THESIS
2023
1 online resource (xiv, 70 pages) : illustrations (chiefly color)
Abstract
Lithium-ion batteries are used in various products ranging from consumer electronics such as
laptops to larger appliances such as battery energy storage systems (BESS). In Hong Kong,
BESS has been introduced to power tower cranes in the construction sites and the air
conditioning system in the airport. Therefore, accurate battery life prediction is critical to
reducing maintenance frequency and cost. This thesis collects battery data through a battery
cycle test. The batteries' sizes are different (18650 and 21700), and the chemistries (NMC and
NCA) were collected from five lithium battery manufacturers. Divide the cells of each
manufacturer into two groups, each with three battery cells, and test them under different
conditions, charge in constant current-constant voltage (CC-CV) mode,...[
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Lithium-ion batteries are used in various products ranging from consumer electronics such as
laptops to larger appliances such as battery energy storage systems (BESS). In Hong Kong,
BESS has been introduced to power tower cranes in the construction sites and the air
conditioning system in the airport. Therefore, accurate battery life prediction is critical to
reducing maintenance frequency and cost. This thesis collects battery data through a battery
cycle test. The batteries' sizes are different (18650 and 21700), and the chemistries (NMC and
NCA) were collected from five lithium battery manufacturers. Divide the cells of each
manufacturer into two groups, each with three battery cells, and test them under different
conditions, charge in constant current-constant voltage (CC-CV) mode, and discharge in
constant current (CC) mode. This paper uses a neural network and linear regression models
to build a predictive model. The battery data of the training set comes from a single
manufacturer to make a prediction model, while the verification set is added to the other
manufacturers' data. The test results show that the model established by using the neural network has good compatibility. Regardless of whether it is the same manufacturer or
chemical composition, the neural network model has a good prediction effect. It also provides
a new direction for the next generation of battery prediction models.
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