Battery State Of Health Research Articles

Remaining useful life and state of health prediction for lithium batteries based on differential thermal voltammetry and a deep-learning model

Lithium-ion batteries (LIBs) are widely used in the assembly of battery packs for electric vehicles and energy storage grids due to their high power density, low self-discharge rate and reasonable costs. Accurate estimation of state of health (SOH) and remaining useful life (RUL) are crucial challenges in developing battery management systems (BMS). In this paper, differential thermal voltammetry (DTV) signal analysis methods and recursive neural networks data-driven methods are combined to approach battery degradation tracking. Firstly, with the Savitzky-Golay (SG) method and Pearson correlation analysis, the DTV curve is smoothed, and three useful feature variables are extracted from different dimensions, bridging signaling characteristics and phase transition characteristics. Then four recursive neural networks are constructed and compared based on NASA databases. The Bayesian optimization method is applied to improve hyperparameter values and the Monte Carlo (MC) simulation is used to quantify uncertainties. The proposed data-driven method can predict the RUL and estimate the SOH of battery accurately. The root mean square error (RMSE) for prediction results could reach below 1% and the capacity rebound phenomenon could be captured as well. The proposed integrated degradation model can contribute to the real-time prediction and optimization of battery health conditions based on cloud computing platform, promoting the continuous development of cloud battery management systems in framework of Cyber Hierarchy and Interactional Network (CHAIN).

Multiobjective Optimized Smart Charge Controller for Electric Vehicle Applications

The continuous deployment of distributed energy sources and the increase in the adoption of electric vehicles (EVs) require smart charging algorithms. The existing EV chargers offer limited flexibility and controllability and do not fully consider factors (such as EV user waiting time and the length of next trip) as well as the potential opportunities and financial benefits from using EVs to support the grid, charge from renewable energy, and deal with the negative impacts of intermittent renewable generation. The lack of adequate smart EV charging may result in high battery degradation, violation of grid control statutory limits, high greenhouse emissions, and high charging cost. In this article, a neuro-fuzzy particle swarm optimization (PSO)-based novel and advanced smart charge controller is proposed, which considers user requirements, energy tariff, grid condition (e.g., voltage or frequency), renewable (photovoltaic) output, and battery state of health. A rule-based fuzzy controller becomes complex as the number of inputs to the controller increases. In addition, it becomes difficult to achieve an optimum operation due to the conflicting nature of control requirements. To optimize the controller response, the PSO technique is proposed to provide a global optimum solution based on a predefined cost function, and to address the implementation complexity, PSO is combined with a neural network. The proposed neuro-fuzzy PSO control algorithm meets EV user requirements, works within technical constraints, and is simple to implement in real time (and requires less processing time). Simulation using MATLAB and experimental results using a dSPACE digital real-time emulator are presented to demonstrate the effectiveness of the proposed controller.

Online Estimations of Li-Ion Battery SOC and SOH Applicable to Partial Charge/Discharge

Estimating the state of health (SOH) and state of charge (SOC) of lithium-ion batteries is crucial for increasing the battery lifetime and performance. Many estimation methods are offline and require large datasets for training. The majority of online estimation methods either take too much time or need a full discharge or charge cycle. In this article, a fast online SOH estimation method that can work with partial charge/discharge is introduced. Only two consecutive partial discharge intervals are used to estimate the battery equivalent circuit model parameters and the open-circuit voltage (OCV). By comparing the estimated OCV curve at each interval with a reference or datasheet OCV curve, the battery capacity and, therefore, its SOH and SOC are accurately estimated. It is shown that updating the OCV reference curve based on temperature readings will provide more accurate results. NASA degradation dataset is used to validate the proposed method and the average reported root-mean-square error is below 1% for SOH and 1.07% for SOC.

A Polak-Ribière-Polyak Conjugate Gradient Algorithm Optimized Broad Learning System for Lithium-ion Battery State of Health Estimation

Accurate state of health (SOH) estimation plays a significant role in the battery management system. This paper investigates a Polak-Ribière-Polyak conjugate gradient (PRPCG) algorithm optimized broad learning system (BLS) for lithium-ion battery SOH estimation. Firstly, effective health indicators (HIs) are extracted from the voltage curve in the constant current charge process. Secondly, a hybrid four layers BLS structure with mapped feature nodes and enhancement nodes connecting to the output is established to build both the linear and nonlinear relationships between the HIs and SOH, in which only the output weights require to be trained. Again, the PRPCG algorithm is adopted for searching optimal output weights without matrix inverse calculation during the training process. Furthermore, certain Gaussian noises are added to enhance the training data for solving the locally low accuracy problem. Finally, under the Oxford battery degradation data set, experiments validate the investigated algorithm has high accuracy in SOH estimation with the mean absolute error below 1%. The enhanced data can efficiently improve the model generalization ability.

