State Of Charge Value Research Articles

State of Charge and State of Health Assessment of Viologens in Aqueous-Organic Redox-Flow Electrolytes Using In Situ IR Spectroscopy and Multivariate Curve Resolution.

Aqueous‐organic redox flow batteries (RFBs) have gained considerable interest in recent years, given their potential for an economically viable energy storage at large scale. This, however, strongly depends on both the robustness of the underlying electrolyte chemistry against molecular decomposition reactions as well as the device's operation. With regard to this, the presented study focuses on the use of in situ IR spectroscopy in combination with a multivariate curve resolution approach to gain insight into both the molecular structures of the active materials present within the electrolyte as well as crucial electrolyte state parameters, represented by the electrolyte's state of charge (SOC) and state of health (SOH). To demonstrate the general applicability of the approach, methyl viologen (MV) and bis(3‐trimethylammonium)propyl viologen (BTMAPV) are chosen, as viologens are frequently used as negolytes in aqueous‐organic RFBs. The study's findings highlight the impact of in situ spectroscopy and spectral deconvolution tools on the precision of the obtainable SOC and SOH values. Furthermore, the study indicates the occurrence of multiple viologen dimers, which possibly influence the electrolyte lifetime and charging characteristics.

State of charge estimation of Power lithium-ion battery based on an Affine Iterative Adaptive Extended Kalman Filter

State of charge (SOC) is a very important parameter for power lithium-ion battery in battery operation, but it cannot be measured directly, so it needs to be accurately estimationed. Considering the time-varying characteristics of the model parameters of the power lithium-ion battery, an online identification algorithm called Adaptive Forgetting Factor Recursive Augmented Least Squares (AFFRALS) is proposed. It can obtain a more accurate model compared with the offline method (fixed model parameters) by considering that the noise of power lithium-ion battery system is commonly non-Gaussian white noise in practice, which is different from the existing equivalent circuit models. Then, an Affine Iterative Adaptive Extended Kalman Filter (AIAEKF) method is proposed to deal with the non-Gaussian white noise and accelerate the convergence rate of the estimated results when the initial SOC value is wrong. Experiments demonstrate the effective of the online identification method as well as the SOC estimation method. This method shows a faster convergence rate and better SOC estimation performance than the traditional Extend Kalman Filter (EKF) method. The RMSE of the SOC estimation results is less than 0.023 except for 0 °C, which rarely occurs in practice.

DEKF-based SOC estimation study for lithium batteries

Abstract In view of the low estimation accuracy of single extended Kalman filter algorithm (EKF) and the influence of the initial value of state of charge (SOC), the double extended Kalman filter algorithm (DEKF) is used for online estimation of battery model parameters and SOC based on the establishment of second-order RC equivalent circuit model. A 36V 32A·h lithium iron phosphate battery pack is used as the research object, and experiments are designed for the actual battery conditions. The experimental results show that the DEKF algorithm has better convergence and adaptability under different conditions than a single EKF algorithm with inaccurate initial values of SOC, and the error is controlled within 5%, which proves the feasibility and effectiveness of the algorithm.

Continual Refoircement Learning Using Real-World Data for Intelligent Prediction of SOC Consumption in Electric Vehicles

The accelerated migration towards electric vehicles (EV) presents several problems to solve. The main aspect is the management and prediction of the state of charge (SOC) in real long-range routes of different variations in altitude for a more efficient energy consumption and vehicle recharge plan. This paper presents the implementation of a new algorithm for SOC estimation based on continuous learning and meta-experience replay (MER) with reservoir sample. It combines the reptile meta-learning algorithm with the experience replay technique for stabilizing the reinforcement learning. The proposed algorithm considers several important factors for the prediction of the SOC in EV such as: speed, travel time, route altimetry, consumed battery capacity, regenerated battery capacity. A modified principal components analysis is used to reduce the dimensionality of the route altimetry data. The experimental results show an efficient estimation of the SOC values and a convergent increase in knowledge while the vehicle travels the routes.

