On-Board State of Health Estimation of Lithium Ion Batteries With Incremental Capacity Analysis Based on Gaussian Function

2018 ◽  
Author(s):  
Tongyi Liang ◽  
Lingjun Song
Keyword(s):  
Estimation Error ◽  
Gaussian Function ◽  
Lithium Ion ◽  
Battery Management System ◽  
Battery Management ◽  
State Of Health ◽  
In Situ Degradation ◽  
Analysis Application ◽  
Key Issues ◽  
Incremental Capacity

Accurately online state of health estimation is one of the key issues in battery management system (BMS), which enable the batteries function safely and efficiently. With excellent performance in in-situ degradation modes detection and precise state of health estimation modeling ability, incremental capacity (IC) analysis is widely used to analyze the situation of aged batteries. This paper discussed the difficulties in IC analysis application at first, and a robust method is then proposed, which parameterize the IC curve with Gaussian function. Battery cycle life experiment is conduced to validate the feasibility and accuracy of the proposed method. A capacity is constructed based the parameters of Gaussian function in each peak. The results show that the model estimation error is less than 3% of normalized capacity in each aging state, promising to be implemented in real BMS.

scholarly journals Innovative Incremental Capacity Analysis Implementation for C/LiFePO4 Cell State-of-Health Estimation in Electrical Vehicles

Batteries ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. 37 ◽  
Author(s):  
Elie Riviere ◽  
Ali Sari ◽  
Pascal Venet ◽  
Frédéric Meniere ◽  
Yann Bultel
Keyword(s):  
Lithium Iron Phosphate ◽  
Iron Phosphate ◽  
Capacity Analysis ◽  
Battery Management System ◽  
Battery Management ◽  
State Of Health ◽  
Depth Of Discharge ◽  
Electrical Vehicles ◽  
Charging Current ◽  
Incremental Capacity

This paper presents a fully embedded state of health (SoH) estimator for widely used C/LiFePO4 batteries. The SoH estimation study was intended for applications in electric vehicles (EV). C/LiFePO4 cells were aged using pure electric vehicle cycles and were monitored with an automotive battery management system (BMS). An online capacity estimator based on incremental capacity analysis (ICA) is developed. The proposed estimator is robust to depth of discharge (DoD), charging current and temperature variations to satisfy real vehicle requirements. Finally, the SoH estimator tuned on C/LiFePO4 cells from one manufacturer was tested on C/LiFePO4 cells from another LFP (lithium iron phosphate) manufacturer.

scholarly journals A Fast Online State of Health Estimation Method for Lithium-Ion Batteries Based on Incremental Capacity Analysis

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3333 ◽  
Author(s):  
Shaofei Qu ◽  
Yongzhe Kang ◽  
Pingwei Gu ◽  
Chenghui Zhang ◽  
Bin Duan
Keyword(s):  
Lithium Ion Batteries ◽  
Estimation Error ◽  
Estimation Method ◽  
Lithium Ion ◽  
Computation Time ◽  
Absolute Error ◽  
Capacity Analysis ◽  
State Of Health ◽  
Important Challenge ◽  
Incremental Capacity

Efficient and accurate state of health (SoH) estimation is an important challenge for safe and efficient management of batteries. This paper proposes a fast and efficient online estimation method for lithium-ion batteries based on incremental capacity analysis (ICA), which can estimate SoH through the relationship between SoH and capacity differentiation over voltage (dQ/dV) at different states of charge (SoC). This method estimates SoH using arbitrary dQ/dV over a large range of charging processes, rather than just one or a limited number of incremental capacity peaks, and reduces the SoH estimation time greatly. Specifically, this method establishes a black box model based on fitting curves first, which has a smaller amount of calculation. Then, this paper analyzes the influence of different SoC ranges to obtain reasonable fitting curves. Additionally, the selection of a reasonable dV is taken into account to balance the efficiency and accuracy of the SoH estimation. Finally, experimental results validate the feasibility and accuracy of the method. The SoH estimation error is within 5% and the mean absolute error is 1.08%. The estimation time of this method is less than six minutes. Compared to traditional methods, this method is easier to obtain effective calculation samples and saves computation time.

scholarly journals Active Cell Balancing Control Method for Series-Connected Lithium-Ion Battery

2019 ◽  
Vol 8 (9) ◽  
pp. 2424-2430 ◽  
Keyword(s):  
Lithium Ion Battery ◽  
Control Method ◽  
Estimation Error ◽  
Lithium Ion ◽  
Rechargeable Batteries ◽  
Battery Management System ◽  
Battery Management ◽  
Soc Estimation ◽  
Terminal Voltage ◽  
Charge Balancing

