Estimation Of Lithium-ion Batteries Research Articles

State-of-charge estimation of lithium-ion battery using an improved neural network model and extended Kalman filter

Accurate state-of-charge (SoC) estimation is remarkably difficult due to nonlinear characteristics of batteries and complex application environment in electric vehicles (EVs), particularly low temperature and low SoC. In this paper, an improved battery model is first built using a feedforward neural network (FFNN) by introducing newly defined inputs. Based on the FFNN model and the extended Kalman filter algorithm, a FFNN-based SoC estimation method is designed, and its robustness is verified and discussed using the experimental data obtained at different temperatures. Finally, a hardware-in-loop test bench is built to further evaluate the real-time and generalization of the designed FFNN model. The results show that the SoC estimation can converge to the reference value at erroneous settings of an initial SoC error and an initial capacity error, and the SoC estimation errors can be stabilized within 2% after convergence, which applies to all the cases discussed in this paper, including low temperature and low SoC. This indicates that the FFNN-based method is an effective method to estimate SoC accurately in complex EV application environment.

Influence of Calcination Temperature on the Performance of Li and Mn Rich Li1.2Mn0.54Co0.13Ni0.13O2layered Electrode for Lithium Ion Battery

The synthesis/calcination temperature playing a significant role on the performance, safety, and cycle life-time of lithium ion batteries (LiB). In this research work, we have reported the formation of homogeneously dispersed Li1.2Mn0.54Co0.13Ni0.13O2 (LMNCO) with sub-micron spheres architectures with good crystallinity through simple cost-effective precipitation further ball milling synthesis method to enhance the performance of the LIB energy storage system. The synthesized LMNCO cathode electrode materials were calcined at different temperature such as 800, 900 and 1000 °C to examine the influence of calcination temperature on the physiochemical and performance of the electrode. Synthesized LMNCO cathode electrode materials were characterized through TGA/DTA, XRD and SEM studies. The electrochemical characteristics of the synthesized materials were analyzed, the LMNCO prepared at 900 °C was exhibited a best electrochemical performance in LIBs while the charge-discharge profile was carried out in a voltage window of 2.0–4.8 V at 0.1 C rate compared with other materials synthesized at 800 and 1000°C. The first charge/discharge capacity of LMNCO synthesized at 900°C was found to be 259.6 mAh g-1 with the coulombic efficiency of 72.3 %. However, a well capacity retention (95%) of 183.0 mAh g-1 in the 100th cycle was obtained at 1 C rate. Other cathode materials LMNCO-800°C and LMNCO-1000°C were also performed well. Figure (1) and Figure (2) References Leng, F. et al. Effect of Temperature on the Aging rate of Li Ion Battery Operating above Room Temperature. Rep. 5, 12967; doi: 10.1038/srep12967 (2015). Handel, P. et al. Termal aging of electrolytes used in lithium-ion batteries – An investigation of the impact of protic impurities and different housing materials. Power Sources 267, 255–259 (2014) Feng, L., Cher Ming, T., Rachid, Y. & Minh Duc, L. A practical framework of electrical based online state-of-charge estimation of lithium ion batteries. Power Sources 255, 423–430 (2014). Keywords: Precipitation;LMNCOsub-microspheres; charge/discharge capacity; LIBs; XRD. Figure 1

A model for state-of-health estimation of lithium ion batteries based on charging profiles

Using an equivalent circuit model to characterize the constant-current part of a charging/discharging profile, a model is developed to estimate the state-of-health of lithium ion batteries. The model is an incremental capacity analysis-based model, which applies a capacity model to define the dependence of the state of charge on the open circuit voltage as the battery ages. It can be learning-free, with the parameters subject to certain constraints, and is able to give efficient and reliable estimates of the state-of-health for various lithium ion batteries at any aging status. When applied to a fresh LiFePO4 cell, the state-of-health estimated by this model (learning-unrequired or learning-required) shows a close correspondence to the available measured data, with an absolute difference of 0.31% or 0.12% at most, even for significant temperature fluctuation. In addition, NASA battery datasets are employed to demonstrate the versatility and applicability of the model to different chemistries and cell designs.

