Characterising Li-ion battery degradation through the identification of perturbations in electrochemical battery models

Lithium ion batteries undergo complex electrochemical and mechanical degradation. This complexity is pronounced in applications such as electric vehicles where highly demanding cycles of operation and varying environmental conditions lead to non-trivial interactions of ageing stress factors. This work presents the framework for an ageing diagnostic tool based on identifying the physical parameters of a fundamental electrochemistry-based battery model from non-invasive voltage/current cycling tests. Exploiting the embedded symbolic manipulation tool and global optimiser in MapleSim, computational cost is reduced, significantly facilitating rapid optimisation. The diagnostic tool is used to study the degradation of a 3Ah LiC₆/LiNiCoAlO₂ battery stored at 45℃ at 50% State of Charge for 202 days; the results agree with expected battery degradation.