Consensus-based energy management of multi-microgrid: An improved SoC-based power coordinated control method

The direct current(DC) multi-microgrid(MMG) with island operation mode is a system formed by interconnecting DC multiple microgrids, which is more complicated than a single microgrid. First of all, this paper proposes the modeling method of the power regulation cost for each controllable unit to guide their power regulation order, which reduces the power regulation cost. Second, the hierarchical control strategy is proposed. The top-level control allocates power through the consensus algorithm to accelerate the convergence rate of the state of charge(SoC) of energy storage batteries(ESBs). The convergence of SoC reduces the switching times of working mode of MMG system caused by the deviation of SoC from the normal working range, which further reduces the cost of power regulation. The bottom-level control performs constant power control on each controllable unit according to the load rate of ESB obtained by top-level control. Furthermore, the correction scheme of measured SoC value is proposed according to its predicted value when the measured SoC value of ESB is inaccurate, which ensures the stability of MMG system. Finally, the example simulation shows that the consensus algorithm can effectively solve the real-time power distribution problem of MMG.

A Data-Driven Model Framework Based on Deep Learning for Estimating the States of Lithium-Ion Batteries

The accurate estimation of state of charge (SOC) and state of health (SOH) of lithium-ion battery is crucial to ensure the safe and stable operation of the battery. In this paper, a data-driven model framework based on deep learning for estimating SOC and SOH is proposed, which mainly consists of long short-term memory (LSTM) neural network and back propagation (BP) neural network. The switch between SOC estimation model and SOH estimation model can be realized by adjusting the output mode of LSTM. When estimating SOC, the LSTM is set to have corresponding output at each input. The model takes 10 consecutive voltage sampling points as input and the estimated value of SOC at the last sampling moment as output. When estimating SOH, the LSTM is set to have a corresponding output only at the last input. The model takes the sequence of 150 sampling points on the charging voltage curve as input and the SOH value at the current cycle as output. Experiments are carried out on the Oxford battery degradation dataset, and the results show that the proposed model framework can obtain accurate and reliable estimates of SOC and SOH.

Co‐Doping Strategies to Improve the Electrochemical Properties of LixMn2O4 Cathodes for Li‐Ion Batteries

AbstractFour novel cathode electrode materials with improved material properties have been derived from the Lithium Manganese Oxide spinel using co‐doping strategies. Specifically, Aluminum, Nickel, Magnesium, and Yttrium were selected as the primary dopant to replace a fraction of Mn3+ (5 %), and S2− was selected as the secondary dopant to replace 1 % of O2−. A combination of quantum mechanics and molecular dynamics was used to study the fracture mechanics of the new materials for various State of Charge values, and improved performance is validated with experimental data. The results show that lattice constant values for all the doped structures decrease by 1.87 %–2.07 %. Overall, with co‐doping, the diffusion properties improved, and activation energy required for Li+ vacancy migration reduced (0.21–0.25 eV). We conclude that with reduced inter‐atomic distance, the overall life of the LMO spinel can be improved. The Computational Fluid Dynamics simulations to study the macro‐scale behaviour of these new materials shows a reduction in intercalation induced stress and heat generation.

State of charge estimation at different temperatures based on dynamic thermal model for lithium-ion batteries

In the existing modeling and state of charge (SOC) estimation of lithium batteries, the effect of ambient temperature is often ignored, which brings large errors to the model parameter identification, so the battery modeling and SOC estimation under temperature variation are performed. In this paper, an improved dual-polarized dynamic thermal model is proposed, firstly, the thermal effect of lithium battery is analyzed, and the model parameters adopt the dynamic values of coupling temperature and SOC, and the model is improved in terms of both heat generation mechanism and heat transfer characteristics; then the model parameters are identified based on the least-squares method with variable forgetting factor (VFF-RLS). Finally, the SOC at different temperatures is evaluated based on the dynamic thermal model combined with the extended Kalman filtering (EKF) with nonlinear filtering capability. The results show that the temperature factor is an important and non-negligible influence factor in modeling, the proposed battery model in this paper has high computational accuracy, and the proposed SOC estimation method has better SOC estimation capability for the temperature variation problems.

