Accurate State Of Charge Estimation Research Articles

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

Accurate state of charge (SOC) estimation at different operating temperatures is essential for the reliable and safe operation of battery management systems (BMS) for lithium-ion batteries in electric vehicles (EVs). In this paper, an optimized long-short-term memory-weighted fading extended Kalman filtering (LSTM-WFEKF) model with wide temperature adaptation is proposed as a temperature-conditioned model for SOC estimation. Firstly, the input datasets are categorized based on the operating temperatures for EVs in the United States Advanced Battery Consortium manual: cold (−10 °C), normal (25 °C), and hot (50 °C) temperatures and optimized with an attention mechanism for faster training of the LSTM model to cross-train and test to specifically study the effects of temperature on the SOC estimation through a transfer learning mechanism. Secondly, the SOC estimated by the LSTM model is input into a WFEKF method, which introduces adaptive weighing and fading factors to correct, denoise, and optimize the final SOC for each temperature variation under complex working conditions. Finally, the results show that the training and testing temperatures have distinctive SOC effects using the LSTM model. Also, the proposed LSTM-WFEKF model estimates the SOC with overall best mean absolute error (MAE), root mean square error (RMSE), and R-squared (R2) values of 0.0697%, 0.0784%, and 99.9965%, respectively, under different temperatures and complex working conditions, which is optimal compared to other existing models. Based on the MAE, RMSE, and R2 values under different operating temperatures and complex working conditions, this paper concludes that the 25 °C training dataset ensures a more accurate SOC estimation. Meanwhile, the −10 °C and 50 °C training datasets cause more and less noisy estimates, respectively. The proposed LSTM-WFEKF model has wide temperature and working condition adaptability for real-time BMS applications in EVs.

Bayesian information criterion based data-driven state of charge estimation for lithium-ion battery

Accurate state of charge (SOC) estimation is essential for the safe and reliable operation of Li-ion batteries. To solve the problem of poor generalisation caused by over-fitting, this paper presents a combination algorithm based on feature selection to estimate battery SOC. Firstly, a portion of the features is extracted from the extended Kalman filtering (EKF) results. It forms the set of features to be selected with four other measured features. Secondly, the optimal feature subset is adopted by designing a wrapped feature screening framework based on the Bayesian information criterion (BIC). Finally, the selected combination of features is adopted to train the support vector regression (SVR) model, which is applied to the battery SOC estimation. The experimental results reveal that the combination strategy of EKF and SVR improves the accuracy of SOC estimation. The optimal SVR model based on the feature selection criterion shows better generalisation. Better estimation results in four driving conditions are achieved, and the root-mean-square error of the battery SOC estimation is decreased by at least 64.1 % and 56.5 % compared to the EKF algorithm and SVR algorithm driven by full feature, respectively.

A novel method for determination of SOC of Lead-acid battery in E-Rickshaw application

Electric rickshaws are a popular mode of short-distance transportation in cities and an alternative to petrol- or diesel-powered auto-rickshaws because of their low capital and running cost and less human effort compared to pedal rickshaws. E Rickshaws can operate if there is energy or charge in the battery. State of Charge (SoC) indicates the amount of energy left in the batter. Accurate estimation of SoC is essential for estimating how long the vehicle can travel apart from overcharge and under-discharge protection resulting in life enhancement. Conventional methods of SoC prediction are based on the estimation of open-circuit voltage. These are inaccurate and time-consuming and become erroneous as the battery ages. A method for quick determination of SoC using a high current short-time discharge is proposed for both old and new batteries. Experimental investigations on 90Ah C10 batteries using the said methodology can determine the correct state of charge in any condition with a maximum error of ± 8 %.

