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Large-scale battery energy storage systems (BESS) play a pivotal role in advancing sustainability through their widespread applications in electrified transportation, power grids, and renewable energy systems. However, achieving optimal power management for these systems poses significant computational challenges. To address this, we propose a scalable approach that partitions the cells of a large-scale BESS into clusters based on state-of-charge (SoC), temperature, and internal resistance. Each cluster is represented by a model that approximates its collective SoC and temperature dynamics and overall power losses during charging and discharging. Using these clusters, we formulate a receding-horizon optimal power control problem to minimize power losses while promoting SoC and temperature balancing. The optimization determines a power quota for each cluster, which is then distributed among its constituent cells. This clustering approach drastically reduces computational costs by working with a smaller number of clusters instead of individual cells, enabling scalability for large-scale BESS. Simulations show a computational overhead reduction of over 60% for small-scale and 98% for large-scale BESS compared to conventional cell-level optimization. Experimental validation using a 20-cell prototype further underscores the approach's effectiveness and practical utility. 

Essential to various practical applications of lithium-ion batteries is the availability of accurate equivalent circuit models. This paper presents a new coupled electro-thermal model for batteries and studies how to extract it from data. We consider the problem of maximum likelihood parameter estimation, which, however, is nontrivial to solve as the model is nonlinear in both its dynamics and measurement. We propose to leverage the Bayesian optimization approach, owing to its machine learning-driven capability in handling complex optimization problems and searching for global optima. To enhance the parameter search efficiency, we dynamically narrow and refine the search space in Bayesian optimization. The proposed system identification approach can efficiently determine the parameters of the coupled electro-thermal model. It is amenable to practical implementation, with few requirements on the experiment, data types, and optimization setups, and well applicable to many other battery models. 

Lithium-ion battery packs consist of a varying number of single cells, designed to meet specific application requirements for output voltage and capacity. Effective fault diagnosis in these battery packs is an essential prerequisite for ensuring their safe and reliable operation. To address this need, we introduce a novel model-based fault diagnosis approach. Our approach distinguishes itself by leveraging informative structural properties inherent in battery packs such as uniformity among the constituent cells, and sparsity of fault occurrences to enhance its fault diagnosis capabilities. The proposed approach formulates a moving horizon estimation (MHE) problem, incorporating such structural information to estimate different fault signals—specifically, internal short circuits, external short circuits, and voltage and current sensors faults. We conduct various simulations to evaluate the performance of the proposed approach under different fault types and magnitudes. The obtained results validate the proposed approach and promise effective fault diagnosis for battery packs. 

This paper presents an integrated motion planning system for autonomous vehicle (AV) parking in the presence of other moving vehicles. The proposed system includes 1) a hybrid environment predictor that predicts the motions of the surrounding vehicles and 2) a strategic motion planner that reacts to the predictions. The hybrid environment predictor performs short-term predictions via an extended Kalman filter and an adaptive observer. It also combines short-term predictions with a driver behavior cost-map to make long-term predictions. The strategic motion planner comprises 1) a model predictive control-based safety controller for trajectory tracking; 2) a search-based retreating planner for finding an evasion path in an emergency; 3) an optimization-based repairing planner for planning a new path when the original path is invalidated. Simulation validation demonstrates the effectiveness of the proposed method in terms of initial planning, motion prediction, safe tracking, retreating in an emergency, and trajectory repairing.