Query: SOH  BATTERY  REVIEW

title: A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems
abstract: With the rapid development of new energy electric vehicles and smart grids, the demand for batteries is increasing. The battery management system (BMS) plays a crucial role in the battery powered energy storage system. This paper presents a systematic review of the most commonly used battery modeling and state estimation approaches for BMSs. The models include the physics based electrochemical models, the integral and fractional order equivalent circuit models, and data driven models. The state estimation approaches are analyzed from the perspectives of remaining capacity and energy estimation, power capability prediction, lifespan and health prognoses, and other crucial indexes in BMS. This present paper, through the analysis of literature, includes almost all states in the BMS. The estimation approaches of state of charge (SOC) state of energy (SOE) state of power (SOP) state of function (SOF) state of health (SOH) remaining useful life (RUL) remaining discharge time (RDT) state of balance (SOB) and state of temperature (SOT) are reviewed and discussed in a systematical way. Moreover, the challenges and outlooks of the research on future battery management are disclosed, in the hope of providing some inspirations to the development and design of the next generation BMSs.

title: A Review of Battery State of Health Estimation Methods: Hybrid Electric Vehicle Challenges
abstract: To cope with the new transportation challenges and to ensure the safety and durability of electric vehicles and hybrid electric vehicles, high performance and reliable battery health management systems are required. The Battery State of Health (SOH) provides critical information about its performances, its lifetime and allows a better energy management in hybrid systems. Several research studies have provided different methods that estimate the battery SOH. Yet, not all these methods meet the requirement of automotive real time applications. The real time estimation of battery SOH is important regarding battery fault diagnosis. Moreover, being able to estimate the SOH in real time ensure an accurate State of Charge and State of Power estimation for the battery, which are critical states in hybrid applications. This study provides a review of the main battery SOH estimation methods, enlightening their main advantages and pointing out their limitations in terms of real time automotive compatibility and especially hybrid electric applications. Experimental validation of an online and on board suited SOH estimation method using model based adaptive filtering is conducted to demonstrate its real time feasibility and accuracy.

title: Review of state of the art battery state estimation technologies for battery management systems of stationary energy storage systems
abstract: Lithium ion batteries have recently been in the spotlight as the main energy source for the energy storage devices used in the renewable energy industry. The main issues in the use of lithium ion batteries are satisfaction with the design life and safe operation. Therefore, battery management has been required in practice. In accordance with this demand, battery state indicators such as the state of charge (SOC) state of health (SOH) state of function (SOF) and state of temperature (SOT) have been widely applied. The use of these indicators ensures safe operation without overcharging and over discharging. In addition, it can also help satisfy the design life. This paper presents a literature review of battery state indicators over the last three years and proposes the requirement of state of the art battery state indicators. It also suggests future developments for battery management system (BMS) in stationary energy storage systems (ESSs)

title: Critical Review of Ageing Mechanisms and State of Health Estimation Methods for Battery Performance
abstract: Battery Management System (BMS) is an essential component for lithium ion battery based devices. It provides a variety of functionalities that help to improve the overall lifespan of the battery, including states estimation algorithms. An accurate state of health is one of the essential features an advanced BMS provides, in order to track long term performance and ensure reliable operation of the battery. This paper aims to provide a structured review of ageing mechanisms that affect the battery performance and also some of the most relevant algorithms that were used in the recent literature to predict the actual health of the battery. Finally, based on the different approaches that were studied, evaluation criteria are proposed to help judge the performance of State Of Health (SOH) estimation algorithm.

title: A review of the state of health for lithium ion batteries: Research status and suggestions
abstract: Lithium ion batteries (LIBs) have become the mainstream power source for battery electric vehicles (BEVs) with relatively superior performance. However, LIBs experience battery aging and performance degradation due to the external environment and internal factors, which should be reflected in the evaluation of the state of health (SOH) Accurately predicting SOH can improve the overall life of the battery and support safe driving in BEVs. At present, while there are many prediction methods for SOH, most are implemented in simulated environments but are challenging to execute in actual industrial production. This review provides a discussion on the aging reasons for LIBs, introduces the SOH prediction method based on the classification framework, and analyzes the key benefits and drawbacks of each method. Finally, the corresponding suggestions and solutions are given in combination with the actual industrial production.

title: A Review of State of health Estimation of Lithiumion Batteries: Experiments and Data
abstract: Accurate state of health (SOH) estimation of Lithiumion batteries (LIBs) is crucial for Battery Management System (BMS) in electric vehicles (EVs) To develop a practical SOH estimation matched with high precision, experimental data, battery model and estimation algorithm are indispensable aspects to be well handled. Recently, review of battery model and estimation algorithm has been well presented to provide references for the world academic community. In this paper, we focus on the review of experiments and data. Three types of experiment modes with the experimental variables and generated data are reviewed and analyzed in this paper: charging discharging mode, discharging mode and charging mode. Based on the concrete analysis of experiments and data, we find that experiment modes and variables affect SOH estimation uniquely. The work provides a new method to study SOH, which is helpful to simplify research and reduce battery experiments.