Voltage-stress-based state of charge estimation of pouch lithium-ion batteries using a long short-term memory network

Accurate state-of-charge (SOC) estimation is critical to the energy management of lithium-ion batteries, which are increasingly employed in electric vehicles (EVs). In this work, a novel SOC estimation method of a battery based on voltage and stress measurements is proposed with the utilization of machine learning techniques. To evaluate the necessity of stress measurements for SOC estimation, a series of stress characteristic experiments are designed to probe the relationship between mechanical responses and the SOC. On this basis, the stress is taken as an auxiliary for the SOC estimation. Then, a typical long short-term memory (LSTM) network is introduced to map battery measurement signals directly to the battery SOC, where the voltage, stress, and current are selected as input vectors. The proposed method is evaluated under constant current (CC), dynamic stress test (DST), and urban dynamometer driving schedule (UDDS) conditions. Experimental results show that the maximum absolute error (MAE) and root mean square error (RMSE) can be bounded within 0.34 % and 0.45 %, which demonstrates that the voltage-stress-based method can achieve good SOC estimation accuracy. Therefore, the proposed method provides new insight into the SOC estimation of pouch lithium-ion batteries.

SOC estimation based on the gas‐liquid dynamics model using particle filter algorithm

Lithium-ion battery state of charge (SOC) estimation is the basis for other state estimations in electric vehicle (EV) battery management systems. Accurate SOC estimation can prevent overcharge and discharge of the battery, improve discharge efficiency and prolong cycle life. A high-precision SOC estimation method based on the gas-liquid dynamics (GLD) model is presented. The standard particle filter (PF) and the unscented particle filter (UPF) algorithm are successively used for optimizing and improving the chattering problem of the original GLD model. The results show that the two kinds of PF algorithms can effectively solve the chattering problem of the GLD model and improve the estimation accuracy. Among them, UPF has higher accuracy and the maximum estimation error is less than 2%. The PF method for estimating SOC introduced in this paper has the characteristics of high precision and large amount of calculation. Due to the above characteristics, this method can be used as a cloud SOC calculation method to periodically correct the local SOC of the vehicle.

State of charge estimation for lithium-ion batteries based on fractional order multiscale algorithm

The accuracy of state of charge (SoC) plays a critical role in lithium-ion batteries management system, whereas it gradually reduces as ohmic internal resistance growing and maximum available capacity decreasing with battery aging, which is still an open and challenging problem in SoC estimation. To tackle this issue, a multi-scale estimation algorithm is proposed by incorporating fractional order adaptive extended Kalman filter and variable forgetting factor recursive least square (FOAEKF-VFFRLS). Firstly, in order to describe the physicochemical reactions inside battery accurately, a fractional order model of lithium-ion battery with its discrete form is introduced, and the resistor-capacitor (RC) values as well as the order of capacitors are obtained by employing ant colony algorithm (ACA). Secondly, for enhancing SoC estimation accuracy, a fractional order adaptive extended Kalman filter algorithm (FOAEKF) is proposed to continuously modify the noise statistical characteristics while utilizing observation data information. Thirdly, to predict ohmic internal resistance and maximum available capacity for battery, a variable forgetting factor recursive least square algorithm (VFFRLS) is established based on the fact that forgetting factor determined by individual data error would be suddenly changes in the case of inaccurate or perturbed data. Besides, to further improve the accuracy of SoC estimation, a multi-scale estimation approach is proposed in light of the slow variation of battery internal resistance and the fast change of SoC. Finally, through comparative experiments, it is proved that the proposed approach has comprehensive strengths. First, the minimum root mean square error (RMSE) of SoC estimation error is controlled within 0.52 %. Second, terminal voltage error is kept small while limiting SoC errors within narrow ranges. Third, favorable robustness in terms of SoC estimation is achieved despite SoC initial value is mis-initialized.