Characterization of aging mechanisms and state of health for second-life 21700 ternary lithium-ion battery

This paper focuses on the identification of aging mechanisms and the estimation of the state of health (SOH) for second-life 21700 nickel–cobalt–aluminum (NCA) lithium-ion batteries. NCA battery is aged at 1/2 C-rate in the laboratory until its SOH value reaches about 60%. Incremental capacity (IC) and different voltage (DV) and probability density function (PDF) analyses are used to explore the aging mechanisms of NCA battery and build SOH estimation models with and without multicollinearity. The results show that the capacity decays almost linearly throughout the aging cycle and the loss of lithium inventory (LLI) and the loss of active material (LAM) on anode are the main aging mechanisms. The equivalence between PDF and IC/DV analyses is proved by NCA battery. Peak (valley) features from IC, DV and PDF curves are extracted as health factors (HFs), which have good correlation with battery SOH. Principal component analysis (PCA) is employed to reduce the multicollinearity and more accurate SOH evaluation models are built based on principal component regression (PCR). The verification results show that the ICA-PCR, DVA-PCR and PDF-PCR models all have good evaluation accuracy.

SOH prediction of lithium battery based on IC curve feature and BP neural network

Precise battery SOH (state of health) prediction and monitoring are of extreme importance for the future intelligent battery management system (BMS). In this paper, battery discharge experiments at different temperatures were carried out. A battery SOH prediction model based on incremental capacity analysis and BP neural network is proposed to predict battery SOH at different ambient temperatures. By analyzing the correlation between the characteristics of IC curve and SOH, the mapping relationship between temperature and IC curve characteristics is established by using the least square method, and the SOH prediction model at different temperatures is obtained. At the same time, combined with ICA, an online real-time correction prediction model is established, and the characteristic data is continuously updated to ensure the SOH prediction accuracy under different aging states. Finally, the feasibility of the prediction method proposed in this paper is verified by comparing the model test results and experimental results, the average error of the model prediction results is 1.16%. Thus, by establishing the relationship between temperature and IC curve characteristics, the battery SOH at different temperatures can be predicted.

Lithium Battery Health Factor Extraction Based on Improved Douglas–Peucker Algorithm and SOH Prediction Based on XGboost

To mine the battery’s health factors more comprehensively and accurately identify the lithium battery’s State of Health (SOH), an Improved Douglas–Peucker feature extraction algorithm is proposed, and the LAOS-XGboost model is proposed to be used to predict the SOH of the battery. Firstly, to solve the problem that the traditional Douglas–Peucker algorithm has difficulties extracting curve features in a fixed dimension, the Douglas–Peucker algorithm is improved by de-thresholding. Then, the Wrapper method combined with the Improved Douglas–Peucker algorithm is used to construct the feature engineering of battery life prediction, and the optimal feature subset is obtained. Then, LAOS-XGboost is used to establish a battery SOH prediction model; finally, this model is used to predict the SOH of different batteries and the same battery, and the robustness of the model is analyzed. The experimental results show that the R2 of all XGboost models is higher than 0.97 in the prediction experiments of different batteries. The AE of the LAOS-XGboost model is 0, and the TIC index is less than 3% under 10 dB SNR. In the same battery prediction experiment, the TIC index of the model is less than 0.3%.

A Novel Evaluation Criterion for the Rapid Estimation of the Overcharge and Deep Discharge of Lithium-Ion Batteries Using Differential Capacity

Differential capacity dQ/dU (capacitance) can be used for the instant diagnosis of battery performance in common constant current applications. A novel criterion allows state-of-charge (SOC) and state-of-health (SOH) monitoring of lithium-ion batteries during cycling. Peak values indicate impeding overcharge or deep discharge, while dSOC/dU = dU/dSOC = 1 is close to “full charge” or “empty” and can be used as a marker for SOC = 1 (and SOC = 0) at the instantaneous SOH of the aging battery. Instructions for simple state-of-charge control and fault diagnosis are given.