Application of Machine Learning in Battery: State of Charge Estimation Using Feed Forward Neural Network for Sodium-Ion Battery

Estimating the accurate State of Charge (SOC) of a battery is important to avoid the over/undercharging and protect the battery pack from low cycle life. Current methods of SOC estimation use complex equations in the Extended Kalman Filter (EKF) and the equivalent circuit model. In this paper, we used a Feed Forward Neural Network (FNN) to estimate the SOC value accurately where battery parameters such as current, voltage, and charge are mapped directly to the SOC value at the output. A FNN could self-learn the weights with each training data point and update the model parameters such as weights and bias using a combination of two gradient descents (Adam). This model comprises the Dropout technique, which can have many neural network architectures by dropping the neuron/mode at each epoch/training cycle using the same weights and biases. Our FNN model was trained with data comprising different current rates and tested for different cycling data, for example, 5th, 10th, 20th, and 50th cycles and at a different cutoff voltage (4.5 V). The battery used for estimating the SOC value was a Na-ion based battery, which is highly non-linear, and it was fabricated in a house using Na0.67Fe0.5Mn0.5O2 (NFM) as a cathode and Na metal as a reference electrode. The FNN successfully estimated the SOC value for the highly non-linear nature of the Na-ion battery at different current rates (0.05 C, 0.1 C, 0.5 C, 1 C, 2 C), for different cycling data, and at higher cut-off voltage of –4.5 V Na+, reaching the R2 value of ~0.97–~0.99, ~0.99, and ~0.98, respectively.

A novel estimation of state of charge for the lithium-ion battery in electric vehicle without open circuit voltage experiment

The estimation of the state-of-charge(SOC) based on equivalent circuit model(ECMs) for the lithium-ion battery has been widely adopted. The relationship between the open-circuit voltage(OCV) and the SOC is essential for the ECMs, which is commonly obtained through the incremental OCV(IO) test or the low-current OCV(LO) test and requires a long experimental time. Meanwhile, the SOC is usually defined as the ratio of the remaining capacity to the nominal capacity, which reduces the accuracy of SOC estimation owing to the change of the available capacity under actual conditions. In this paper, the recursive least square with forgetting factor(FFRLS) is applied to identify the parameters including the OCV values based on the Thevenin model. Afterwards, the differential equations of the OCV and available capacity with respect to time are established, respectively. And then an adaptive extended Kalman filter(AEKF) is used to identify the OCV and available capacity. The estimated OCV and available capacity are input to a second AEKF for SOC estimation, and neither IO nor LO test is required. The proposed method is verified by experiments. The results indicate that the estimated SOC presents high accuracy and good robustness to the noises and different initial SOC values at different temperatures.

Distributed Optimal Control of DC Microgrid Considering Balance of Charge State

State-of-charge (SoC) imbalance and bus voltage deviation are two of the main problems in autonomous dc microgrids. Based on this concern, this paper presents an improved dual-quadrant SoC weighted control strategy and a distributed optimization control method to achieve SoC balance, ensuring accurate power-sharing and bus voltage recovery. Firstly, this paper couples the injected/released power with the current SoC and observed average SoC value to weight the droop coefficient, which is based on the charge/discharge mode for the energy storage system. Then a secondary controller is designed based on distributed optimal control to eliminate the bus voltage deviation caused by the line impedance difference. The proposed optimal control method optimizes the average bus voltage to the nominal value and achieve accurate power-sharing by constructing the correlated variables and voltage independent intermediate variables exchanged among bulk energy storage units (ESUs). Since the voltage observer cannot accurately observe the true average bus voltage under the communication delay, the proposed distributed optimal control method without the voltage observer can ensure that the average bus voltage is optimized to the nominal value, thus improving the robustness of the control system. Finally, the correctness and effectiveness of the proposed method are verified in Simulink/MATLAB.