A Hybrid Method for State-of-Charge Estimation for Lithium-Ion Batteries Using a Long Short-Term Memory Network Combined with Attention and a Kalman Filter

A battery management system (BMS) is an important link between on-board power battery and electric vehicles. The BMS is used to collect, process, and store important information during the operation of a battery pack in real time. Due to the wide application of lithium-ion batteries in electric vehicles, the correct estimation of the state of charge (SOC) of lithium-ion batteries (LIBS) is of great importance in the battery management system. The SOC is used to reflect the remaining capacity of the battery, which is directly related to the efficiency of the power output and management of energy. In this paper, a new long short-term memory network with attention mechanism combined with Kalman filter is proposed to estimate the SOC of the Li-ion battery in the BMS. Several different dynamic driving plans are used for training and testing under different temperatures and initial errors, and the results show that the method is highly reliable for estimating the SOC of the Li-ion battery. The average root mean square error (RMSE) reaches 0.01492 for the US06 condition, 0.01205 for the federal urban driving scheme (FUDS) condition, and 0.00806 for the dynamic stress test (DST) condition. It is demonstrated that the proposed method is more reliable and robust, in terms of SOC estimation accuracy, compared with the traditional long short-term memory (LSTM) neural network, LSTM combined with attention mechanism, or LSTM combined with the Kalman filtering method.

State of charge estimation of lithium‐ion battery based on extended Kalman filter and unscented Kalman filter techniques

AbstractLithium‐ion batteries (LIBs) are currently the most widely used energy storage technologies in electric vehicles (EVs) due to their higher power density, greater energy density, longer life, fast charging capabilities, and low discharge rate. The brain of rechargeable batteries is referred to be the battery management system (BMS). The operation of the battery is monitored & regulated by an electronic system called BMS. Most significantly, it prevents the battery from exceeding its safety limits. One of the important parameters of BMS is the state of charge (SOC); it determines the battery's remaining capacity of accessible stored energy, battery lifespan, and cell balancing. As a result, an accurate SOC calculation is essential for BMS, and it is used for safer operations that avoid overcharging and discharging for the optimum battery life. The primary ambition of this article is to find an accurate and robust SOC estimation based on extended Kalman filter (EKF) and unscented Kalman filter (UKF) techniques. RMS error, average error, minimum error, and maximum error are used to compare the EKF method and UKF method. Two resistor‐capacitor (2RC) equivalent circuit model has been considered for the dynamic performance of the battery. The waveforms are displayed, and errors are simulated for SOC estimate using the EKF and UKF algorithms, taking the same initial SOC into consideration at a time. The UKF algorithm outperforms the EKF approach in terms of dynamic results when comparing the two methods.

State of charge estimation of lithium-ion battery based on improved forgetting factor recursive least squares-extended Kalman filter joint algorithm

In order to solve the problem that forgetting factor recursive least squares (FFRLS) is prone to abnormal jitter and even divergence under complex working conditions, improved forgetting factor recursive least squares based on dynamic constraint and parameter backtracking is proposed. A joint algorithm of improved forgetting factor recursive least squares and extended Kalman filter (EKF) is used to estimate the state of charge (SOC) of lithium-ion battery. Firstly, parameters of Thevenin equivalent circuit model are identified on-line by the improved FFRLS considering dynamic constraint and parameter backtracking, and then the SOC of lithium-ion battery is estimated by extended Kalman filter. The results show that the improved forgetting factor recursive least squares has high accuracy of battery model parameters identification and the joint algorithm also has high accuracy and robustness of SOC estimation under dynamic stress test (DST) condition, the maximum absolute SOC estimation error is 2.49 % and the average absolute SOC estimation error is 1.39 %.

State-of-charge estimation for lithium-ion batteries of electric vehicle based on sensor random error compensation

