Learning control policies from heterogeneous multi-horizon time series in battery energy management systems

This paper introduces Representation-to-Decision (R2D), an end-to-end imitation learning framework that maps heterogeneous multi-horizon time-series inputs directly to battery control decisions through modular Temporal Feature Extractors (TFEs) and a shared latent representation, without an explicit load and PV forecasting step. While accurate forecasting improves prediction quality, optimal control performance is not guaranteed in prediction-then-optimization pipelines; standard Reinforcement Learning (RL) lacks the long-horizon temporal awareness due to short-window observations, even when forecast signals are available as additional inputs. R2D offers a different perspective: rather than forecasting first and deciding second, it learns to decide directly from raw temporal inputs, with control optimality anchored by an aging-aware Mixed-Integer Linear Programming (MILP) expert through Behavior Cloning (BC). Benchmarked against six controllers on a high-fidelity electro-thermal battery simulation across five industrial sites, R2D achieves 62–77% of the global clairvoyant optimum, outperforms the tested Model Predictive Control (MPC) and RL benchmarks, and yields battery degradation nearly identical to its MILP teacher across all five sites. Comprehensive ablation studies over temporal backbone, model size, expert formulation, aging-cost weighting, and horizon configuration confirm that a Long Short-Term Memory (LSTM) encoder with a 15-minute single-step control horizon provides the most robust and deployment-ready configuration, and cross-site and single-factor out-of-distribution tests show that generalization to unseen profiles is profile-dependent, with policies trained on high-activity sites transferring most reliably.