Online SoC Estimation of Lithium-Ion Batteries Using a New Sigma Points Kalman Filter

The accurate state of charge (SoC) online estimation for lithium-ion batteries is a primary concern for predicting the remaining range in electric vehicles. The Sigma points Kalman Filter is an emerging SoC filtering technology. Firstly, the charge and discharge tests of the battery were carried out using the interval static method to obtain the accurate calibration of the SoC-OCV (open circuit voltage) relationship curve. Secondly, the recursive least squares method (RLS) was combined with the dynamic stress test (DST) to identify the parameters of the second-order equivalent circuit model (ECM) and establish a non-linear state-space model of the lithium-ion battery. Thirdly, based on proportional correction sampling and symmetric sampling Sigma points, an SoC estimation method combining unscented transformation and Stirling interpolation center difference was designed. Finally, a semi-physical simulation platform was built. The Federal Urban Driving Schedule and US06 Highway Driving Schedule operating conditions were used to verify the effectiveness of the proposed estimation method in the presence of initial SoC errors and compare with the EKF (extended Kalman filter), UKF (unscented Kalman filter) and CDKF (central difference Kalman filter) algorithms. The results showed that the new algorithm could ensure an SoC error within 2% under the two working conditions and quickly converge to the reference value when the initial SoC value was inaccurate, effectively improving the initial error correction ability.

A GA-based online real-time optimized energy management strategy for plug-in hybrid electric vehicles

The performance of plug-in hybrid electric vehicles (PHEVs) depends on the energy management strategy (EMS). An optimal EMS can maximize energy utilization by coordinating the battery energy and fuel consumption. However, the real-time EMS remains a challenge due to the multiple dynamic parameters. To this end, this paper proposed a real-time EMS based on the adaptive regulation of multiple parameters including the driving cycle, driving distance and battery state of charge (SOC). First, the rule-based EMS (RB-EMS) is designed, in which the operative threshold of the charge depleting mode (CD) and charge sustaining (CS) mode are initially determined through engineering experience considering the engine and battery SOC characteristics. Meanwhile, equivalent consumption minimization strategy (ECMS) is utilized to replace the traditional rules in the CD-CS region, which benefits to find the real-time optimal solution in a wider range and simultaneously addresses the fixed torque distribution between the engine and motor in the RB strategy. After then, to improve the adaptability of EMS in different driving cycles, the threshold in the RB strategy and equivalent factor (s(t)) in the ECMS strategy with different driving distances and initial SOC values are optimized with a global genetic algorithm (GA). Besides, a driving cycle recognition algorithm (DCR) based on fuzzy logic is online executed with GPS and onboard sensors, with which the optimal engine and motor torque distribution under the current conditions are adaptively determined according to the identified driving cycles and the above-mentioned offline results. Finally, the proposed adaptive hybrid energy management (HEM) approach was validated by the numerical simulation and hard-in-loop (HIL) experiments. Results show that the proposed HEM strategy can adaptively change the dynamic parameters to achieve the optimized CD stage in real-time; meanwhile, it can achieve a suboptimal result compared with DP which has reduced the fuel consumption by 14.82% for high power demand level.

