Study on the Characteristics of a High Capacity Nickel Manganese Cobalt Oxide (NMC) Lithium-Ion Battery—An Experimental Investigation

Abstract

The use of high-capacity batteries as the battery pack of electric vehicles is the current development trend. In order to better design battery packages and battery management systems and develop related battery estimation technology, the related characteristics of high capacity battery cells need to be studied in depth. Capacity and pulse tests of batteries at different temperatures are carried out in this paper. Through these experimental data, the electrical characteristics of different parameters of the high capacity battery, such as capacity characteristic data, internal resistance characteristic data, OCV-SOC characteristic relation curve, power data and temperature rise are analyzed. The specific parameters of the battery in the second order equivalent circuit model are obtained by using the off-line parameter identification method. These parameters results can be used as comparison data and reference data. It is beneficial to the on-line parameter identification of battery model and the estimation of battery state, so as to shorten the development time and improve the quality of the development.

Novel Parameter Identification Method for Lithium-Ion Batteries Based on Curve Fitting

“…Usually, ΔSoC is in the range of 10% or 5%. Sometimes, a smaller ΔSoC is preferred outside [10%÷90%] SoC interval, because parameters tend to vary more in [0%÷10%] and [90%÷100%] intervals [8]. The choice of ΔSoC represents a compromise between the total experiment time and desired accuracy.…”

Analysis of the Current Electric Battery Models for Electric Vehicle Simulation

“…Resistance is constant too, independent from SoC or temperature. In this sense, it has to be noted that, in a real battery, the resistance is highly dependent on battery type, SoC and SoH state, and temperature ( Figure 4 [18,19]). Generally, resistance increases when SoC lowers ( Figure 4a), SoH lowers (degradation increases) ( Figure 4b), and temperature lowers ( Figure 4c).…”

Online Parameter Identification of Lithium-Ion Batteries Using a Novel Multiple Forgetting Factor Recursive Least Square Algorithm

“…The battery was discharged with a constant current and rested for 2 h. The internal resistance was calculated from the instantaneous terminal voltage change Δ t U after the disappearance of the constant discharge current i as expressed in Equation (21). Then in the rest period, the variation of terminal voltage t U can be deduced as Equation (22) according to Equations (1) and (2), the polarization resistance and capacity were derived from the exponential curve fitting method. It is noteworthy to mention that the internal resistance identified offline is more reliable while other offline parameters may have considerable errors [48], nonetheless, the order of magnitude is a worthy point of reference as analyzed in Section 3.4.…”

“…However, it should be noted that the values of the model parameters are not constant, instead, they are affected by various factors depending on the cell chemistry, including the ambient temperature, the current and voltage profiles, and the aging of the battery. For example, the aging state plays a fundamental role in the parameters related to the hysteresis effect in lithium iron phosphate batteries [21], while the model parameters of nickel manganese cobalt oxide batteries are greatly influenced by temperatures [22]. The discrepancies between offline data and actual value of model parameters may cause significant impact on the accuracy of model output.…”