Researchers introduce a novel approach enabling on-device learning in resource-constrained electric vehicle systems to continuously adapt pretrained battery prediction models to new, unseen data.
- The method transforms existing pretrained models into adaptable versions that retain critical hyperparameter knowledge from initial training.
- The study investigates both online and offline model adaptation strategies for time-series forecasting.
- Results show mean absolute error reductions of up to 7.49% with online adaptation and 14.88% with offline adaptation techniques.
This approach highlights the substantial benefit of on-device adaptation, resulting in enhanced battery power predictions compared to unadapted model deployments in real-world scenarios.