title: Multiobjective Optimization of Data Driven Model for Lithium Ion Battery SOH Estimation With Short Term Feature
abstract: As a favorable energy storage component, lithium ion (Li ion) battery has been widely used in the battery energy storage systems (BESS) and electric vehicles (EV) Data driven methods estimate the battery state of health (SOH) with the features extracted from the measurement. However, excessive features may reduce the estimation accuracy and also increases the human labor in the lab. By proposing an optimization process with nondominated sorting genetic algorithm II (NSGA II) this article is able to establish a more efficient SOH estimator with support vector regression (SVR) and the short term features from the current pulse test. NSGA II optimizes the entire process of establishing a SOH estimator considering both the measurement cost of the feature and the estimation accuracy. A series of nondominated solutions are obtained by solving the multiobjective optimization problem, which also provides more flexibility to establish the SOH estimator at various conditions. The degradation features in this article are the knee points at the transfer instants of the voltage in the short term current pulse test, which is fairly convenient and easy to be obtained in real applications. The proposed method is validated on the measurement from two LiFePO4/C batteries aged with the mission profile providing the primary frequency regulation (PFR) service to the grid.

title: Integrated Approach Based on Dual Extended Kalman Filter and Multivariate Autoregressive Model for Predicting Battery Capacity Using Health Indicator and SOC/SOH
abstract: To enhance the efficiency of an energy storage system, it is important to predict and estimate the battery state, including the state of charge (SOC) and state of health (SOH) In general, the statistical approaches for predicting the battery state depend on historical data measured via experiments. The statistical methods based on experimental data may not be suitable for practical applications. After reviewing the various methodologies for predicting the battery capacity without measured data, it is found that a joint estimator that estimates the SOC and SOH is needed to compensate for the data shortage. Therefore, this study proposes an integrated model in which the dual extended Kalman filter (DEKF) and autoregressive (AR) model are combined for predicting the SOH via a statistical model in cases where the amount of measured data is insufficient. The DEKF is advantageous for estimating the battery state in real time and the AR model performs better for predicting the battery state using previous data. Because the DEKF has limited performance for capacity estimation, the multivariate AR model is employed and a health indicator is used to enhance the performance of the prediction model. The results of the multivariate AR model are significantly better than those obtained using a single variable. The mean absolute percentage errors are 1.45% and 0.5183% respectively.

title: SOH Estimation and SOC Recalibration of Lithium Ion Battery with Incremental Capacity Analysis Cubic Smoothing Spline
abstract: Conventional state of health (SOH) estimation often requires capacity measurement from battery's full charge or discharge profile between fully charged state and cut off state. Incremental capacity analysis can improve estimation efficiency by extracting features to estimate SOH or recalibrate state of charge estimation without using full profile. While direct numerical derivatives often do not show smooth result due to measurement noise, this paper utilizes robust cubic smoothing spline method on producing incremental capacity curve, which is superior over typical filters that require tuning on window size usually by trial&error because smoothing parameters in the proposed method can be determined by cross validation. Comparison through simulated data shows that the proposed method maintains good fidelity on data and feature of interest with low RMSE values under derivative form. This paper also proposes a peak height ratio feature for SOH estimation. While a linear relationship is noted between SOH and peak height ratio, estimation of SOH from peak height ratio is demonstrated using linear regression. A more generalized version of SOH estimation method is also demonstrated using multiple linear regression with covariates of both peak height ratio and the height of peak associated with "last phase transition of Li ions intercalation during charging"

title: Lithium Ion Battery SOH Estimation Based on XGBoost Algorithm with Accuracy Correction
abstract: SOH (state of health) estimation is important for battery management. Since the electrochemical reaction inside LIBS (lithium ion battery system) is extremely complex and the external working environment is uncertain, it is difficult to achieve accurate determination of SOH. To improve the accuracy of SOH estimation, we propose a SOH estimation method for lithium ion battery based on XGBoost algorithm with accuracy correction. We extract several features, including average voltage, voltage difference, current difference, and temperature difference, to describe the aging process of batteries. Due to the higher prediction accuracy and generalization ability of ensemble learning algorithm, the XGBoost model is established to estimate the SOH of lithium ion battery. Then, the estimation values are corrected by Markov chain. Compared with the methods by XGBoost, random forest, k nearest neighbor algorithm (KNN) SVM, linear regression, our proposed method shows an accuracy improvement by 10% to 20% Additionally, the errors of our method are also superior to the others in terms of the average absolute error, root mean square error, and root mean square error.