Reinforcement Learning-Based Cell Balancing for Electric Vehicles
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Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Computer systems and networks (MPCSN), MSc
Publicerad
2024
Författare
MAZZOLO, GIOVANNI
SCHIOPU, MATEI
Modellbyggare
Tidskriftstitel
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Volymtitel
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Sammanfattning
Lithium-ion battery packs are comprised of hundreds to thousands of individual cells
which, even though manufactured uniformly, exhibit small variations in their characteristics
that impact their behavior during operation. These differences cause cells’ State
of Charge (SOC) to become unbalanced, which can, in turn, reduce the capacity utilization
efficiency of the pack [1]. Additionally, battery cells age differently over time,
and fast-aged cells can cause packs with healthy cells to be retired early, without fully
taking advantage of each cell. When a battery has deteriorated to around 80% of its
total capacity, it is retired from electric vehicle usage [2].
To maintain batteries functioning correctly, cell SOC balancing must be done on battery
packs. However, balancing the SOC of cells provides a window of opportunity to also
include cells’ health into the balancing equation, aiming for the homogenization of cell
aging, allowing to thoroughly utilize a battery’s resources. In this way, it is possible to
both keep batteries in operating condition and potentially increase their lifespan.
In this work, we develop and research a multi-cell simulation framework and Reinforcement
Learning (RL) methodologies to explore the potential of cell SOC and health
balancing. We propose an active balancing strategy for re-configurable cell topology
with RL, in which instead of transferring energy between high SOC cells to low SOC
cells, cell utilization is modulated so that the power consumption is optimally distributed
based on each cell’s SOC. This strategy is applied to SOC balancing, as well as SOC and
State of Health (SOH) balancing simultaneously, to potentially allow for an exhaustive
utilization of the battery’s potential.
Beskrivning
Ämne/nyckelord
Battery , cell balancing , reinforcement learning , lithium-ion batteries , automotive , computer science , engineering , deep learning