Abstract:
Electric batteries have gained attention with recent developments in the
transport sector, especially with electric vehicles (EVs) technology and with the rapid
development in the energy storage sector with application to the electricity grid.
Lithium-ion batteries (LIBs) are particularly popular due to their high-power density, high
energy density, low self-discharge rate, and performance. LIB systems are also widely
utilized in extreme operating conditions and harsh environments, and the safe operation
of any battery management system requires rapid detection and accurate diagnosis of
faults. To have an effective fault diagnosis, the nonlinear behavior of battery systems
has been studied in considering the battery real-time operation. In addition, accurate
battery models are used to mimic battery physical processes and predict aging. The
knowledge of battery model parameters plays a crucial role in accurately predicting
performance and ageing. This paper critically reviews different battery models, such as
electrochemical models, equivalent circuit models, and data-driven models. Then, the
parameter extraction methods for the electrochemical model were discussed critically
since it has been identified as the most promising battery model and also the techniques
for the other battery models may rely on these approaches as they can be derived based
on the electrochemical model parameters. According to the literature parameter
estimation for electrochemical models was discussed under the categories of online,
offline, and analytical methods. By the state-of-the-art review conducted, it has been
identified that the mixed method that combines the online and offline methods shows
good performance compared to using them separately. This paper also discusses some
future research directions to obtain better parameter extraction methods for
electrochemical models to facilitate battery fault diagnosis.