An Improved Model Equation Based on a Gaussian Function Trinomial for State of Charge Estimation of Lithium-ion Batteries

Establishing a model equation with high accuracy and high computational efficiency is very important for the estimation of battery state of charge (SOC). To ensure better SOC estimation results, most studies have focused on the improvement of the algorithm, while the impact of the model equation which may offset the benefits of advanced algorithms has been overlooked. To address this problem, this paper studies the widely used model equations and presents a new model equation based on a Gaussian function that improves the SOC estimation accuracy and computational efficiency. With the Worldwide harmonized Light Vehicles Test Cycle (WLTC) which is highly dynamic and more realistic than any other driving cycles, the proposed model equation is applied to different filtering algorithms to validate its performance in SOC estimation. The results indicate that the proposed model equation can greatly improve the accuracy of SOC estimation without an increase of computation. In addition, for the traditional polynomial-based model equations, the 6th-order power function polynomial has better performance in SOC estimation than polynomials with other orders.

Entropy measurement of a large format lithium ion battery and its application to calculate heat generation

As large format lithium ion batteries are used in EV and ESS applications, the temperature control of a battery is important to achieve a safe and long cycle life operation. To control the temperature properly, we need to know the heat generation characteristics. Heat generation of a battery mainly depends on the applied current, internal resistance, temperature and entropy of the battery. However, it is not easy to measure the entropy, because they depend on SOC and temperature simultaneously. Here, we suggested a new way to measure the entropy, on the assumption of adiabatic condition and calculate the heat generation. Internal resistance, specific heat capacity, OCCP values are also measured experimentally. The entropy (F ∂EOC ∂T−1) at a given SOC were measured from the variation of OCCP with temperature and expressed via a polynomial equation. The temperature dependence of the internal resistance were also expressed in the same procedure. The battery temperature variations calculated from the above parameters showed a good agreement with those from experiments. This suggested method provides a simple and reliable temperature estimation of lithium ion batteries.

Sensitivity of Impedance Parameters of Li-ion Batteries Under Different State of Health

State of Health (SOH) estimation of lithium-ion battery is momentous for Battery Management Systems (BMS) in Electric Vehicle (EV). Nevertheless, one of the biggest challenges is which indicator can be used to describe the SOH considering external impact factors. In this paper, according to the accelerated testing results during the whole battery lifetime, the statistical-driven analysis method such as Path Analysis (PA) is adopted to reveal the sensitive relationship between capacity degradation and the fading law of internal impedance parameters identified from Equivalent Circuit Model (ECM). And the direct influence of each impedance parameter and indirect influence between them are analyzed, a new life indicator based on sensitive characteristic parameters is proposed, which can describe the capacity degradation characteristics of the battery as well as the power recession characteristics. Furthermore, to analysis the disturbance caused by the external factors such as environment temperature, more experiments are added and the compensating method is presented.

Fractional-Order Model-Based Incremental Capacity Analysis for Degradation State Recognition of Lithium-Ion Batteries

State of health (SOH) estimation of lithium-ion batteries is a key but challengeable technique for the application of electric vehicles. Due to the ambiguous aging mechanisms and sensitivity to the applied conditions of lithium-ion batteries, the recognition of aging mechanisms and SOH monitoring of the battery might be difficult. A novel SOH estimation and aging mechanism identification method is presented in this paper. First, considering the dispersion effect, a fractional-order model is constructed, and the parameter identification approach is proposed, and a comparison between integer-order model and fractional-order model has been done from the prospect of predicting accuracy. Then, based on the identified open-circuit voltage, the battery aging mechanism can be analyzed by the means of an incremental capacity analysis method. Moreover, the normalized incremental capacity peak is used to estimate the remaining capacity. Finally, the robustness of the SOH estimation method is validated by batteries aged at different conditions based on the idea of cross validation, and the estimation error of the remaining capacity can be reduced within 3.1%.

