A Novel Real-Time State-of-Health and State-of-Charge Co-Estimation Method for LiFePO_4 Battery

The state of charge （SOC） and state of health （SOH） are two of the most important parameters of Li-ion batteries in industrial production and in practical applications. The real-time estimation for these two parameters is crucial to realize a safe and reliable battery application. However, this is a great problem for LiFePO4 batteries due to the large constant potential plateau in the charge/discharge process. Here we propose a combined SOC and SOH co-estimation method based on the experimental test under the simulating electric vehicle working condition. A first-order resistance-capacitance equivalent circuit is used to model the battery cell, and three parameter values, ohmic resistance （Rs）, parallel resistance （Rp） and parallel capacity （Cp）, are identified from a real-time experimental test. Finally we find that Rp and Cp could be utilized to make a judgement on the SOIl. More importantly, the linear relationship between Cp and the SOC is established to make the estimation of the SOC for the first time.

State-of-Health Map of Lithium-ion Batteries Established by ICE Map Approach

State-of-health(SOH) is one of the main factors for lithium-ion batteries that indicate their life information. Thus it is essential to estimate SOH accurately during the operation of lithium-ion batteries. In this paper, an SOH map is proposed to illustrate the SOH of lithium-ion batteries by an internal combustion engine(ICE) map approach. Both direct current internal resistance(DCR) and open circuit voltage(OCV) are key parameters of lithium-ion batteries, which are obtained through metering and computing. Due to serious affection by environmental temperature, temperature translation is proposed to translate DCR/OCV of different temperature into a nominal value at 25 ℃. Compared with ICE map, SOH map is illustrated by the nominal DCR and OCV, which can be looked up to get a nominal SOH. In the SOH map, a pair of the DCR and the OCV can only map out a unique SOH, which is beneficial for application in engineering practice in most cases.

State-of-health estimation of lithium-ion batteries based on electrochemical impedance spectroscopy: a review

Lithium-ion batteries(LIBs)are crucial for the large-scale utilization of clean energy.However,because of the com-plexity and real-time nature of internal reactions,the mechanism of capacity decline in LIBs is still unclear.This has become a bottleneck restricting their promotion and application.Electrochemical impedance spectroscopy(EIS)contains rich electrochemical connotations and significant application prospects,and has attracted widespread atten-tion and research on efficient energy storage systems.Compared to traditional voltage and current data,the state-of-health(SOH)estimation model based on EIS has higher accuracy.This paper categorizes EIS measurement methods based on different principles,introduces the relationship between LIBs aging mechanism and SOH,and compares the advantages of different SOH estimation methods.After a detailed analysis of the latest technologies,a review is given.The insights of this review can deepen the understanding of the relationship between EIS and the aging effect mechanism of LIBs,and promote the development of new energy storage devices and evaluation methods.

Prognostics for Lithium-ion batteries for electric Vertical Take-off andLanding aircraft using data-driven machine learning

The health management of batteries is a key enabler for the adoption of Electric Vertical Take-off and Landingvehicles (eVTOLs). Currently, few studies consider the health management of eVTOL batteries. One distinctcharacteristic of batteries for eVTOLs is that the discharge rates are significantly larger during take-off andlanding, compared with the battery discharge rates needed for automotives. Such discharge protocols areexpected to impact the long-run health of batteries. This paper proposes a data-driven machine learningframework to estimate the state-of-health and remaining-useful-lifetime of eVTOL batteries under varying flightconditions and taking into account the entire flight profile of the eVTOLs. Three main features are consideredfor the assessment of the health of the batteries: charge, discharge and temperature. The importance of thesefeatures is also quantified. Considering battery charging before flight, a selection of missions for state-ofhealth and remaining-useful-lifetime prediction is performed. The results show that indeed, discharge-relatedfeatures have the highest importance when predicting battery state-of-health and remaining-useful-lifetime.Using several machine learning algorithms, it is shown that the battery state-of-health and remaining-useful-lifeare well estimated using Random Forest regression and Extreme Gradient Boosting, respectively.

Robust state of charge and state of health estimation for batteries using a novel multi model approach

Estimation of state-of-charge and state-of-health for batteries is one of the most important feature for modern battery management system(BMS).Robust or adaptive methods are the most investigated because a more intelligent BMS could lead to sensible cost reduction of the entire battery system.We propose a new robust method,called ERMES(extendible range multi-model estimator),for determining an estimated state-of-charge(SoC),an estimated state-of-health(SoH)and a prediction of uncertainty of the estimates(state-of-uncertainty—SoU),thanks to which it is possible to monitor the validity of the estimates and adjust it,extending the robustness against a wider range of uncertainty,if necessary.Specifically,a finite number of models in state-space form are considered starting from a modified Thevenin battery model.Each model is characterized by a hypothesis of SoH value.An iterated extended Kalman filter(EKF)is then applied to each model in parallel,estimating for each one the SoC state variable.Residual errors are then considered to fuse both the estimated SoC and SoH from the bank of EKF,yielding the overall SoC and SoH estimates,respectively.In addition,a figure of uncertainty of such estimates is also provided.

A novel data-driven method for mining battery open-circuit voltage characterization

Lithium-ion batteries(LiB)are widely used in electric vehicles(EVs)and battery energy storage systems,and accurate state estimation relying on the relationship between battery Open-Circuit-Voltage(OCV)and State-of-Charge(SOC)is the basis for their safe and efficient applications.To avoid the time-consuming lab test needed for obtaining OCV-SOC curves,this study proposes a data-driven universal method by using operation data collected onboard about the variation of OCV with ampere-hour(Ah).To guarantee high reliability,a series of constraints have been implemented.To verify the effectiveness of this method,the constructed OCV-SOC curves are used to estimate battery SOC and State-of-Health(SOH),which are compared with data from both lab tests and EV manufacturers.Results show that a higher accuracy can be achieved in the estimation of both SOC and SOH,for which the maximum deviations are less than 3.0%and 2.9%respectively.