Reduced-Order Modeling of a Lithium-Ion Lithium Iron Phosphate Battery

Battery energy storage systems are essential for stabilizing the intermittent power generation of renewable energy (RE) technologies. Their integration into RE systems is typically studied using energy systems modeling software that utilize either idealized models or complex models that require experimental data. Reduced order modeling offers minimal experimental costs through the use of a multiphysics model in lieu of experimental battery data. In this work, a previously reported multiphysics model of a lithium ion lithium iron phosphate (Li ion LFP) battery was simulated in COMSOL Multiphysics® and reduced into an equivalent circuit model (ECM). The reduced order ECM was then implemented as a battery systems model in an energy systems modeling tool to perform RE-based hybridization studies. Techno economic case studies were conducted on RE based systems powering a household and an off grid island to validate the reduced order ECM with the idealized battery model with HOMER Pro. Optimal component sizes computed using the two software generally showed good agreement and deviations were attributed to electrical losses. The state of charge (SOC) vs. time graphs generated by the two software had an average root mean square error of 0.00173 SOC units across the different case studies. Discrepancies were observed during rapid charging or high SOC values, which were characteristic of the reduced order ECM. This model reduction framework can be applied to other energy storage and conversion technologies, such as other Li ion chemistries, fuel cells, and supercapacitors, to generate chemistry specific models for energy systems research.

Reliability improvement of wind power frequency modulation based on look‐ahead control strategy and stage of charge optimization of energy storage

Aiming at the continuous frequency modulation (FM) dynamic process of the current wind turbine (WT), FM reliability is affected by wind speed fluctuation and prediction error. This study proposes an advanced energy storage (ES) control strategy based on wind speed prediction. First, according to the wind speed fluctuation in a future forecast period, the model predictive control (MPC) method is used to modify the current charging and discharging behavior in advance to improve the FM reliability of WT in the forecast period. Second, considering the ES capacity limitation, an adaptive ES dynamic state of charge (SOC) two-layer optimization model is proposed. In the outer layer of the model, an optimization function is designed to automatically adjust the initial SOC value with the change in wind speed to maintain the optimal FM capability of the wind-storage system at different wind speeds. In the inner layer, the ES charge-discharge speed model is constructed to avoid the loss of ES life caused by overcharge and over-discharge in the continuous FM process. The simulation results show that in the continuous perturbation, maximum frequency deviation is improved by 0.035 Hz in the first perturbation and 0.036 Hz in the second perturbation compared with the non-optimization model. Besides, the deviation degree of system frequency decreased by 0.082 Hz under continuous load disturbance. The strategy and dynamic SOC optimization model can effectively improve the FM reliability.

A Method for Estimating State of Charge of Lithium-Ion Batteries Based on Deep Learning

State of charge (SOC) estimation of lithium-ion batteries is a problem of time series. In deep learning methods, both convolutional neural network (CNN) and recurrent neural network (RNN) can be used to solve such problems. In this paper, based on deep learning, a hybrid neural network model is proposed to estimate the SOC of lithium-ion batteries by taking the sequence of sampling points of voltage, current and temperature as input. The model is mainly composed of three modules, namely, convolutional module, ultra-lightweight subspace attention mechanism (ULSAM) module and the gated recurrent unit (GRU) module. Convolutional module and ULSAM module are responsible for extracting the feature information from the sequence of sampling points and outputting feature maps. GRU module is responsible for processing the sequences of the feature maps and outputting the value of SOC. The proposed model is tested on the public NASA Randomized Battery Usage dataset and Oxford Battery Degradation dataset. The experimental results show that the proposed model can obtain a relatively accurate SOC estimation at unknown aging state and complex operating conditions.

Application of Machine Learning in Battery: State of Charge Estimation Using Feed Forward Neural Network for Na-Ion Batteries

Estimating accurate State-of-Charge (SOC) of a battery is important to avoid the over/under charging and protect the battery pack from low cycle life. Current methods of SOC estimation use complex equation in Extended Kalman Filter (EKF) and equivalent circuit model. In this paper, we used a Feed Forward Neural Network (FNN) to estimate SOC value accurately where battery parameters such current, voltage and charge are mapped directly to SOC value at the output. FNN has the ability to self-learn the weights with each training data points and update the model parameters like weights and bias using a combination of two gradient descent (Adam). This model comprises Dropout technique, which can have many neural network architectures by dropping the neuron/mode at each epoch/training cycle using the same weights and bias. Our FNN model was trained with data comprising different current rates and tested for different cycling data for example 5th, 10th, 20th, 50th cycle and at different cut-off voltage (4.5 V). The battery used for estimating the SOC value is Na-ion, which is highly non-linear and it was fabricated in house using Na0.67Fe0.5Mn0.5O2 (NFM) and Na metal. FNN successfully estimated the SOC value for highly non-linear nature of Na-ion battery at different current rates- 0.05C, 0.1C, 0.5C, 1C, 2C, having the R2 value of ~0.97-0.99 and ~0.99 for different cycling data and at higher cut-off voltage of -4.5 VNa+.

