Abstract
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.
Highlights
Since the commercialisation of Lithium-ion batteries, significant improvements in energy density and power capability has made lithium ion batteries a preferred solution for low carbon mobility [1]
We develop an electrochemistry based battery ageing model which extends the work of Doyle Fuller and Newman [5], that combines
A comprehensive summary of expected parameter modifications resulting from battery degradation is beyond the scope of this paper; readers are referred to a forthcoming publication by the authors on “Characterising Li-ion battery degradation through the identification of model parameters.”
Summary
Since the commercialisation of Lithium-ion batteries, significant improvements in energy density and power capability has made lithium ion batteries a preferred solution for low carbon mobility [1]. The data provides a valuation for capacity fade and power fade, detailed interpretation, diagnosis and prognosis of degradation and failure requires mathematical models to be developed. To this end, we develop an electrochemistry based battery ageing model which extends the work of Doyle Fuller and Newman [5], that combines. An important contribution to parameter identification was made by Forman et al [9] who identified the full set of parameters (88 scalars and function control points) of the P2D model using a genetic algorithm using PHEV vehicle drive cycle data.
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