State-of-Health Estimate for the Lithium-Ion Battery Based on Constant Voltage Current Entropy and Charging Duration

An accurate state-of-health (SOH) estimation is vital to guarantee the safety and reliability of a lithium-ion battery management system. In application, the electrical vehicles generally start charging when the battery is at a non-zero state of charge (SOC), which will influence the charging current, voltage and duration, greatly hindering many traditional health features to estimate the SOH. However, the constant voltage charging phase is not limited by the previous non-zero SOC starting charge. In order to overcome the difficulty, a method of estimating the battery SOH based on the information entropy of battery currents of the constant voltage charging phase and charging duration is proposed. Firstly, the time series of charging current data from the constant voltage phase are measured, and then the information entropy of battery currents and charging time are calculated as new indicators. The penalty coefficient and width factor of a support vector machine (SVM) improved by the sparrow search algorithm is utilized to establish the underlying mapping relationships between the current entropy, charging duration and battery SOH. Additionally, the results indicate the adaptability and effectiveness of the proposed approach for a battery pack and cell SOH estimation.

Real-time energy management strategy for a plug-in hybrid electric bus considering the battery degradation

The energy conservation remains the key issue for the hybrid electric vehicles (HEVs) today. However, most existing energy management strategies (EMS) only focus on the fuel consumption or battery preservation, and little considers the battery health. Besides, the global energy optimality and real-time execution are two trade-off counterparts for the EMS application. To this end, this paper proposed a real time EMS of the HEVs considering the battery health. Explicitly, the battery health status and SOC values are predicted with the space vector machine algorithm and adaptive Kalman filter algorithm, respectively. Then, the offline energy optimization is realized with the Pontryagin's minimum principle; thereby, the offline energy conversion coefficient can be further extracted into the simple rules. On this basis, the online equivalent consumption minimization strategy (ECMS) is adopted to output the optimal control variables including the motor torque, engine torque, clutch states and gear information. Besides, to apply the proposed EMS in the practical vehicles, the energy conversion coefficients are further modified according to the estimated battery state of the charge (SOC). The simulation and experimental tests have validated the superiority of the proposed EMS in terms of energy economy and maneuverability. Explicitly, the proposed EMS considering the battery degradation has effectively prevented the SOC trajectory into charge-sustaining stage earlier and meanwhile the fuel utilization is improved by 4.1 % than that without considering the battery degradation. Besides, compared with the existing ECMS method, the proposed EMS can reduce the fuel consumption by about 8–14 % and meanwhile enhance the handling adaptiveness by about 15–20 %. In general, the proposed EMS is of significance to the vehicular energy conservation and the real-time executability for the HEVs.

Improved Particle Swarm Optimization-Extreme Learning Machine Modeling Strategies for the Accurate Lithium-ion Battery State of Health Estimation and High-adaptability Remaining Useful Life Prediction

To ensure the secure and stable operation of lithium-ion batteries, the state of health (SOH) and the remaining useful life (RUL) are the critical state parameters of lithium-ion batteries, which need to be estimated precisely. A joint SOH and RUL estimation approach based on an improved Particle Swarm Optimization Extreme Learning Machine (PSO-ELM) is proposed in this paper. The approach adopts Pearson coefficients to screen multivariate information of the discharge process as health indicators and uses them as inputs to enable accurate estimation of SOH and RUL prediction of lithium-ion batteries on the basis of the PSO-ELM model. The validity of the model is demonstrated by the NASA lithium-ion battery data set: the maximum root mean square error (RMSE) of the SOH estimation of the tested battery is 0.0033, the maximum RMSE of its RUL prediction is 0.0082, and the maximum absolute error of RUL prediction is one cycle number. In comparison with the prediction results of the traditional extreme learning machine, the optimized model proposed in this paper estimates the SOH of lithium-ion batteries and RUL with relatively high accuracy.

Intelligent decision support platform of new energy vehicles

New energy vehicles (NEVs) are gaining wider acceptance as the transportation sector is developing more environmentally friendly and sustainable technology. To solve problems of complex application scenarios and multi-sources heterogenous data for new energy vehicles and weak platform scalability, the framework of an intelligent decision support platform is proposed in this paper. The principle of software and hardware system is introduced. Hadoop is adopted as the software system architecture of the platform. Master-standby redundancy and dual-line redundancy ensure the reliability of the hardware system. In addition, the applications on the intelligent decision support platform in usage patterns recognition, energy consumption, battery state of health and battery safety analysis are also described.