Estimation of state of health of lithium-ion batteries based on charge transfer resistance considering different temperature and state of charge

Abstract State of health diagnosis and estimation of lithium-ion batteries is a key feature of an advanced battery management system. Electrochemical impedance spectroscopy is frequently used for the state of health diagnosis and estimation. In the paper, the charge transfer resistance of the battery is obtained by fitting the impedance spectroscopy with an equivalent impedance model to estimate state of health. And an analytical calculation model with temperature and state of charge as inputs is derived and verified considering their effect on the charge transfer resistance. With the calculation model, the charge transfer resistance at randomly selected state of charge and temperature is converted to the standard state to be comparable for the state of health estimation. It is indicated that state of health estimated with the converted and the direct fitted charge transfer resistance agree well. The battery state of health estimation method with the converted charge transfer resistance eliminates the need to specifically control the temperature and state of charge during the impedance spectrum measurement. It benefits the practical application of the state of health estimation with electrochemical impedance spectroscopy.

Convolutional Gated Recurrent Unit–Recurrent Neural Network for State-of-Charge Estimation of Lithium-Ion Batteries

For most deep learning practitioners, recurrent networks are often used for sequence modeling. However, recent researches indicate that convolutional architectures may be used to optimize recurrent networks on some machine translation tasks. Problems here are which architecture we should use for a new sequence modeling. By integrating and systematically evaluating the general convolution and recurrent architecture used for sequence modeling, a convolution gated recurrent unit (CNN-GRU) network is proposed for the state-of-charge (SOC) estimation of lithium-ion batteries in this paper. Deep-learning models are well suited for SOC estimation because a battery management system is time-varying and non-linear. The CNN-GRU model is trained using data collected from the battery-discharging processes, such as the dynamic stress test and the federal urban driving schedule. The experimental results show that the proposed method can achieve higher estimation accuracy than two commonly used deep learning models (recurrent neural network and gated recurrent unit) and two traditional machine learning approaches (support vector machine and extreme learning machine) for SOC estimation of lithium-ion batteries.

SOC Oriented Electrochemical-Thermal Coupled Modeling for Lithium-Ion Battery

This paper proposes an electrochemical-thermal coupling model for state of charge (SOC) estimation of lithium-ion batteries (LIBs). In this paper, to solve two numerical problems in the quasi two-dimensional model of LIBs, a simplified one-dimensional model is built. Considering that the parameters in the model are affected by temperature, the internal parameters of the battery which obey the Arrhenius equation are introduced. Based on the simplified one-dimensional model, the electrochemical-thermal coupling model is built. Finally, an adaptive SOC observer based on electrochemical-thermal coupling model is designed. The simulation results show that the electrochemical-thermal coupling model can be effectively applied to SOC estimation.

Application of Artificial Intelligence to State-of-Charge and State-of-Health Estimation of Calendar-Aged Lithium-Ion Pouch Cells

Accurate State-of-Charge (SOC) and State-of-Health (SOH) estimation of lithium-ion batteries (LIBs) is essential for the battery management system (BMS). For the first time, a feed-forward artificial neural network (ANN) has been used to estimate SOC of calendar-aged lithium-ion pouch cells. Calendar life data has been generated by applying galvanostatic charge/discharge cycle loads at different storage temperature (35°C and 60°C) and conditions (fully-discharged and fully-charged). The data has been obtained at various C-rates for duration of 10 months at one-month intervals. In order to include LIB hysteresis effect, two separate ANNs have been trained for charge and discharge data. The ANN have achieved a Root Mean Square Error (RMSE) of 1.17% over discharge data and 1.81% over charge data, confirming the ability of the network to capture input-output dependency. The calendar-aged battery data at various degradation conditions has been employed to train a new ANN to estimate SOH. The ANN has shown RMSE of 1.67%, demonstrating the network capability to estimate SOH. This study highlights the importance of considering aging effects in SOC estimates and the ability of ANN to include these effects efficiently.