Accurate state of charge (SOC) estimation is essential for ensuring the security and efficiency of lithium-ion batteries. Generally, the signal detected by a sensor inevitably has noise and drift. Therefore, the prediction process will indefinitely accumulate this random error, resulting in lower estimation accuracy, which is particularly prominent when the performance of vehicle sensors is poor. To solve this problem, a method combining an adaptive extended Kalman filter (AEKF) with a long short-term memory (LSTM) network is proposed in this paper. The original data with random errors observed in each period is denoised by the AEKF; then, the processed values are served as the input of the LSTM neural network. In this way, it avoids the disadvantage that the LSTM depends too much on the accuracy of training data and retains the advantages of the high robustness of the AEKF and strong nonlinear characteristics of the LSTM network. The proposed method is compared with an LSTM method without pre-processing the input data on the New European Driving Cycle (NEDC) and the latest World Light Vehicle Test Cycle (WLTC) datasets. Subsequently, the experimental results are analysed and compared with other algorithms under various initial noise, temperatures, and unknown initial SOC conditions. The results show that the accuracy of SOC estimation is improved a lot in the case of compensating the random input error by the AEKF in advance in comparison with feeding it into a neural network algorithm directly, converging quickly under inaccurate initial conditions and having strong robustness against noise and other factors. In particular, the root-mean-square error is less than 0.6 %, and the maximum error is less than 1.6 %.

A Novel Bias Compensated Recursive Least Squares and Multi Innovation Unscented Kalman Filtering Algorithm Method for Accurate State of Charge Estimation of Lithium-ion Batteries

A Novel Bias Compensated Recursive Least Squares and Multi Innovation Unscented Kalman Filtering Algorithm Method for Accurate State of Charge Estimation of Lithium-ion Batteries

A Review of Critical State Joint Estimation Methods of Lithium-Ion Batteries in Electric Vehicles

Battery state of charge (SOC), state of health (SOH), and state of power (SOP) are decisive factors that influence the energy-management system (EMS) performance of electric vehicles. However, the accurate estimation of SOC, SOH, and SOP remains a challenge due to the high nonlinearity of the battery dynamic characteristics and the strong coupling among the states. In this paper, different methods of single-state and two-state joint estimation are classified and discussed, including SOC/SOH and SOC/SOP joint estimation methods, and their advantages and limitations are analyzed. On this basis, key issues of joint multi-state estimation are discussed, and suggestions for future work are made.

State-of-Charge Estimation for Lithium-Ion Batteries Using Residual Convolutional Neural Networks.

State-of-charge (SOC) is a relative quantity that describes the ratio of the remaining capacity to the present maximum available capacity. Accurate SOC estimation is essential for a battery-management system. In addition to informing the user of the expected usage until the next recharge, it is crucial for improving the utilization efficiency and service life of the battery. This study focuses on applying deep-learning techniques, and specifically convolutional residual networks, to estimate the SOC of lithium-ion batteries. By stacking the values of multiple measurable variables taken at many time instants as the model inputs, the process information for the voltage or current generation, and their interrelations, can be effectively extracted using the proposed convolutional residual blocks, and can simultaneously be exploited to regress for accurate SOCs. The performance of the proposed network model was evaluated using the data obtained from a lithium-ion battery (Panasonic NCR18650PF) under nine different driving schedules at five ambient temperatures. The experimental results demonstrated an average mean absolute error of 1.260%, and an average root-mean-square error of 0.998%. The number of floating-point operations required to complete one SOC estimation was 2.24 × 106. These results indicate the efficacy and performance of the proposed approach.

Evaluation of Various Offline and Online ECM Parameter Identification Methods of Lithium-Ion Batteries in Underwater Vehicles.

For underwater vehicles, the state of charge (SOC) of battery is often used to guide the optimal allocation of energy. An accurate SOC estimation can improve work efficiency and reliability of underwater vehicles. Model-based SOC estimation methods are still mainstream routes used in practical applications. Hence, accurate battery models are highly desirable, which depends not only on the circuit structure but also on the circuit parameters. Four-parameter identification algorithms, offline mechanism-based and least squared (LS) methods, as well as online recursive least-squares with forget factor (FFRLS) and extended Kalman filter (EKF) methods were analyzed in terms of SOC estimation under three different conditions. The results revealed that in the case without any disturbance, the predicted SOCs based on four-parameter identification circuits fitted well with the reference. Moreover, it is remarkable that the LS offline methods work better than the FFRLS online routes. In addition, the robustness has also been accessed through the other two conditions, i.e., measurement data with disturbance and initial SOC value with deviation. The results showed that maximum errors of SOC estimation based on the EKF approach are significantly lower than those of the other methods, and the values are 0.51% and 0.20%, respectively. Thus, the circuit model based on the EKF parameter identification approach possessed a stronger anti-interference performance during the SOC estimation process. This research can provide corresponding theoretical support on ECM parameter identification for lithium-ion batteries in underwater vehicles.

