It remains challenging to effectively estimate the remaining capacity of the secondary lithium-ion batteries that have been widely adopted for consumer electronics, energy storage, and electric vehicles. Herein, by integrating regular real-time current short pulse tests with data-driven Gaussian process regression algorithm, an efficient battery estimation has been successfully developed and validated for batteries with capacity ranging from 100% of the state of health (SOH) to below 50%, reaching an average accuracy as high as 95%. Interestingly, the proposed pulse test strategy for battery capacity measurement could reduce test time by more than 80% compared with regular long charge/discharge tests. The short-term features of the current pulse test were selected for an optimal training process. Data at different voltage stages and state of charge (SOC) are collected and explored to find the most suitable estimation model. In particular, we explore the validity of five different machine-learning methods for estimating capacity driven by pulse features, whereas Gaussian process regression with Matern kernel performs the best, providing guidance for future exploration. The new strategy of combining short pulse tests with machine-learning algorithms could further open window for efficiently forecasting lithium-ion battery remaining capacity.

Geothermal heat pumps (GHPs) are a type of heating ventilation and air conditioning (HVAC) systems that use low-temperature resources from soil and groundwater for heating/cooling. In recent years, there has been an increasing interest in GHP systems due to their high energy efficiency and abundant geothermal resources. Thus, the optimization and control design of the GHP system has become a hot topic. On the other hand, as the GHP system is an ideal responsive load, mechanism design for the GHP system to realize demand response (DR) in a virtual power plant (VPP) without affecting user comfort is particularly essential. In this paper, we propose a distributed real-time temperature and energy management method via GHP systems for multi-buildings, where both floor and radiator heating/cooling distribution subsystems in multiple thermal zones are considered. We design an energy demand response mechanism for a single GHP to track the given energy consumption command for participating in VPP aggregation/disaggregation. Besides, a coordination mechanism for multiple GHPs is designed for the community-level operator in joining VPP aggregation/disaggregation. Both designed schemes are scalable and do not need to measure or predict any exogenous disturbances such as outdoor temperature and heating disturbances from external sources, e.g., user activity and device operation. Finally, four numerical examples for the simulation of two different scenarios demonstrate the effectiveness of the proposed methods.