. Also, for all practical purposes, it is a fact that the model that takes inputs at the point of use gives accurate results. Considering the 2 facts mentioned above, the current model uses a less complicated algorithm, within acceptable accuracy for the purpose and relatively user-friendly.&#x0D; HIGHLIGHTS&#x0D; &#x0D; State Of Charge (SOC) estimation of 48V Lead Acid battery for Low Voltage DC (LVDC) building energy management systems&#x0D; Straightforward, reasonably accurate, and timely estimation of SOC which protects against overcharging or discharging issues, with fewer processor requirements and computations&#x0D; For the SOC estimation, the open-circuit voltage is obtained by measuring the terminal voltage, current and first-order model of the battery developed under similar climatic condition&#x0D; Development and testing of an algorithm incorporating temperature-independent first-order battery model for the tropical climate with lesser temperature variation&#x0D; &#x0D; GRAPHICAL ABSTRACT

Accurate state of charge (SOC) estimation is key for the efficient management of lithium–ion (Li-ion) batteries, yet is often compromised by noise levels in measurement data. This study introduces a new approach that uses wavelet denoising with a machine learning regression model to enhance SOC prediction accuracy. The application of wavelet transform in data pre-processing is investigated to assess the impact of denoising on SOC estimation accuracy. The efficacy of the proposed technique has been evaluated using various polynomial and ensemble regression models. For empirical validation, this study employs four Li-ion battery datasets from NASA’s prognostics center, implementing a holdout method wherein one cell is reserved for testing to ensure robustness. The results, optimized through wavelet-denoised data using polynomial regression models, demonstrate improved SOC estimation with RMSE values of 0.09, 0.25, 0.28, and 0.19 for the respective battery datasets. In particular, significant improvements (p-value &lt; 0.05) with variations of 0.18, 0.20, 0.16, and 0.14 were observed between the original and wavelet-denoised SOC estimates. This study proves the effectiveness of wavelet-denoised input in minimizing prediction errors and establishes a new standard for reliable SOC estimation methods.

The accurate estimation of battery state of charge (SOC) enables the reliable and safe operation of lithium‐ion batteries. Data‐driven SOC estimation is considered an emerging and effective solution. However, existing data‐driven SOC estimation methods typically involve direct estimation and lack effective feedback correction. Moreover, battery degradation poses additional challenges to accurate SOC estimation. Therefore, this study proposes an adaptive combined method for battery SOC estimation based on a long short‐term memory (LSTM) network and unscented Kalman filter (UKF) algorithm considering battery aging status. First, an LSTM model is constructed to characterize the battery's dynamic performance instead of traditional battery models. Then, the UKF algorithm is employed to perform SOC estimation through the feedback of terminal voltage prediction. To enhance estimation accuracy under different aging statuses, a proportional‐integral‐derivative controller is employed to correct the capacity fading during the SOC estimation process. Validation results indicate that the terminal voltage prediction model demonstrates exceptional robustness against interference from current and voltage noise. Compared to the traditional estimation method combining the deep learning model and Kalman filter algorithm, the proposed method demonstrates superior estimation accuracy under various complex operating conditions. Furthermore, the proposed method outperforms the traditional method in estimation performance during battery aging.

Highly accurate state of charge (SOC) estimation of lithium-ion batteries is one of the key technologies of battery management systems in electric vehicles. The performance of SOC estimation directly influences the driving range and safety of these vehicles. Due to external disturbances, temperature variation and electromagnetic interference, accurate SOC estimation becomes difficult. To accurately estimate the SOC of lithium-ion batteries, this article presents a novel machine-learning method to address the risk of gradient explosion and gradient decent using the dynamic nonlinear auto-regressive models with exogenous input neural network (NARX) with long short-term memories (LSTM).The proposed hybrid NARX model embeds LSTM memory, which provides jump-ahead connections in the time-unfolded model. These jump-ahead connections provide a shorter path for the propagation of gradient information, therefore reducing long-term dependence on the recurrent neural network. Experimental results show that the estimation performance root mean square error (RMSE) of the proposed model is less than 1%, and this model has better multitime prediction performance. Finally, the hybrid NARX and LSTM model is compared with the standard back propagation neural network based on particle swarm optimization (BPNN-PSO), the least-squares support vector machine (LS-SVM) and LSTM existing models under urban dynamometer driving schedule (UDDS) and dynamic stress test (DST) conditions. The proposed hybrid NARX-LSTM model yield relative to other methods and can estimate the battery SOC with high accuracy. The RMSE of proposed model is improved by approximately 60% compared with the standard LSTM.

The battery management system (BMS) of new energy vehicles is a research hotspot for its importance to security. A typical BMS is designed in this paper to collect the status parameters (voltage, current, and temperature) and estimate state of charge (SOC) of the battery, which includes the battery pack, the lower computer and the upper computer. The lower computer mainly collected the status parameters. The upper computer mainly displayed the status of battery pack and controlled the working mode. For SOC estimation, the improved ampere-hour (Ah) integral algorithm in the lower computer considered the charge and discharge efficiency, temperature, and ageing factors; the improved extended Kalman filter (EKF) algorithm in the upper computer includes the dual polarisation (DP) model. Finally, the 1C constant current discharge experiment shows that the SOC estimation accuracy is good; especially the improved EKF is only ±0.02% and also tracks voltage fluctuations well.

Dynamic ultrasonic response modeling and accurate state of charge estimation for lithium ion batteries under various load profiles and temperatures

In this paper, a battery state of charge (SOC) estimation method based on the extended Kalman filter is proposed. In some known battery SOC estimation methods, it is assumed that the relationship between battery open circuit voltage and SOC is linear and static. However, this relationship is only piece wisely linear in practice and varies with the ambient temperature, as assumed in this work. The proposed model assumption matches better with the real battery behavior. A battery is modeled as a nonlinear system, with the SOC defined as a system state. The extended Kalman filter is applied to estimate SOC directly for a lithium battery pack. The effectiveness of the proposed method is verified on a power transmission line inspection robot. The experimental results verify the effectiveness of the proposed method.

State of charge (SOC) is a critical factor to guarantee that a battery system is operating in a safe and reliable manner. Many uncertainties and noises, such as fluctuating current, sensor measurement accuracy and bias, temperature effects, calibration errors or even sensor failure, etc. pose a challenge to the accurate estimation of SOC in real applications. This paper adds two contributions to the existing literature. First, the auto regressive exogenous (ARX) model is proposed here to simulate the battery nonlinear dynamics. Due to its discrete form and ease of implemention, this straightforward approach could be more suitable for real applications. Second, its order selection principle and parameter identification method is illustrated in detail in this paper. The hybrid pulse power characterization (HPPC) cycles are implemented on the 60AH LiFePO4 battery module for the model identification and validation. Based on the proposed ARX model, SOC estimation is pursued using the extended Kalman filter. Evaluation of the adaptability of the battery models and robustness of the SOC estimation algorithm are also verified. The results indicate that the SOC estimation method using the Kalman filter based on the ARX model shows great performance. It increases the model output voltage accuracy, thereby having the potential to be used in real applications, such as EVs and HEVs.