scholarly journals Incremental Capacity Analysis as a State of Health Estimation Method for Lithium-Ion Battery Modules with Series-Connected Cells

Batteries ◽  
2020 ◽  
Vol 7 (1) ◽  
pp. 2
Author(s):  
Amelie Krupp ◽  
Ernst Ferg ◽  
Frank Schuldt ◽  
Karen Derendorf ◽  
Carsten Agert
Keyword(s):  
Lithium Iron Phosphate ◽  
Estimation Method ◽  
Lithium Ion ◽  
Iron Phosphate ◽  
Capacity Analysis ◽  
State Of Health ◽  
Incremental Capacity ◽  
Li Ion ◽  
Parallel Connections

Incremental capacity analysis (ICA) has proven to be an effective tool for determining the state of health (SOH) of Li-ion cells under laboratory conditions. This paper deals with an outstanding challenge of applying ICA in practice: the evaluation of battery series connections. The study uses experimental aging and characterization data of lithium iron phosphate (LFP) cells down to 53% SOH. The evaluability of battery series connections using ICA is confirmed by analytical and experimental considerations for cells of the same SOH. For cells of different SOH, a method for identifying non-uniform aging states on the modules’ IC curve is presented. The findings enable the classification of battery modules with series and parallel connections based on partial terminal data.

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.

State-of-Health Online Estimation for Li-Ion Battery

2020 ◽  
Vol 9 (2) ◽  
pp. 185-196
Author(s):  
Liu Fang ◽  
◽  
Liu Xinyi ◽  
Su Weixing ◽  
Chen Hanning ◽  
...  
Keyword(s):  
Estimation Method ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Estimation Methods ◽  
Battery Management ◽  
State Of Health ◽  
Li Ion Battery ◽  
Improved Genetic Algorithm ◽  
Battery Management Systems ◽  
Li Ion

To realize a fast and high-precision online state-of-health (SOH) estimation of lithium-ion (Li-Ion) battery, this article proposes a novel SOH estimation method. This method consists of a new SOH model and parameters identification method based on an improved genetic algorithm (Improved-GA). The new SOH model combines the equivalent circuit model (ECM) and the data-driven model. The advantages lie in keeping the physical meaning of the ECM while improving its dynamic characteristics and accuracy. The improved-GA can effectively avoid falling into a local optimal problem and improve the convergence speed and search accuracy. So the advantages of the SOH estimation method proposed in this article are that it only relies on battery management systems (BMS) monitoring data and removes many assumptions in some other traditional ECM-based SOH estimation methods, so it is closer to the actual needs for electric vehicle (EV). By comparing with the traditional ECM-based SOH estimation method, the algorithm proposed in this article has higher accuracy, fewer identification parameters, and lower computational complexity.

Sign in / Sign up

Export Citation Format

Share Document