Modeling of Back-Propagation Neural Network Based State-of-Charge Estimation for Lithium-Ion Batteries with Consideration of Capacity Attenuation

The state of charge of lithium-ion batteries reflects the power available in the battery. Precise SOC estimation is a challenging task for battery management system. In this paper, a n ...

State-of-Charge Estimation of Lithium-Ion Batteries via Long Short-Term Memory Network

Accurate state-of-charge (SOC) estimation is critical for driving range prediction of electric vehicles and optimal charge control of batteries. In this paper, a stacked long short-term memory network is proposed to model the complex dynamics of lithium iron phosphate batteries and infer battery SOC from current, voltage, and temperature measurements. The proposed network is trained and tested using data collected from the dynamic stress test, US06 test, and federal urban driving schedule. The performance on SOC estimation is evaluated regarding tracking accuracy, computation time, robustness against unknown initial states, and compared with results from the model-based filtering approach (unscented Kalman filter). Moreover, different training and testing data sets are constructed to test its robustness against varying loading profiles. The experimental results show that the proposed network well captures the nonlinear correlation between SOC and measurable signals and provides better tracking performance than the unscented Kalman filter. In case of inaccurate initial SOCs, the proposed network presents quick convergence to the true SOC, with root mean square errors within 2% and mean average errors within 1%. Moreover, the estimation time at each time step is sub-millisecond, making it appropriate for real-time applications.

Combined CNN-LSTM Network for State-of-Charge Estimation of Lithium-Ion Batteries

State-of-charge (SOC), which indicates the remaining capacity at the current cycle, is the key to the driving range prediction of electric vehicles and optimal charge control of rechargeable batteries. In this paper, we propose a combined convolutional neural network (CNN) - long short-term memory (LSTM) network to infer battery SOC from measurable data, such as current, voltage, and temperature. The proposed network shares the merits of both CNN and LSTM networks and can extract both spatial and temporal features from input data. The proposed network is trained using data collected from different discharge profiles, including a dynamic stress test, federal urban driving schedule, and US06 test. The performance of the proposed network is evaluated using data collected from a new combined dynamic loading profile in terms of estimation accuracy and robustness against the unknown initial state. The experimental results show that the proposed CNN-LSTM network well captures the nonlinear relationships between SOC and measurable variables and presents better tracking performance than the LSTM and CNN networks. In case of unknown initial SOCs, the proposed network fast converges to true SOC and, then, presents smooth and accurate results, with maximum mean average error under 1% and maximum root mean square error under 2%. Moreover, the proposed network well learns the influence of ambient temperature and can estimate battery SOC under varying temperatures with maximum mean average error under 1.5% and maximum root mean square error under 2%.

State of health estimation for lithium ion batteries based on an equivalent-hydraulic model: An iron phosphate application

Abstract A two-step approach for state-of-health (SOH) estimation of a lithium-ion (Li-ion) battery is developed. In the first step, state-of-charge (SOC) estimation is performed by a constrained extended Kalman filter (EKF) based on the so-called equivalent-hydraulic model. The latter model allows to characterize the internal battery state and main physical parameters while being suitable for on-line computation. The internal battery states are further exploited in the second step of the approach to obtain parameter-based SOH indicators that characterize the long term evolution of the diffusion and charge transfer processes associated to aging. Capacity and power fade indicators are determined by using notably an instrumental variable method in order to obtain unbiased parameter estimates in the presence of heteroscedastic colored noise. The methodology is validated on both simulation and experimental data for a lithium iron phosphate (LFP) half battery cell. This also provides insight on the properties of the LFP electrodes.

