A Review of Lithium-Ion Battery Capacity Estimation Methods for Onboard Battery Management Systems: Recent Progress and Perspectives

Peng, Jichang; Meng, Jinhao; Chen, Dan; Liu, Haitao; Hao, Sipeng; Sui, Xin; Du, Xinghao

doi:10.3390/batteries8110229

Cited by 33 publications

(13 citation statements)

References 167 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…63 The hybrid method combines multiple estimation methods and uses appropriate weights for comprehensive calculation based on actual data and conditions in different situations, to obtain more accurate and reliable SOC estimation results. 64 The hybrid method can adaptively adjust according to the needs of different application scenarios, thereby further improving the accuracy and reliability of SOC estimation. 65 Figure 1.…”

Section: Soc Socmentioning

confidence: 99%

Review—Optimized Particle Filtering Strategies for High-Accuracy State of Charge Estimation of LIBs

Wang¹,

Jia²,

Takyi‐Aninakwa³

et al. 2023

J. Electrochem. Soc.

View full text Add to dashboard Cite

Lithium-ion batteries (LIBs) are used as energy storage systems due to their high efficiency. State of charge (SOC) estimation is one of the key functions of the battery management system (BMS). Accurate SOC estimation helps to determine the driving range and effective energy management of electric vehicles (EVs). However, due to complex electrochemical reactions and nonlinear battery characteristics, accurate SOC estimation is challenging. Therefore, this review examines the existing methods for estimating the SOC of LIBs and analyzes their respective advantages and disadvantages. Subsequently, a systematic and comprehensive analysis of the methods for constructing LIB models is conducted from various aspects such as applicability and accuracy. Finally, the advantages of particle filtering (PF) over the Kalman filter (KF) series algorithm for estimating SOC are summarized, and various improved PF algorithms for estimating the SOC of LIBs are compared and discussed. Additionally, this review provides corresponding suggestions for researchers in the battery field.

show abstract

Section: Soc Socmentioning

confidence: 99%

Review—Optimized Particle Filtering Strategies for High-Accuracy State of Charge Estimation of LIBs

Wang¹,

Jia²,

Takyi‐Aninakwa³

et al. 2023

J. Electrochem. Soc.

View full text Add to dashboard Cite

show abstract

“…It is one of the important indicators which reveals the maximum energy a battery is capable to store which is necessary for state-of-theart performance and its lifespan management. 83 It is a significant parameter for estimation of the state and health of a battery. Another important parameter for a battery is the Coulomb efficiency which is defined as the total output charge extracted from the battery to the total input charge provided to the battery over a full working cycle.…”

Section: Introductionmentioning

confidence: 99%

“…The energy stored in the Li-ion battery is defined in terms of Li-ion storage capacity. It is one of the important indicators which reveals the maximum energy a battery is capable to store which is necessary for state-of-the-art performance and its lifespan management . It is a significant parameter for estimation of the state and health of a battery.…”

Section: Introductionmentioning

confidence: 99%

MXenes as Li-Ion Battery Electrodes: Progress and Outlook

et al. 2023

View full text Add to dashboard Cite

MXenes, in view of their extraordinary properties, such as layered structure, metal-like high thermal stability, good mechanical strength, high conductivity, presence of active sites, and terminal functional groups, have received the utmost importance as outstanding materials for energy storage applications such as supercapacitors and metal-ion batteries. Even though MXenes have promising applications in Li-ion batteries due to their high Li-ion storage capacity, their performance is not satisfactory compared to the presently used state-of-the-art electrodes. Pure MXenes suffer from restacking of their sheets, and to overcome this issue and to enhance their performance, attempts have been made to modify their surfaces and/or surface terminal groups. Synthesis of MXene-based composites has produced attractive electroactive materials. The present review focuses on MXene-based electrode materials for applications in Liion batteries.

show abstract

“…Since it cannot be directly measured, real-time methods to estimate the cycle capacity of a battery are a must. With the widespread application of renewable energy sources and electric vehicles, researchers have paid much attention to the development of accurate battery RUL models [17].…”

Section: Introductionmentioning

confidence: 99%

“…Several methods were proposed to estimate battery capacity and can be roughly classified as direct and indirect methods, while the latter ones can be subclassified as analysisbased methods, state of charge (SOC)-based methods, and data-driven methods [17].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Lithium Battery Aging Prediction Based on Capacity Estimation and Deep Learning Methods

de la Vega,

Riba,

Ortega-Redondo

2023

Batteries

View full text Add to dashboard Cite

Lithium-ion batteries are key elements in the development of electrical energy storage solutions. However, due to cycling, environmental, and operating conditions, battery capacity tends to degrade over time. Capacity fade is a common indicator of battery state of health (SOH) because it is an indication of how the capacity has been degraded. However, battery capacity cannot be measured directly, and thus, there is an urgent need to develop methods for estimating battery capacity in real time. By analyzing the historical data of a battery in detail, it is possible to predict the future state of a battery and forecast its remaining useful life. This study developed a real-time, simple, and fast method to estimate the cycle capacity of a battery during the charge cycle using only data from a short period of each charge cycle. This proposal is attractive because it does not require data from the entire charge period since batteries are rarely charged from zero to full. The proposed method allows for simultaneous and accurate real-time prediction of the health and remaining useful life of the battery over its lifetime. The accuracy of the proposed method was tested using experimental data from several lithium-ion batteries with different cathode chemistries under various test conditions.

show abstract

A Review of Lithium-Ion Battery Capacity Estimation Methods for Onboard Battery Management Systems: Recent Progress and Perspectives

Cited by 33 publications

References 167 publications

Review—Optimized Particle Filtering Strategies for High-Accuracy State of Charge Estimation of LIBs

Review—Optimized Particle Filtering Strategies for High-Accuracy State of Charge Estimation of LIBs

MXenes as Li-Ion Battery Electrodes: Progress and Outlook

Real-Time Lithium Battery Aging Prediction Based on Capacity Estimation and Deep Learning Methods

Contact Info

Product

Resources

About