Torsten Wik

Yicun Huang, Changfu Zou, Yang Li et al.

The concept of integrating physics-based and data-driven approaches has become popular for modeling sustainable energy systems. However, the existing literature mainly focuses on the data-driven surrogates generated to replace physics-based models. These models often trade accuracy for speed but lack the generalizability, adaptability, and interpretability inherent in physics-based models, which are often indispensable in modeling real-world dynamic systems for optimization and control purposes. We propose a novel machine learning architecture, termed model-integrated neural networks (MINN), that can learn the physics-based dynamics of general autonomous or non-autonomous systems consisting of partial differential-algebraic equations (PDAEs). The obtained architecture systematically solves an unsettled research problem in control-oriented modeling, i.e., how to obtain optimally simplified models that are physically insightful, numerically accurate, and computationally tractable simultaneously. We apply the proposed neural network architecture to model the electrochemical dynamics of lithium-ion batteries and show that MINN is extremely data-efficient to train while being sufficiently generalizable to previously unseen input data, owing to its underlying physical invariants. The MINN battery model has an accuracy comparable to the first principle-based model in predicting both the system outputs and any locally distributed electrochemical behaviors but achieves two orders of magnitude reduction in the solution time.

Huang Zhang, Yang Su, Faisal Altaf et al.

Battery cycle life prediction using early degradation data has many potential applications throughout the battery product life cycle. For that reason, various data-driven methods have been proposed for point prediction of battery cycle life with minimum knowledge of the battery degradation mechanisms. However, managing the rapidly increasing amounts of batteries at end-of-life with lower economic and technical risk requires prediction of cycle life with quantified uncertainty, which is still lacking. The interpretability (i.e., the reason for high prediction accuracy) of these advanced data-driven methods is also worthy of investigation. Here, a Quantile Regression Forest (QRF) model, having the advantage of not assuming any specific distribution of cycle life, is introduced to make cycle life range prediction with uncertainty quantified as the width of the prediction interval, in addition to point predictions with high accuracy. The hyperparameters of the QRF model are optimized with a proposed alpha-logistic-weighted criterion so that the coverage probabilities associated with the prediction intervals are calibrated. The interpretability of the final QRF model is explored with two global model-agnostic methods, namely permutation importance and partial dependence plot.

Francesco Popolizio, Torsten Wik, Chih Feng Lee et al.

This paper investigates the economic impact of vehicle-home-grid integration through an online optimization algorithm that manages energy flows between an electric vehicle, a household, and the electrical grid. The algorithm exploits vehicle-to-home (V2H) for self-consumption and vehicle-to-grid (V2G) for energy trading, adapting in real-time via a hybrid long short-term memory (LSTM) network for household load prediction and a nonlinear battery degradation model including cycle and calendar aging. Simulations show annual economic benefits up to EUR 3046.81 compared to smart unidirectional charging, despite a modest 1.96% increase in battery aging. Even under unfavorable market conditions, with no V2G revenue, V2H alone provides yearly savings of EUR 425.48. Sensitivity analyses on battery capacity, household load, and price ratios confirm the consistent benefits of bidirectional energy exchange, highlighting the role of EVs as active energy nodes for sustainable management.