Diagnostic and Prognosis of Li-Ion Battery State-of-Health Based on Electrodes Potentials Shifts
Authors: Koné joel-Louis , Montaru Maxime , Fiette Sébastien , Bultel Yann 
Published: February 2020

The degradation mechanisms affecting each electrode of a Li-ion cell are physical and chemical processes widely depicted in the literature [1-4] They lead to the battery loss of performance in terms of energy availability (capacity decay) and power capability (resistance increase). To diagnose the electrode degradation and cell available capacity, many models have been designed [5-8]. These models provide battery state-of-heath (SOH) indicators by simulating various scenarios of degradation modes. In this work, we want to go further and present a new solution not only able to estimate the battery SOH, but also to predict the evolution of the battery available capacity over the next couple of months. This diagnosis/prognosis solution is a key-enabling technology to ensure the storage system reliability of future electric cars.

In our approach, each electrode open circuit potential (OCP) is used to rebuild the cell voltage at low rate. Only three parameters (fig.1) are used to define the cell voltage curve: the positive electrode capacity (Cpos), the negative electrode capacity (Cneg), and the offset (OFS) which represents the shift between both electrode OCPs. The cell capacity is directly related to these three parameters and depends on the voltage range applied during cycling.

To diagnose the cell degradations, a two-step method was proposed (fig.2). First, we fitted the experimental voltage of the cell measured at low rate of discharge (C/10) with the open circuit potential (OCP) of the negative and positive electrodes to identify the values of each of the three parameters (Cneg, Cpos, OFS). Then, we finely tune the identified parameter values from differential voltage curves by ensuring proper peak alignment. The capacity is therefore derived capacity from the final values of the three parameters. This method has been applied on a graphite/LMO-NMC cell for various calendar and cycling ageing conditions, with temperatures ranging from 0 to 60 °C, under low and high discharge rates (C/3, 1C), and different state-of-charge (0 to 100 %). As expected, calendar and cycling conditions have different impacts on the cell ageing and therefore on the electrodes ageing. Figure 3 shows a good agreement between experimental and simulated capacities when identifying the three parameters while Figure 4 shows the evolution of the three parameters (Cneg, Cpos, OFS) during calendar and cycling ageing. These three parameters evolution are path dependent of the ageing test conditions.

For the cell SOH prognosis, we propose a novel empirical ageing model predicting the electrode parameter evolution over the time. It is worth noticing that the Palmgren-Miner rule [9] allows us to cumulate calendar and cycling ageing effects for the total loss of three electrodes parameters in our aging model.

References:

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Figure 1