Fast Charging of Li-ion Batteries via Learning and Optimization

Advanced fast charging strategies aim to balance quick charging with battery longevity, considering factors like elevated temperatures that accelerate electrochemical aging, resulting in increased lithium plating, higher mechanical stresses, and an increased growth rate of the solid-electrolyte interface layer. To prevent accelerated aging, we propose optimizing the charging current while adhering to safety constraints. This involves an adaptive control strategy that learns a system’s dynamics from data and optimizes the response accordingly. We utilize full-order dynamics, employing the Doyle-Fuller-Newman model with a thermal component, to comprehensively capture the intricate behaviors of the battery. This methodology led to the development of a hybrid charging solution, enabling a 66% faster charge for a 5Ah 21700 NMC-811 cylindrical cell compared to conventional approaches, all while adherence to safety constraints.

Data-driven Discovery of Governing Equations of Li-ion Batteries Pertaining State of Charge

Lithium-ion batteries (LiBs) have complex electrochemical behaviors, which result in nonlinear and high-dimensional dynamics. The modeling of this complex system often requires models involving PDEs, which are costly to develop and require invasive experiments to identify battery parameters. Here, we propose data-driven techniques to discover nonlinear reduced-order models that govern state-of-charge (SOC) dynamics from non-invasive input/output data. Accurate SOC estimation is paramount for increased performance, improved operational safety, and extended longevity of LiBs. The SOC model is developed from a library of candidate terms via a sparsity-promoting algorithm and data generated by the Doyle-Fuller-Newman model with a thermal model for the characterization of the cell’s thermal effects. Read more … 

Safety Evaluation of Li-ion batteries

Li-ion Batteries (LiBs) are the preferred energy storage solution for many applications, including cellphones and electric vehicles. However, they can pose serious hazards if their safety is compromised, such as after sustaining mechanical damage. We investigate the effects of mechanical damage on the response of LiBs. We use distributed equivalent circuit modeling techniques and distribution of relaxation times and functions in our analyses. Read more …

Optimal Control of an AC75 Sailboat for the America’s Cup Race

This research presents an adaptive control scheme to achieve optimal sailing maneuvers for an AC75 foiling sailboat competing in America’s Cup, the world’s premier sailboat race. The innovative sailboat design introduces extra degrees of freedom and articulations in the boat that result in nonlinear, high-dimensional, and unstable switching dynamics. These complex dynamical characteristics make the optimization of this MIMO (multiple-input multiple-output) system via traditional methods prohibitive. Therefore, we presented an adaptive learning scheme to learn the Jacobian of the system from measurement data, and adapt the commands at each time step to achieve the optimal maneuvers. Read more …

Sample-based methods such as RRT and PRM and their variants have been widely used in robotic applications and beyond. Still, the convergence of such methods is known only for the specific cases of holonomic systems and sub-Riemannian non-holonomic systems. Taking a metric space perspective, we expand the framework for sample-based algorithms commonly used for trajectory planning in such applications and demonstrate the algorithm in both abstract metric spaces and the case of a fleet of collaborative vehicles. Read more …

Co-Design of Cyber Physical Systems

The analysis and synthesis of Cyber-Physical Systems require an integrative approach to studying the underlying physical and cyber components and their intricate and interactive interconnections. When multiple applications on the network have to be controlled, the underlying resources are often shared, which introduces delays in the control messages. These delays can degrade system performance and cause instabilities. We have developed several algorithms to co-design CPS to improve stability and resource utilization. The algorithms range from Adaptive schemes to using Switching Theory methods. We have introduced overrun strategies to effectively design networks and controllers to improve resource utilization by one order of magnitude.

Health Monitoring of Offshore Field Infrastructure

Failure of offshore infrastructure can be catastrophic. We are working on Health-monitoring of offshore infrastructure to determine vulnerabilities before failure occurs. this project is a collaborating work with Professor Sahraei at Temple University, and Professors Lattanzi and Zhang at George Mason University. We look into design hardware for undersea vehicle monitoring devices and predicting failures using advanced Finite Element Modeling.