An optimized relevant long short-term memory-squared gain extended Kalman filter for the state of charge estimation of lithium-ion batteries

Accurate state of charge (SOC) estimation of lithium-ion batteries by the battery management system (BMS) plays a prominent role in ensuring their reliability, safe operation, and acceptable durability in smart devices, electric vehicles, etc. In this paper, the effect of the training and testing working conditions on the accuracy of the SOC using a long short-term memory (LSTM) network is studied through transfer learning. Then, a relevant attention mechanism is introduced as a data optimizer for faster training of the LSTM network to establish a relevant LSTM (RLSTM). Finally, the SOCs estimated by the RLSTM are independently input with the working current to an extended Kalman filter (EKF) and a proposed squared gain EKF (SGEKF) method to iteratively denoise and optimize the accuracy of the final SOC under the three complex working conditions. The results show that the SOC estimation accuracy is influenced by the training and testing working conditions using the LSTM network, which provides a technique for accurate SOC estimation. Also, the established RLSTM network is computationally efficient for accurate SOC estimation. Moreover, the proposed hybrid RLSTM-SGEKF model has an overall maximum mean absolute error, mean squared error, root mean squared error, and mean absolute percentage error values of 0.35299%, 0.0017448%, 0.41765%, and 2.34403%, respectively, under the three complex working conditions. The proposed hybrid RLSTM-SGEKF model is optimal, robust, and computationally efficient for accurate SOC estimation of lithium-ion batteries for real-time BMS applications.

A Hybrid Data-Driven Method for State-of-Charge Estimation of Lithium-Ion Batteries

As a portable energy storage system, lithium-ion batteries (LIBs) are widely used in wireless sensor networks, electric vehicles and other fields. To ensure the continuity of power supply, it is necessary to monitor the state of charge (SOC) of LIBs. However, due to the nonlinearity of battery operation, accurate SOC estimation has become a challenging task. In this paper, a SOC estimation method based on long-term short-term memory (LSTM) network and improved particle filter (IPF) is proposed, which maps the easily observed voltage, current and temperature to the target SOC. Firstly, through a layer of the LSTM network, the timing characteristics of the data are fully utilized to obtain the SOC variation trend of LIBs. Then, the noise variance adaptive algorithm and particle distribution optimization algorithm are introduced to improve the standard particle filter (PF). On this basis, the estimation results of the LSTM network are optimized by IPF. In addition, the performance of the proposed LSTM-IPF method is compared with other methods. The results show that the estimation performance of the proposed model is excellent, and the root mean squared error (RMSE) and maximum error (MAX) are controlled below 1% and 2% respectively, which meets the requirements of SOC estimation and verifies the effectiveness of the proposed method.

A Multi-Scale Time Method for the State of Charge and Parameter Estimation of Lithium-Ion Batteries Using MIUKF-EKF

Accurate state estimation is essential for the safe and reliable operation of lithium-ion batteries. However, the accuracy of the battery state estimation depends on the accuracy of the battery parameters. Because the state of charge (SOC) cannot be directly measured, estimation methods based on the Kalman filter are widely used. However, it is difficult to estimate SOC online and get high accuracy results. This article proposes a method for parameter identification and SOC estimation for lithium-ion batteries. Because the lithium-ion battery has slow-varying parameters (such as internal resistance, and polarization resistance), and the SOC has fast-varying characteristics, so a multi-scale multi-innovation unscented Kalman filter and extended Kalman filter (MIUKF-EKF) are used to perform online measurement of battery parameters and SOC estimation in this method. The battery parameters are estimated with a macro-scale, and the SOC is estimated with a micro-scale. This method can improve the estimation accuracy of the SOC in real-time. Results of experiments indicate that the algorithm has higher accuracy in online parameter identification and SOC estimation than in the dual extended Kalman filter (DEKF) algorithm.

