Lithium-ion batteries are getting larger due to the expansion of transportation and mass storage markets and they can now contain up to thousands of cells. However, a sole damaged cell can significantly impact the whole battery pack efficiency [1]. Thus, the diagnosis of a single cell remains critical for those systems. Many methods exist [2, 3] in which the cell is considered homogeneous. We recently developed a heterogeneous equivalent circuit model that considers a distribution of internal resistances to better represent a real single cell behavior [4, 5]. This resistances distribution (RD) may bring valuable information about a single cell internal quality, but only if it is determined with a sufficient accuracy. In this paper, we propose an algorithm that allows a responsive determination of the RD. The results are compared to other determination methods. This resistances distribution (RD), which is determined thanks to the preliminary construction of a homogenous model and a single discharge, is also valid for other operating conditions. This proves the relevance of the determination method and it should now be usable to detect abnormal evolution of the RD during a single cell lifetime. Although this work is developed for a single cell, it can also be used for several cells connected in parallel and may thus be used to detect a damaged cell inside a battery pack.
In this paper, we propose a non-invasive method to determine the electrode balancing of the lithium-ion batteries, which is the determination of (i) individual electrodes capacities and (ii) individual curves of equilibrium potentials of the electrodes as functions of the battery state of charge. The proposed method requires the average measurements of the battery voltage between discharge and charge for a low current. This averaged voltage is then associated with the reference average curves of the electrode equilibrium potentials issued from literature. To determine the electrode balancing, the method was first used on a pristine LiFePO 4 /graphite cell. The results are consitent with this cell data and the proposed method accuracy is compared to a reference method of the litterature. We then used the proposed method on an aged cell of the same type to evaluate the method robustness with respect to the change in the cell state of health. The tracking of each electrode capacity during the battery lifespan can be used as a non-invasive diagnosis tool to monitor the state of health evolution to each electrode. Furthermore, the access to the individual curves of electrodes equilibrium potentials as functions of the battery state of charge is the first step towards the implementation of a non-invasive tool for an optimal prediction of the battery operating limits during fast recharging.
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