An improved adaptive estimator for state-of-charge estimation of lithium-ion batteries

State-of-charge is an important indicator for guiding the charging-discharging operation of lithium-ion batteries. In this paper, an improved adaptive battery state estimator is proposed, which estimates the state-of-charge accurately under both the constant current constant voltage charging conditions and various dynamic operating conditions. Besides, the state-of-charge estimation error is less than 3% under various common perturbations and parameter uncertainties, including the uncertainty of battery capacity, the Gaussian white noise and the biases of the sensors, which proves the robustness of the estimator. Generally, the drivers prefer to charge the electric vehicles after every driving. In other words, the lithium-ion batteries are likely to be charged or discharged from any state-of-charge and polarization state. In order to simulate this usage habit, several data segments are picked out from original data for state-of-charge estimation. Experiment results show that the state-of-charge estimation error reaches and remains within 3% rapidly under both the constant current constant voltage charging conditions and the dynamic discharging conditions. In conclusion, the improved adaptive battery state estimator is a promising algorithm for engineering application.

State of Health Estimation of Lithium-Ion Batteries Based on Combination of Gaussian Distribution Data and Least Squares Support Vector Machines Regression

Lithium-ion batteries play a critical role in the reliability and safety of a system. Battery health monitoring and remaining useful life (RUL) prediction are needed to prevent catastrophic failure of the battery. The aim of this research is to develop a data-driven method to monitor the batteries state of health and predict their RUL by using the battery capacity degradation data. This paper also investigated the effect of prediction starting point to the RUL prediction error. One of the data-driven method drawbacks is the need of a large amount of data to obtain accurate prediction. This paper proposed a method to generate a series of degradation data that follow the Gaussian distribution based on limited battery capacity degradation data. The prognostic model was constructed from the new data using least square support vector machine (LSSVM) regression. The remaining useful life prediction was carried out by extrapolating the model until reach the end of life threshold. The method was applied to three differences lithium-ion batteries capacity data. The results showed that the proposed method has good performance. The method can predict the lithium-ion batteries RUL with a small error, and the optimal RUL starting point was found at the point where the battery has experienced the highest capacity recovery due to the self-recharge phenomenon.

Online State of Charge Estimation for Lithium-Ion Battery by Combining Incremental Autoregressive and Moving Average Modeling with Adaptive H-Infinity Filter

The state of charge (SOC) estimation is one of the most important features in battery management system (BMS) for electric vehicles (EVs). In this article, a novel equivalent-circuit model (ECM) with an extra noise sequence is proposed to reduce the adverse effect of model error. Model parameters identification method with variable forgetting factor recursive extended least squares (VFFRELS), which combines a constructed incremental autoregressive and moving average (IARMA) model with differential measurement variables, is presented to obtain the ECM parameters. The independent open circuit voltage (OCV) estimator with error compensation factors is designed to reduce the OCV error of OCV fitting model. Based on the IARMA battery model analysis and the parameters identification, an SOC estimator by adaptive H-infinity filter (AHIF) is formulated. The adaptive strategy of the AHIF improves the numerical stability and robust performance by synchronous adjusting noise covariance and restricted factor. The results of experiment and simulation have verified that the proposed approach has superior advantage of parameters identification and SOC estimation to other estimation methods.

Enhanced Coulomb Counting Method for State-of-Charge Estimation of Lithium-ion Batteries based on Peukert’s Law and Coulombic Efficiency

Enhanced Coulomb Counting Method for State-of-Charge Estimation of Lithium-ion Batteries based on Peukert’s Law and Coulombic Efficiency

A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve

The state-of-health (SOH) estimation is always a crucial issue for lithium-ion batteries. In order to provide an accurate and reliable SOH estimation, a novel Gaussian process regression (GPR) model based on charging curve is proposed in this paper. Different from other researches where SOH is commonly estimated by cycle life, in this work four specific parameters extracted from charging curves are used as inputs of the GPR model instead of cycle numbers. These parameters can reflect the battery aging phenomenon from different angles. The grey relational analysis method is applied to analyze the relational grade between selected features and SOH. On the other hand, some adjustments are made in the proposed GPR model. Covariance function design and the similarity measurement of input variables are modified so as to improve the SOH estimate accuracy and adapt to the case of multidimensional input. Several aging data from NASA data repository are used for demonstrating the estimation effect by the proposed method. Results show that the proposed method has high SOH estimation accuracy. Besides, a battery with dynamic discharging profile is used to verify the robustness and reliability of this method.