Parallel Balancing Battery using Adaptive Power Sharing and ANN SOC Estimator

The battery balancing method is commonly used in cell circuits and battery circuits to maintain the power continuity on the DC Bus. The power continuity on the DC Bus is guaranteed if the load continues to get a power source, even if either the battery or power supply malfunctions. Besides, the battery balancing method is also used to protect the battery from excessive charging current pliers flowing into the battery. Therefore, the State-of-Charge (SoC) should be concern in balancing the maintained battery condition on both systems and avoiding overcharging. Artificial Neural Network (ANN) is used in this paper to determine the value of battery SoC. Based on simulations using MATLAB 2018, SoC values with ANN showed accurate results with error values below 0.1%. Based on the simulation results, the two batteries, which are arranged to have a difference of SoC value of 0.3%, will achieve a balanced SoC value for 28.45 seconds from the simulation.

A novel hybrid equivalent circuit model for lithium-ion battery considering nonlinear capacity effects

In this paper, a novel hybrid equivalent circuit model for lithium-ion battery. The proposed equivalent circuit model of lithium-ion battery is based on Thevenin equivalent circuit model, and a state-of-charge (SOC) part is added into the model to improve the model performance. In the proposed model, the battery capacity is divided into available capacity and unavailable capacity, which can well reflect the rated capacity effect when the battery is working, the recovery effect when the battery is not working and the self-discharge effect at all times. At the same time, a new definition of the SOC is given. The SOC value of the battery is estimated in real time through the model of the charged state part, and the one-to-one correspondence between SOC and open circuit voltage is established. Besides, the second-order polarized RC network in Thevenin model is improved to the third-order RC network, and the third-order RC network is endowed with practical physical significance, to reflect the polarization effect of the battery more accurately. Finally, Simulink simulation and experimental results have verified the correctness of the proposed model.

MPGA-based-ECMS for energy optimization of a hybrid electric city bus with dual planetary gear

To improve the fuel economy and reduce the exhaust emissions of a hybrid electric city bus (HECB) with dual planetary gear, a vehicle model is proposed based on the coupling mechanism between engine and battery motor in the gear. Then, two kinds of adaptive equivalent consumption minimization strategy (ECMS) algorithms based on fuzzy proportional-integral (PI) controller: Fuzzy PA-ECMS and Fuzzy MPGA-ECMS (MGPA: multiple population genetic algorithm), are established to improve the control effect of ECMS with equivalent factor (EF) as the core. Firstly, an approximate expression of optimal EF is derived based on the Pontryagin’s minimum principle (PMP). Subsequently, the deviation between the reference state of charge (SOC) and the actual SOC and the corresponding variation rate are calculated, which are combined with the fuzzy logic controller to adjust the EF. Finally, the driving style is introduced to rectify the trajectory of EF. According to different driving conditions (different initial values of SOC), the PI parameters of EF are optimized offline by multiple population genetic algorithm. Meanwhile, the interval of PI parameters is continuously optimized by the fuzzy controller. Then, simulation experiments are conducted to verify the efficacy of the two proposed algorithms. The simulation results show that compared to the conventional bus and rule-based control strategy, the proposed Fuzzy PA-ECMS algorithm can improve the fuel economy of bus by 41.70% and 5.29%, respectively. Further, compared to Fuzzy PA-ECMS, Fuzzy MPGA-ECMS algorithm can improve the fuel economy of bus by 0.3%.