Power conveyance potentiality for series and parallel allied battery-packages are constrained by the wickedest cell of the string. Every cell contains marginally dissimilar capability and terminal voltage because of industrialized acceptances and functional situations. During charging or discharging progression, the charge status of the cell strings become imbalanced and incline to loss equalization. Therefore, the enthusiasm of this paper is to design an active charge balancing system for Lithium-ion battery pack with the help of online state of charge (SOC) estimation technique. A Battery Management System (BMS) is modeled by means of controlling the SOC of the cells to upsurge the efficacy of rechargeable batteries. The capacity of each cell is calculated by dint of SOC function estimated as a result of Backpropagation Neural Network (BPNN) algorithm through four switched DC/DC Buck-Boost converter. The simulation results confirm that the designed BMS can synchronize the cell equalization via curtailing the SOC estimation error (RMSE 1.20%) productively.

scholarly journals A State of Health Estimation Method for Lithium-Ion Batteries Based on Voltage Relaxation Model

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1349 ◽  
Author(s):  
Qiaohua Fang ◽  
Xuezhe Wei ◽  
Tianyi Lu ◽  
Haifeng Dai ◽  
Jiangong Zhu
Keyword(s):  
Estimation Error ◽  
Estimation Method ◽  
Lithium Ion ◽  
The State ◽  
Estimation Methods ◽  
Model Parameters ◽  
Battery Management System ◽  
Relaxation Model ◽  
State Of Health ◽  
Capacity Estimation

The state of health estimation for lithium-ion battery is a key function of the battery management system. Unlike the traditional state of health estimation methods under dynamic conditions, the relaxation process is studied and utilized to estimate the state of health in this research. A reasonable and accurate voltage relaxation model is established based on the linear relationship between time coefficient and open circuit time for a Li1(NiCoAl)1O2-Li1(NiCoMn)1O2/graphite battery. The accuracy and effectiveness of the model is verified under different states of charge and states of health. Through systematic experiments under different states of charge and states of health, it is found that the model parameters monotonically increase with the aging of the battery. Three different capacity estimation methods are proposed based on the relationship between model parameters and residual capacity, namely the α-based, β-based, and parameter–fusion methods. The validation of the three methods is verified with high accuracy. The results indicate that the capacity estimation error under most of the aging states is less than 1%. The largest error drops from 3% under the α-based method to 1.8% under the parameter–fusion method.

scholarly journals System protection for lithium-ion batteries management system: a review

2019 ◽  
Vol 13 (3) ◽  
pp. 1184 ◽  
Author(s):  
L. Rimon ◽  
Khairul Safuan Muhammad ◽  
S.I. Sulaiman ◽  
AM Omar
Keyword(s):  
Management System ◽  
Short Circuit ◽  
Lithium Ion ◽  
Cell Voltage ◽  
Battery Management System ◽  
Battery Management ◽  
Battery Cell ◽  
Safety Issues ◽  
System Protection ◽  
Faulty Cell

<span>Robustness of a battery management system (BMS) is a crucial issue especially in critical application such as medical or military. Failure of BMS will lead to more serious safety issues such as overheating, overcharging, over discharging, cell unbalance or even fire and explosion. BMS consists of plenty sensitive electronic components and connected directly to battery cell terminal. Consequently, BMS exposed to high voltage potential across the BMS terminal if a faulty cell occurs in a pack of Li-ion battery. Thus, many protection techniques have been proposed since last three decades to protect the BMS from fault such as open cell voltage fault, faulty cell, internal short circuit etc. This paper presents a review of a BMS focuses on the protection technique proposed by previous researcher. The comparison has been carried out based on circuit topology and fault detection technique</span>

scholarly journals Development of Life-cycle Detection and Extending Function for Reusing Automotive Lithium-ion Battery – Verified in 52V LiMnNiCoO2 Platform

2020 ◽  
Author(s):  
Wu-Yang Sean ◽  
Ana Pacheco
Keyword(s):  
Life Cycle ◽  
Lithium Ion Battery ◽  
Management System ◽  
Open Circuit Voltage ◽  
Lithium Ion ◽  
Open Circuit ◽  
Battery Pack ◽  
Battery Management System ◽  
Battery Management ◽  
Cycle Detection

Abstract For reusing automotive lithium-ion battery, an in-house battery management system is developed. To overcome the issues of life cycle and capacity of reused battery, an online function of estimating battery’s internal resistance and open-circuit voltage based on adaptive control theory are applied for monitoring life cycle and remained capacity of battery pack simultaneously. Furthermore, ultracapacitor is integrated in management system for sharing peak current to prolong life span of reused battery pack. The discharging ratio of ultracapacitor is adjusted manually under Pulse-Width-Modulation signal in battery management system. In case study in 52V LiMnNiCoO2 platform, results of estimated open-circuit voltage and internal resistances converge into stable values within 600(s). These two parameters provide precise estimation for electrical capacity and life cycle. It also shows constrained voltage drop both in the cases of 25% to 75% of ultracapacitors discharging ratio compared with single battery. Consequently, the Life-cycle detection and extending functions integrated in battery management system as a total solution for reused battery are established and verified.

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