State-of-health estimation for lithium-ion batteries based on historical dependency of charging data and ensemble SVR

Accurate estimation of State-of-Health (SOH) is very important for the safe and reliable operation of lithium-ion batteries. Considering that the historical dependency of charging data could reflect the internal electrochemical reaction of the battery, a new SOH estimation method is proposed. Firstly, a data pre-processing method is developed to resample the voltage data of the constant current charging stage with a predefined fixed number of samples. It can suppress the measurement noise and facilitate calculating the difference of voltage curves under different aging levels. Secondly, a new health indicator (HI) is proposed. It includes two types of features, one is accumulated voltage of different intervals and the other is charging capacity, they are used to reflect the non-linear changes of the charging voltage and changes of the charging time with the battery aging respectively. In addition, considering the cell inconsistency, an Ensemble Support Vector Regression (ESVR) model is put forward to establish the relationship between HI and battery SOH. Finally, two kinds of open-source battery data are tested and the results show that the method developed in the paper could get high-precision SOH estimation results and the HI is robust to the battery type and cell inconsistency.

Battery Full Life Cycle Management and Health Prognosis Based on Cloud Service and Broad Learning

Dear editor, This letter presents battery full life cycle management and health prognosis based on cloud service and broad learning. Specifically, a cloud-based framework for battery full life cycle management is presented. Then, the broad learning method is proposed for battery state-of-health (SOH) prediction. The features of charging data including the constant current time, constant voltage time, and the total charging time are selected as the input characteristics of the network to estimate SOH. Moreover, the empirical mode decomposition is carried out on the initial data to restore the most essential attenuation trajectory of battery capacity. Experimental results show that the proposed method can provide more accurate battery SOH prediction than several state-of-the-art methods.

State of health estimation of power batteries based on multi-feature fusion models using stacking algorithm

The data-driven method is used widely to estimate the state of health (SOH) of the battery, but the selection of data features and the data training methods affect the estimation results greatly. With the stacking algorithm, this paper proposes a multi-feature fusion model to estimate battery SOH by fusing different feature parameters and combining support vector regression (SVR) and long short-term memory network (LSTM). The feature parameters were extracted only from the current change curve of the constant voltage charging stage. The support vector regression based on grid search (GS-SVR) was selected as the primary-learner, and the primary SVR models were constructed through 5-fold cross-validation for different feature parameters. The LSTM was selected as the secondary-learner. With the stacking algorithm, LSTM was used to fuse multiple primary SVR models to form an ensemble learner model to improve the performance of multi-feature fusion. The battery aging test data set and NASA battery test data set were used to evaluate the effectiveness. The results verified the validity and superiority of the proposed method. Compared with the existing estimation methods, root mean square error is reduced by at least 0.11, and mean absolute percentage error is reduced by at least 0.12%.

A comparative study of battery state-of-health estimation based on empirical mode decomposition and neural network

Accurate estimation of state of health (SOH) in the battery management system furnishes powerful support for ensuring safe and reliable operation of lithium-ion batteries. Data-based neural networks have progressively developed into the most representative solution of SOH estimation. This paper systematically compares three typical neural networks and variants on the accuracy and robustness. Moreover, empirical mode decomposition is firstly adopted to withdraw efficacious health indicators from measurement data acquired during constant current and constant voltage charging. Secondly, Pearson correlation coefficient is applied to elect features with strong characterization from constant-current phase duration, constant-voltage phase duration, constant-current phase time proportion, constant-voltage phase time proportion, and total charge time. Finally, several neural network models such as simple recurrent neural networks, long and short-term memory neural networks (LSTM), gated recurrent units and opposite bidirectional structure are built and compared. Considering the universality and fairness of the results, the novel hyperband optimization seeks optimal configuration for deep learning models through dynamic resource allocation based on the early-stopping strategy and Successive Halving algorithm. Experimental results indicate that LSTM and bidirectional LSTM have higher charge–discharge conditions insensitivity and precision in battery SOH.

A Joint Estimation Method Based on Kalman Filter of Battery State of Charge and State of Health

In a battery management system, the accurate estimation of the battery’s state of health (SOH) and state of capacity (SOC) are vital functions. The traditional estimation methods have limitations. To accurately estimate the SOC and SOH of power battery and improve the performance of the long-term estimation of a battery’s SOC, a joint estimation method based on a Kalman filter is proposed in this work. First, a second-order RC equivalent circuit model of a ternary lithium battery was built, whose parameters were identified online, and the model’s accuracy was verified. Then, the battery data under actual working conditions were collected. The SOC and SOH were estimated based on the Kalman filter algorithm, and the simulation was implemented using MATLAB. Finally, according to a time scale transformation, the battery state was jointly estimated, the SOC was estimated at a short-time scale, the SOH was estimated at a long-time scale, and the SOH estimation results were updated to the model parameters for SOC estimation. The results show that the accuracy of the method is very good, and it can effectively improve estimation accuracy and ensure batteries’ long-term estimation performance.

