Efficiency Degradation Model of Lithium-Ion Batteries for Electric Vehicles

Abstract

The purpose of this paper is to analyse efficiency degradation of lithium-ion batteries. Two lithiumion cell technologies are considered under calendar ageing. It is well known that ageing mechanisms have an impact in cells' performances. Most of studies focus on capacity fade and impedance rise but efficiency is less frequently studied. However, from the application point of view, battery efficiency degradation directly impacts the system energy efficiency. Results reveal the importance of considering battery ageing in the design phase of electric vehicles, not only for capacity but also for efficiency reasons: efficiency degradation depends of the technology, so when comparing two technologies one must take into account the cells' performances not just when cells are fresh but during the whole lifespan. Another finding reported in this paper is the high correlation between capacity fade and energy efficiency for the tested technologies. Finally, two empirical models for energy efficiency degradation were developed in both technologies: the first one is based on Eyring relationships and the second one lies on the existing correlation between capacity fade and efficiency. Quality of each model is reported for both model types and battery technologies.

Current Li-Ion Battery Technologies in Electric Vehicles and Opportunities for Advancements

“…Power is likewise reduced when parasitic reactions occur that convert battery materials to other compounds that act as transport barriers, increasing the cell's internal impedance, and which in turn reduces the operating voltage at each discharge rate [88,89]. A recent report highlights the high correlation between capacity fade and energy efficiency of the cell (i.e., low hysteresis), presumably due to the low impedance of the solid electrolyte interphase of the new cell, and the rate at which this interphase therefore changes [90,91]. This section reviews some of the key issues that can affect the overall State of Health (SOH) of those batteries, e.g., the ability to deliver power compared to a new pack, and provides a review of several current research areas that help address the issues.…”

Modelling Lithium-Ion Battery Ageing in Electric Vehicle Applications—Calendar and Cycling Ageing Combination Effects

“…For the purpose of this work, we are not considering SoC drift influence in the identification process. In fact, a sensible SoC drift influence was found in LFP/C cells [23] but not in NMC/C cells which are studied here [24]. Another important assumption is to consider that C a is independent of the State of Health (SoH), meaning that at each SoC level a value of C a can be found independently of the current capacity loss.…”

“…A parameter identification is performed to fit our model (C a ) to the results of calendar ageing tests ( Figure 2). This task has already been reported in References [23,24]. For the purpose of this work, we are not considering SoC drift influence in the identification process.…”

“…The general form of the Eyring law for a stress factor S 1 is given in Equation 3. A is the pre-exponential factor, n is the temperature exponent, E a is the activation energy, k is the Boltzmann constant, B 1 the direct influence factor of S 1 and C 1 the interaction factor between S 1 and T. This law was used in preceding works to model capacity fade [23,24] as a function of temperature and SoC (S 1 = SoC).…”

Towards Developing a Large Distributed Energy Storage Using a Weighted Batteries Scheduling Scheme

“…However, the degradation in the efficiencies is very small. In a case study [33], it is shown that the efficiency of the li-ion battery degrades from 95% to 94% over the lifetime. Another study [34] also shows similar results.…”