A combined state-of-charge estimation method for lithium-ion battery using an improved BGRU network and UKF

Lithium-ion batteries have become the fastest-growing energy storage equipment available for extrinsic and intrinsic reasons. State of Charge (SOC) is one of the lithium-ion batteries' most critical performance indicators, reflecting the remaining capacity. An accurate and stable estimate of SOC is critical for any lithium-ion battery. This paper proposes a hybrid method to achieve stable and real-time battery SOC estimation at different temperatures, composed of an Improved Bidirectional Gated Recurrent Unit (IBGRU) network and Unscented Kalman filtering (UKF). The proposed method is experimentally validated using data from UDDS and US06 driving cycles. The verification results show that the method can adapt to various working conditions and obtain good estimation accuracy and robustness, with MAE and RMSE less than 0.83% and 1.12%, respectively. After transfer learning, the method can also be applied to new lithium-ion batteries and achieve good estimation performance at new temperature conditions. The maximum errors are 4.98% and 5.76% at 25 °C and −10 °C, respectively. Therefore, the IBGRU-UKF method can achieve a more accurate and stable SOC estimation with good expansion performance for different lithium-ion batteries.

State-of-charge estimation for lithium-ion battery during constant current charging process based on model parameters updated periodically

With online established battery model, model-based estimation method can track battery state-of-charge (SOC) precisely under dynamic conditions. Nevertheless, both recursive least square-based and filter-based methods cannot distinguish whether the voltage difference comes from SOC difference or internal resistance difference during constant current (CC) conditions, further leading to erroneously identified model parameters and inaccurate SOC estimation. To address this issue, a novel SOC estimation method during CC charging process by fusion of global optimization algorithm and Kalman filter family algorithm is developed in this paper. Firstly, some key parameters that are helpful for initialization and lower/upper bounds setting for global optimization method are extracted from electric vehicles’ driving process. Secondly, considering the shortcomings in traditional global optimization methods, including possible premature convergence, slow search speed in the late stage and relatively large computational cost, an improved particle swarm optimization is designed to periodically update model parameters during CC charging process. With obtained model parameters, SOC is further tracked via extended Kalman filter (EKF). The verification results based on experimental data demonstrates that the developed method can significantly weaken the strong cross-interference between model parameters and SOC, further achieving much more accurate SOC estimation than existing dual/joint EKF during CC charging process.

State of Charge Estimation for Lithium-Ion Batteries Using Simple Recurrent Units and Unscented Kalman Filter

Accurate estimation of the state of charge plays a very important role in ensuring the safe and effective operation of battery lithium-ion batteries and is one of the most important state parameters. However, the estimation method of state of charge has various limitations, so it is of great significance to improve the accuracy and calculation speed of the method. In this article, we propose an improved recurrent neural network model to estimate lithium-ion battery state of charge. Simple recurrent units are used to replace the traditional recurrent neural network basic unit or long short-term memory unit, and the computation speed is improved by implementing parallel processing. Finally, the prediction results of the model are fed into an unscented Kalman filter module to remove the interference of noise on the prediction. This article studies the prediction accuracy and speed of Samsung INR 18650-20R and INR 18650-25R under various ambient temperatures, initial state of charge values, and electric vehicle drive cycles. The results show that the proposed method can obtain accurate state of charge estimation results in the INR 18650-20R data set. For different temperatures and initial SOC, the root mean square error is less than 0.015 and 0.016, and the prediction speed is about 30% higher than that of long short-term memory. In the INR 18650-25R data set, for three different driving cycles, the root mean square error is less than 0.034, and the average test speed is about 2.7s, which proves the effectiveness of this method in estimating accuracy and speed.