Health Factor Extraction of Lithium-Ion Batteries Based on Discrete Wavelet Transform and SOH Prediction Based on CatBoost

Aiming to accurately identify the state of health (SOH) and the remaining useful life (RUL) of lithium-ion batteries, in this paper, we propose an algorithm for the health factor extraction and SOH prediction of the batteries based on discrete wavelet transform and the Cauchy–Gaussian variation tent sparrow search algorithm (DWT-CGTSSA). Firstly, concerning the inconsistent data length, discrete wavelet transform (DWT) was adopted to decompose the battery’s signals and extract features. Then, the Cauchy–Gaussian variation tent sparrow search algorithm (CGTSSA) was utilized to extract features and obtain the optimal feature subset after encoding. Finally, the optimal feature subset was used to establish a prediction model based on CatBoost for predicting the SOH of lithium-ion batteries. Experiments were conducted for verification. The experimental results showed that the model established in this research is capable of realizing the prediction between different battery packs. The B0005 battery from dataset A was taken as the training set to predict the complete SOH of B0006 and B0007 batteries. For the prediction model of CGTSSA-CatBoost, the goodness of fit (R2) exceeded 0.99, and the value of mean square error (MSE) was less than 1‰. A comparison with other state-of-the-art prediction models verified the superior performance of the CGTSSA-CatBoost model. Under different working conditions, the R2 of all models in dataset B exceeded 0.98.

Data-driven battery state of health estimation based on interval capacity for real-world electric vehicles

State of health (SOH) estimation is critical to the safety of battery systems in real-world electric vehicles. Accurate battery health status is difficult to be measured during dynamic and robust vehicular operation conditions. This paper proposes a novel SOH estimation model based on Catboost and interval capacity during the charging process. A year-long operation dataset of an electric taxi is derived with all charging segments separated to construct the research dataset. The charging patterns are analyzed, and the segments with rich aging information are extracted, then a general aging feature of interval capacity is extracted by incremental capacity analysis. Furthermore, comparison with the other six machine learning methods is conducted, and five inputs are determined through Pearson correlation analysis, including start charging state of charge (SOC), end charging SOC, mileage, temperature of probe, and current. The results show the Catboost-based model achieves the best accuracy, with the mean absolute percentage error and root mean squared error limited within 2.74% and 1.12%, respectively. More importantly, a battery aging evaluation strategy and its further research plan is proposed for the application in real-world electric vehicles.

Direct Comparison using Coulomb Counting and Open Circuit Voltage Method for the State of Health Li-Po Battery

Electric cars have undergone many developments in the current digital era. This is to avoid the use of increasingly scarce fuel. Recent studies on electric cars show that battery estimation is an interesting topic to be implemented directly. The battery estimation strategy is carried out by the Battery Management System (BMS). BMS is an indispensable part of electric vehicles or hybrid vehicles to ensure optimal and reliable operation of regulating, monitoring, and protecting batteries. A reliable BMS can extend battery life by setting voltage, temperature, and charging and discharging current limits. The main estimation strategy used by BMS is battery fault, SOH, and battery life. Battery State of Health (SOH) is part of the information provided by the BMS to avoid battery damage and failure. SOC is the proportion of battery capacity SOH is a measure of battery health. This study aims to develop a method for estimating SOH simultaneously using Coulomb Counting and Open Circuit Voltage (OCV) algorithms. The battery is modeled to obtain battery parameters and components of internal resistance, capacitance polarization and OCV voltage source. Several tests were implemented in this research by applying the constant current (CC)-charge CC-discharge test. The state-space system is then formed to apply the Coulomb Counting and OCV algorithms so that SOH can be estimated simultaneously. The OCV-SOC function is obtained in the form of a tenth order polynomial and the battery model parameters say that these parameters change with the health of the battery. The results of the model validation are able to accurately model the battery with an average relative error of 0.027%. Coulomb Counting resulted in an accurate SOH estimation with an error of 3.4%.