Improved State Of Charge Estimation Research Articles

Deep learning-based state of charge estimation for electric vehicle batteries: Overcoming technological bottlenecks

This study presents a novel deep learning-based approach for the State of Charge (SOC) estimation of electric vehicle (EV) batteries, addressing critical challenges in battery management and enhancing EV efficiency. Unlike conventional methods, our research leverages a diverse dataset encompassing environmental factors (e.g., temperature, altitude), vehicle parameters (e.g., speed, throttle), and battery attributes (e.g., voltage, current, temperature) to train a sophisticated deep learning model. The key novelty of our approach lies in its integration of real-world driving data from a BMW i3 EV, enabling the model to capture the intricate dynamics affecting SOC with remarkable accuracy. We conducted 72 tests using actual driving trip data, which included 25 types of environmental variables, to validate the feasibility and effectiveness of our proposed model. The deep learning network, designed specifically for SOC estimation, outperformed traditional models by demonstrating superior accuracy and reliability in predicting SOC values. Our findings indicate a significant advancement in SOC estimation techniques, offering actionable insights for both policymakers and industry practitioners aimed at fostering energy conservation, carbon reduction, and the development of more efficient EVs. The study's major contribution is its demonstrated capability to improve SOC estimation accuracy by understanding the complex interrelationships among various influencing factors, thereby addressing a pivotal challenge in EV battery management. By employing cutting-edge deep learning techniques, this research not only marks a significant leap forward from traditional SOC estimation methods but also contributes to the broader goals of sustainable transportation and environmental protection.

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.

State of charge estimation for electric vehicles using random forest

This paper introduces an innovative approach to addressing a critical challenge in the electric vehicle (EV) industry—the accurate estimation of the state of charge (SOC) of EV batteries under real-world operating conditions. The electric mobility landscape is rapidly evolving, demanding more precise SOC estimation methods to improve range prediction accuracy and battery management. This study applies a Random Forest (RF) machine learning algorithm to improve SOC estimation. Traditionally, SOC estimation has posed a formidable challenge, particularly in capturing the complex dependencies between various parameters and SOC values during dynamic driving conditions. Previous methods, including the Extreme Learning Machine (ELM), have exhibited limitations in providing the accuracy and robustness required for practical EV applications. In contrast, this research introduces the RF model, for SOC estimation approach that excels in real-world scenarios. By leveraging decision trees and ensemble learning, the RF model forms resilient relationships between input parameters, such as voltage, current, ambient temperature, and battery temperatures, and SOC values. This unique approach empowers the model to deliver precise and consistent SOC estimates across diverse driving conditions. Comprehensive comparative analyses showcase the superiority of the RF over ELM. The RF model not only outperforms in accuracy but also demonstrates exceptional robustness and reliability, addressing the pressing needs of the EV industry. The results of this study not only underscore the potential of RF in advancing electric mobility but also suggest a promising integration of the SOC estimation approach into the battery management system of BMW i3. This integration holds the key to more efficient and dependable electric vehicle operations, marking a significant milestone in the ongoing evolution of EV technology. Importantly, the RF model demonstrates a lower Root Mean Squared Error (RMSE) of 5.902,8% compared to 6.312,7% for ELM, and a lower Mean Absolute Error (MAE) of 4.432,1% versus 5.111,2% for ELM across rigorous k-fold cross-validation testing, reaffirming its superiority in quantitative SOC estimation.

Open circuit voltage - state of charge curve calibration by redefining max–min bounds for lithium-ion batteries

The relationship between open circuit voltage (OCV) and state of charge (SoC) is essential for SoC estimation of lithium-ion batteries, which can be secured by either low-current OCV test or incremental OCV test, with incremental OCV test demonstrating better results. Nevertheless, low current always leads to a discharge capacity that is inconsistent with maximum available capacity, which is a key parameter in SoC estimation. Therefore, the resultant OCV-SoC curve fails to provide a consistent SoC range and thus affects SoC estimation. In this paper, OCV-SoC curves obtained from low-current OCV tests are calibrated by redefining max–min bounds to improve SoC estimation accuracy. Max–min bounds of SoC are redefined by measuring, calculating and resetting upper and lower cut-off voltages of the OCV-SoC curve. Based on second-order RC model, model parameters are identified online and adaptive square-root unscented Kalman filter (ASRUKF) algorithm is introduced to perform SoC estimation, which is proved to be precise and adaptive. The calibrated OCV-SoC curve achieves an overall better SoC estimation performance than that obtained from the low-current OCV test and incremental OCV test, which is validated by experiments of LiNiMnCoO2 (NMC) batteries at 0°C and 25°C.

