  High-throughput computing (HTC) is a pivotal asset in many scientific fields,
such as biology, material science and machine learning. Applying HTC to the
complex physics-based degradation models of lithium-ion batteries enables
efficient parameter identification and sensitivity analysis, which further
leads to optimal battery design and operating conditions. However, running
physics-based degradation models comes with pitfalls, as solvers can crash or
get stuck in infinite loops due to numerical errors. Also, how to pipeline HTC
for degradation models has seldom been discussed. To fill these gaps, we have
created ParaSweeper, a Python script tailored for HTC, designed to streamline
parameter sweeping by running as many ageing simulations as computational
resources allow, each with different parameters. We have demonstrated the
capability of ParaSweeper based on the open-source platform PyBaMM, and the
approach can also apply to other numerical models which solve partial
differential equations. ParaSweeper not only manages common solver errors, but
also integrates various methods to accelerate the simulation. Using a
high-performance computing platform, ParaSweeper can run millions of
charge/discharge cycles within one day. ParaSweeper stands to benefit both
academic researchers, through expedited model exploration, and industry
professionals, by enabling rapid lifetime design, ultimately contributing to
the prolonged lifetime of batteries.
