Incremental Capacity Analysis for Electric Vehicle Battery State-of-Health Estimation

Calearo

2020 55th International Universities Power Engineering Conference (UPEC)

et al. 2020

This paper presents a characterization of Nickel Manganese Cobalt (NMC) Lithium-Ion (Li-ion) batteries based on empirical tests to improve online state estimation. It investigates how battery degradation affects the battery management systems (BMS) ability to estimate the state of charge (SOC) and state of health (SOH). The testing is conducted on 8 NMC 18650 Li-ion battery cells split up in two groups of four new and four degraded cells with an SOH of 90%. As both the SOC and the SOH estimations are dependent on the relation between the SOC and the open circuit voltage (OCV), the necessary relaxation time to achieve a stable OCV as well as the SOC-OCV relations dependency on temperature and ageing are derived. The relaxation time needed to achieve a stabilised terminal voltage is, in the worst case, found to be equal to 20 minutes for both the new and the degraded cells. The SOC-OCV curve is not changing significantly when the operation temperature is increased from 23 • C to 45 • C, which is also the case for both the new and the degraded cells. The internal battery resistance and capacitance are however found to depend significantly on both the SOC and operation temperature. A first order RC representation is derived for different SOC and temperatures levels.

Section: Methodsmentioning

confidence: 99%

Characterization of NMC Lithium-ion Battery Degradation for Improved Online State Estimation

Calearo

2020 55th International Universities Power Engineering Conference (UPEC)

et al. 2020

“…In order to avoid noise in the the IC, the voltage and current have been through a moving average filter with a duration of 300 s before Eq. ( 8) has been applied [16]. The IC calculation is only considered during the constant current charging mode, in order to avoid variation of the voltage due to the internal resistance of the battery.…”

Section: G Incremental Capacity Analysismentioning

confidence: 99%

“…As the battery experiences degradation, the peaks of the (IC,V) curve will change. It can then be compared to other IC analysed cells with known SOH, and thereby the SOH of the battery can be derived [16]. The bottleneck of the ICA is that empirical lifetime analysis for a reference of a certain battery type would require years of studying, which is impractical and would generate reliable results only when the battery has aged [17].…”

Section: Introductionmentioning

confidence: 99%

“…The ICA method is conventionally used on battery cells, however it needs to be feasible on pack or EV level and not only on individual cell level. Nickel Manganese Cobalt (NMC) based cells for the BMW i3 EV has been tested both on cell level and on car level and the results shows consistent characteristic peaks and valleys of the ICA on car and cell level [16,18].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Empirical Capacity Measurements of Electric Vehicles Subject to Battery Degradation From V2G Services

IEEE Trans. Veh. Technol.

Calearo

Andersen

et al. 2021

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“…Therefore, achieving accurate and effective SOH estimation remains a challenging task [11]. To be practical, SOH estimation methods must be practicable at the battery pack or EV level as well as at the individual cell level [12].…”

Section: Introductionmentioning

confidence: 99%

Internal Parameter Estimation of Lithium-Ion Battery Using AC Ripple With DC Offset Wave in Low and High Frequencies

2021

xEV batteries are not easy to accurately estimate and measure aging parameters for reuse due to continuous charging and discharging. In addition, existing AC-IR measurement methods require high-performance micro-controller units (MCUs) to handle complex operations for parametric estimation. As a cost-effective way to solve this problem, we propose AC-IR estimation techniques and implementation circuits to estimate internal battery parameters using AC ripple with DC offset wave in low and high frequencies. It estimates electrolyte resistance (Ri) by using band-pass filter (BPF) to extract AC components of a particular frequency voltage and current and by entering very high frequencies that have the effect of shorting the equivalent capacitance by the electrical double layer (Cd) of the Randls model. The charge transfer resistance (Rd) and electrical double layer capacitance (Cd) are estimated by Discrete Fourier transform (DFT), detecting effective and reactive current by inserting low frequencies. To realize the proposed approach, we propose analog power measurements and peak detection circuits, and a circuit configuration of converter supplying AC ripple with DC offset wave to estimate internal parameters at the same time to charge battery. To verify the feasibility and high-performance of the proposed AC-IR estimation method, we carry out PSIM simulations and experiments, and compare results with commercial battery parameter measuring instrument. As a result, we found that the error rate of the proposed method is lower than that of the commercial instrument as Ri is 0.24% and Rd is 1.18%, respectively.