Dual time-scale state-coupled co-estimation of state of charge, state of health and remaining useful life for lithium-ion batteries via Deep Inter and Intra-Cycle Attention Network

The reliable and safe battery operations demand accurate estimation of battery states in the battery management system (BMS). Due to the complex electrochemical process, it is challenging to directly measure SOH and RUL of batteries for online applications. Moreover, the battery aging can strongly influence SOC estimation. The paper proposes a SOC, SOH, and RUL co-estimation method based on Deep Inter and Intra-Cycle Attention Network (DIICAN). From BMS data, the dual time-scale inter-cycle and intra-cycle features are automatically extracted with neural networks. The state degradation attention (SDA) unit is employed to recognize the state evolution pattern and evaluate battery degradation degree based on inter-cycle features. In addition, the extracted inter-cycle and intra-cycle features are simultaneously coupled for the SOC estimation to calibrate the capacity degradation. The mean absolute error (MAE) and root mean square error (RMSE) of the state of health (SOH) estimation are 0.30% and 0.43%, respectively. For the remaining useful life (RUL) estimation, the MAE is 178.5, and the RMSE is 230.3. With state coupling, the MAE of the state of charge (SOC) estimation decreases from 2.36% to 1.00%, and the RMSE decreases from 3.27% to 1.36% over the entire lifespan. In summary, the proposed method can not only accurately co-estimate SOH and RUL, but improve SOC estimation accuracy over the entire battery life.

State of Charge Estimation of Lithium-Ion Batteries Using Physics-Informed Transformer for Limited Data Scenarios

Abstract Accurate estimation of the state of charge (SOC) is crucial for ensuring the safe and efficient operation of lithium-ion batteries. Machine learning (ML) models may achieve high SOC estimation accuracy, but typically require large training datasets that may not always be accessible in practical applications. To address this issue, this work proposes a hybrid model consisting of a transformer neural network and a single particle model with electrolyte dynamics (SPMe) for SOC estimation in limited data scenarios. The transformer can leverage the internal battery states estimated by the SPMe when necessary and learn to use information from multiple sources (i.e., experimental data and SPMe). Two limited data scenarios, partially available cycles and varying temperatures, are evaluated with experimental battery discharge cycles to identify the conditions under which the proposed model outperforms traditional ML models. Despite being highly dependent on the SPMe’s performance, the hybrid model demonstrated improved SOC estimation over the baseline models, with less than 2% error for most scenarios.

Multi-dimensional features based data-driven state of charge estimation method for LiFePO4 batteries

The flat open-circuit voltage (OCV) curve of LiFePO4 (LFP) batteries poses a significant challenge to state of charge (SOC) estimation. To solve this problem, this paper proposes a data-driven SOC estimation method based on multi-dimensional features, especially incorporating force signals. The significant force variation at the middle SOC region section compensates for the flat OCV problem. A long short-term memory (LSTM) neural network model is established to estimate SOC. Battery voltage, current, temperature, and force data sampled only in 5 s are taken as input. The proposed method is validated under different dynamic testing profiles and different temperatures. Experimental results indicate that this method can highly improve SOC estimation accuracy in the middle SOC region, with less than 0.5% root mean square errors and less than 2.5% maximum errors. The validation results at different temperatures also maintain high accuracy with the same model, showing strong robustness and excellent generalization performance. Additionally, the model training process of this method only takes 1.5 h, and the online estimation time is less than 1 s, considerably reducing time cost.

An improved state of charge estimation of lithium-ion battery based on a dual input model

ABSTRACT The state of charge (SOC) is critical for the safety and reliable utilization of the battery. However, SOC estimation accuracy of lithium iron phosphate (LiFePO4) battery is a challenging task because of its flat voltage plateau and its performance is affected greatly by temperature. An improved open circuit voltage (OCV) combined with coulomb counting (CC) method based on the dual input model of ambient temperature and OCV is proposed. This approach is employed to obtain the initial value of SOC based on the dual input model over a wide range of temperature, and to establish SOC estimation model of modified CC with Simulink. The temperature entropy coefficient and differential analysis has been analyzed under different SOC conditions. The SOC maximum errors predicted by the proposed OCV-CC method maintain within 3.10%, while the maximum errors of the traditional OCV-CC increase from 4.78% to 23.64% under the temperature of 35°C ~−15°C. The results show the estimation accuracy of SOC using the proposed OCV-CC method is improved obviously, especially under low temperature.

