Forecasting Lithium-Ion Battery Longevity with Limited Data
  Availability: Benchmarking Different Machine Learning Algorithms
Hudson Hilal Pramit Saha
As the use of Lithium-ion batteries continues to grow, it becomes
increasingly important to be able to predict their remaining useful life. This
work aims to compare the relative performance of different machine learning
algorithms, both traditional machine learning and deep learning, in order to
determine the best-performing algorithms for battery cycle life prediction
based on minimal data. We investigated 14 different machine learning models
that were fed handcrafted features based on statistical data and split into 3
feature groups for testing. For deep learning models, we tested a variety of
neural network models including different configurations of standard Recurrent
Neural Networks, Gated Recurrent Units, and Long Short Term Memory with and
without attention mechanism. Deep learning models were fed multivariate time
series signals based on the raw data for each battery across the first 100
cycles. Our experiments revealed that the machine learning algorithms on
handcrafted features performed particularly well, resulting in 10-20% average
mean absolute percentage error. The best-performing algorithm was the Random
Forest Regressor, which gave a minimum 9.8% mean absolute percentage error.
Traditional machine learning models excelled due to their capability to
comprehend general data set trends. In comparison, deep learning models were
observed to perform particularly poorly on raw, limited data. Algorithms like
GRU and RNNs that focused on capturing medium-range data dependencies were less
adept at recognizing the gradual, slow trends critical for this task. Our
investigation reveals that implementing machine learning models with
hand-crafted features proves to be more effective than advanced deep learning
models for predicting the remaining useful Lithium-ion battery life with
limited data availability.