Comparing seven methods for state-of-health time series prediction for the lithium-ion battery packs of forklifts

TL;DR

This study compares seven machine learning methods, including gradient boosting and neural networks, for predicting the state-of-health of lithium-ion forklift batteries, demonstrating improved accuracy and generalization with a novel validation approach.

Contribution

It introduces a comprehensive comparison of multiple prediction methods for battery SoH, utilizing a unique dataset and a novel walk-forward validation with confidence intervals.

Findings

Gradient boosting outperformed other methods in prediction accuracy.

The best model generalized well across different battery packs.

Predictions and confidence intervals were validated effectively.

Abstract

A key aspect for the forklifts is the state-of-health (SoH) assessment to ensure the safety and the reliability of uninterrupted power source. Forecasting the battery SoH well is imperative to enable preventive maintenance and hence to reduce the costs. This paper demonstrates the capabilities of gradient boosting regression for predicting the SoH timeseries under circumstances when there is little prior information available about the batteries. We compared the gradient boosting method with light gradient boosting, extra trees, extreme gradient boosting, random forests, long short-term memory networks and with combined convolutional neural network and long short-term memory networks methods. We used multiple predictors and lagged target signal decomposition results as additional predictors and compared the yielded prediction results with different sets of predictors for each method. For this work, we are in possession of a unique data set of 45 lithium-ion battery packs with large variation in the data. The best model that we derived was validated by a novel walk-forward algorithm that also calculates point-wise confidence intervals for the predictions; we yielded reasonable predictions and confidence intervals for the predictions. Furthermore, we verified this model against five other lithium-ion battery packs; the best model generalised to greater extent to this set of battery packs. The results about the final model suggest that we were able to enhance the results in respect to previously developed models. Moreover, we further validated the model for extracting cycle counts presented in our previous work with data from new forklifts; their battery packs completed around 3000 cycles in a 10-year service period, which corresponds to the cycle life for commercial Nickel-Cobalt-Manganese (NMC) cells.