A Self-Calibration SOC Estimation Method for Lithium-Ion Battery

Accurate state of charge (SOC) estimation is essential for the battery management system (BMS). In engineering, inappropriate selection of equivalent circuit model (ECM) and model parameters is common for lithium-ion batteries. It can result in systematic errors (i.e., modeling errors) in the state-space equation, thus affecting the SOC estimation accuracy. Aiming at that, this paper proposes a self-calibration method to enhance SOC estimation. In the method, a novel state-space equation containing an unknown systematic error term is developed based on the Thevenin model. A self-calibration unscented Kalman filter (SC-UKF) algorithm is then introduced for recursive SOC estimation. The algorithm can automatically recognize and calibrate the unknown systematic error in the state equation, while also reducing the random noise effect through data fusion with the measurement equation. Test results demonstrate that the method can effectively correct the Thevenin modeling error and improve SOC estimation accuracy. Furthermore, the proposed method is computationally simple and convenient for engineering applications without increasing model complexity.

Joint Estimation Method with Multi-Innovation Unscented Kalman Filter Based on Fractional-Order Model for State of Charge and State of Health Estimation

This study simulates the polarization effect during the process of battery charging and discharging, and investigates the characteristics of the process. A fractional-order model (FOM) is established and the parameters of the FOM are identified with the adaptive genetic algorithm. As Kalman filter estimation causes error accumulation over time, using the fractional-order multi-innovation unscented Kalman filter (FOMIUKF) is a better choice for state of charge (SOC) estimation. A comparative study shows that the FOMIUKF has higher accuracy. A multiple timescales-based joint estimation algorithm of SOC and state of health is established to improve SOC estimation precision and reduce the amount of computation. The FOMIUKF algorithm is used for SOC estimation, while the UKF algorithm is used for SOH estimation. The joint estimation algorithm is then compared and analyzed alongside other Kalman filter algorithms under different dynamic operating conditions. Experimental results show that the joint estimation algorithm possesses high estimation accuracy with a mean absolute error of under 1% and a root mean square error of 1.35%.

SOC Estimation of Lithium-Ion Battery Based on Equivalent Circuit Model with Variable Parameters

The state of charge (SOC) of the battery is an important basis for the battery management system to perform state monitoring and control decisions. In this paper, by identifying the internal parameters of the battery model at different temperatures and SOCs of the lithium-ion battery, the specific factors that affect the change of the parameters are analyzed, the segmentation basis of the model and the fitting method of related parameters are discussed, the second-order equivalent circuit model of the lithium-ion battery whose parameters vary with SOC and temperature is established, the unscented Kalman filter (UKF) is used to estimate the SOC of the lithium-ion battery, and an improved SOC estimation method based on optimized equivalent circuit model is proposed. Simulation and experimental results show that the improved SOC estimation strategy can obtain high estimation accuracy in a wide temperature range.

Charging Diagnosis of Power Battery Based on Adaptive STCKF and BLS for Electric Vehicles

This article proposes a BLS-STCKF estimation and diagnosis scheme of state of charge (SOC) and RUL prediction for power batteries in electric vehicles (EVs). First, the process of SOC estimation is considered as a time series problem. The walk-forward validation method has been applied in BLS (Broad Learning System) estimator training and validation. Then, a sliding window with variable width is used to help the time series SOC estimation improve. Meanwhile, STCKF (Strong Tracking Cubature Kalman Filter) is combined with BLS to help search for an optimal hyperparameter for BLS. Then, this article takes the SOC diagnosis as a problem of diagnosing abnormal time segments and uses HOT SAX(Heuristically Ordered Time series using Symbolic Aggregate ApproXimation) to detect SOC anomalies. Moreover, a prediction method of RUL (Remain Useful Life) has been designed by BLS-STCK. In light of result of RUL, the capacity degradation is considered to improve SOC estimation, and capacity retention coefficient is posed to adjust BLS-STCKF when battery aged. Finally, the proposed methods has also been evaluated by real driving data of EVs. Based on those critical experiments, rational analysis and conclusion have been illustrated that the proposed method has competitive accuracy.

Temperature characterization based state-of-charge estimation for pouch lithium-ion battery

Pouch lithium-ion batteries have aroused widespread attention in the field of electric vehicles because of the high energy density and low internal resistance. The available capacity of the battery varies greatly under different temperatures, resulting significant error in state-of-charge (SOC) estimation. An effective approach to relieve this issue is to make full use of the battery surface temperature. This article presents an enhanced Coulomb counting method based on surface temperature characterization to improve SOC estimation accuracy. Theoretical analysis and visualization tests using infrared thermal imager are conducted to identify surface temperature features during battery operation. The feature regions of the pouch lithium-ion battery are initially determined by temperature distribution function and further screened by dynamic time warping (DTW) algorithm. Finally, the screened temperature features are applied to battery available capacity modeling. The experimental results prove that the proposed method can effectively improve the SOC estimation accuracy under various temperature conditions.

Research on the State of Charge of Lithium-Ion Battery Based on the Fractional Order Model

Accurate estimation of the state of charge (SOC) of lithium batteries is paramount to ensuring consistent battery pack operation. To improve SOC estimation accuracy and suppress colored noise in the system, a fractional order model based on an unscented Kalman filter and an H-infinity filter (FOUHIF) estimation algorithm was proposed. Firstly, the discrete state equation of a lithium battery was derived, as per the theory of fractional calculus. Then, the HPPC experiment and the PSO algorithm were used to identify the internal parameters of the second order RC and fractional order models, respectively. As discovered during working tests, the parameters identified via the fractional order model proved to be more accurate. Furthermore, the feasibility of using the FOUHIF algorithm was evaluated under the conditions of NEDC and UDDS, with obvious colored noise. Compared with the fractional order unscented Kalman filter (FOUKF) and integer order unscented Kalman filter (UKF) algorithms, the FOUHIF algorithm showed significant improvement in both the accuracy and robustness of the estimation, with maximum errors of 1.86% and 1.61% under the two working conditions, and a terminal voltage prediction error of no more than 5.29 mV.

State of charge estimation of Lithium-ion batteries based on the probabilistic fusion of two kinds of cubature Kalman filters

The accurate simulation of batteries’ dynamic characteristics is important for improving the State of Charge (SoC) estimation performance. However, an equivalent circuit model of batteries tends to have changing simulation accuracy of the battery’s dynamic characteristics during SoC estimation. Although the adaptive high-degree cubature Kalman filter (AHCKF) has a more accurate estimation of the dynamic characteristics simulated by the battery model than the adaptive cubature Kalman filter (ACKF) does, AHCKF may have lower estimation accuracy of the battery’s real dynamic characteristics. Therefore, AHCKF does not always outperform ACKF at each time step during SoC estimation.To improve SoC estimation accuracy, this paper proposes an estimation method based on the probabilistic fusion of ACKF and AHCKF. Furthermore, the impact of the filters’ initial weights on the accuracy of the fusion method is analyzed. Under the dynamic stress test, when ACKF’s initial weight is 0.5, the mean absolute error and the root mean square error of SoC estimation based on the proposed method decrease by 22.35% and 6.36% compared with ACKF, respectively, as well as 7.04% and 4.63% compared with AHCKF, respectively. The result shows that the proposed method can improve the accuracy of SoC estimation when the filters’ initial weights are appropriately set.

Mobile GPS Application Design Based on System-Level Power and Battery Status Estimation

Mobile systems such as smartphones require accurate estimation of the battery-related features including the remaining energy and operating time, especially as the the power consumption of user applications is growing continuously these days. We present an energy-aware smartphone application design framework that considers the battery’s state of charge (SOC), energy depletion rate, as well as the service quality of the target application. We use a verified-accurate battery energy estimation method in an Android-OS-based mobile computing system. The battery model considers the rate-capacity effect. We apply regression-based models for the power estimation of the major subsystems in the smartphone, and then aggregate the result to yield the whole system’s power. We first determine the quality of service for the location device (GPS), the display device (LCD), and the overall system (application). Then, we control the error rate of the GPS and the brightness of the display to acquire the maximum service quality of the system for a given car trip. We show the advantage of the proposed method with a case study of a trip. In this case, the smartphone guides a user’s car trip using its GPS navigation capabilities; to do this, we propose an adaptive algorithm that exploits our improved SOC estimation and considers the car’s variable velocity. This proposed adaptive power and service quality control of the GPS application improves the quality of service in this example case and ensures there is enough remaining battery for the trip to be completed. In contrast, conventional approaches to this task provide a lower quality of service and run out of battery before the trip finishes. In conclusion, if a trip plan is provided, an application using our method delivers the maximum quality of service, such as system endurance time, location error, and display brightness.

Leveraging Cell Expansion Sensing in State of Charge Estimation: Practical Considerations

Measurements such as current and terminal voltage that are typically used to determine the battery’s state of charge (SOC) are augmented with measured force associated with electrode expansion as the lithium intercalates in its structure. The combination of the sensed behavior is shown to improve SOC estimation even for the lithium ion iron phosphate (LFP) chemistry, where the voltage–SOC relation is flat (low slope) making SOC estimation using measured voltage difficult. For the LFP cells, the measured force has a non-monotonic F–SOC relationship. This presents a challenge for estimation as multiple force values can correspond to the same SOC. The traditional linear quadratic estimator can be driven to an incorrect SOC value. To address these difficulties, a novel switching estimation gain is used based on determining the operating region that corresponds to the actual SOC. Moreover, a drift in the measured force associated with a shift of the cell SOC–expansion behavior over time is addressed with a bias estimator for the force signal. The performance of Voltage-based (V) and Voltage and Force-based (V&F) SOC estimation algorithms are then compared and evaluated against a desired ± 5 % absolute error bound of the SOC using a dynamic stress test current protocol that tests the proposed estimation scheme across wide range of SOC and current rates.

State of Charge Estimation for Lithium-Ion Battery in Electric Vehicle Based on Kalman Filter Considering Model Error

The state of charge (SOC) is one of the crucial states for battery management. The Kalman filter (KF) family algorithms are promising for SOC estimation. Based on the KF theory, a sufficiently accurate system model is the precondition for a better performance of the algorithm. Thus, we manage to improve the algorithm by estimating the battery model error. In this paper, the sources that may cause model errors are analyzed. Then, in order to estimate the unknown error term, the bias term characterizing the model error is adjoined to the original state vector to form a new state vector, and the KF is utilized to estimate the new state vector. It is a joint estimation algorithm for both SOC and the model error. Subsequently, by decoupling this joint estimation algorithm, a battery model error observer has been built. Finally, to verify the robustness of the proposed method against the battery model error, different types of errors such as open circuit voltage drift and voltage sensor drift are injected. The results indicate that the improved SOC estimation algorithm has better robustness and accuracy against the model mismatch compared with the standard KF algorithm.

Optimal BP neural network algorithm for state of charge estimation of lithium-ion battery using PSO with PCA feature selection

The state of charge (SOC) is the residual capacity of a battery, which indicates the available charge left inside a battery to drive a vehicle. Accurate SOC estimation is of great significance for a lithium-ion battery to ensure its safe operation and to prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is an inner state of a battery cell, which cannot be directly measured. This paper presents an improved SOC estimation strategy for a lithium-ion battery using the back-propagation neural network (BPNN). Two algorithms, principal component analysis (PCA) and particle swarm optimization (PSO), are used to enhance the accuracy and robustness. PCA is utilized to select the most significant input features. The PSO algorithm is developed to determine the optimal value of hidden layer neurons and the learning rate since these parameters are the most critical factors in constructing an optimal BPNN model. The proposed model is tested and evaluated by using three electric vehicle drive cycles. The performance of the proposed model is compared with common BPNN and radial basis function neural network (RBFNN) models and verified based on the root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and SOC error. The validation results are very effective in predicting SOC with very narrow SOC error which demonstrates the model robustness. The results indicate that the proposed model computes RMSE to be 0.58%, 0.72%, and 0.47% for the Beijing Dynamic Stress Test (BJDST), Federal Urban Drive Schedule (FUDS), and US06, cycle, respectively.

Future vision for reduction of range anxiety by using an improved state of charge estimation algorithm for electric vehicle batteries implemented with low‐cost microcontrollers

The fear of being stranded by a depleted electric vehicle (EV) battery is commonly referred to as `range anxiety'. This study explores a future vision for a comprehensive driver alerting algorithm to reduce the range anxiety by increasing the accuracy of the EV range estimation method. The key piece of information required to achieve this is an accurate battery state of charge (SoC) estimation. This study proposes an improved SoC estimation algorithm for implementation using low-cost microcontrollers. A method by which this improved algorithm can be implemented in a distributed battery management system is presented. The improved SoC estimation can be used to provide an enhanced range estimation method that can take into consideration a variety of environmental and behavioural factors. The proposed range estimate is more accurate than when only SoC is considered and can be implemented as part of a comprehensive driver alerting system to alert the EV driver of the expected energy required to reach the destination, the expected range with current SoC, an advisory if charging will be required prior to reaching the destination and the suggested duration of the charging required. By reducing the uncertainty surrounding EV range, it is hoped that the uptake of EVs can be improved.