PATH

Probabilistic Seismic Hazard Analysis for Spatially Distributed Infrastructure Considering the Correlation of Spectral Acceleration Across Spectral Periods

Kavvada, Ioanna
Moura, Scott
Horvath, Arpad
Abrahamson, Norman
2022

Regional seismic hazard analyses are necessary to assess the infrastructure performance within a region and ensure that mitigation funds are utilized effectively by probabilistically considering the suite of potential earthquake events. This research aims to efficiently represent the regional seismic hazard through a compact set of seismic inputs in the form of spectral acceleration (SA) maps by considering the spatial cross-correlation of SA at a wide period range. The SA maps can then be used to probabilistically estimate the performance of a portfolio of spatially distributed structures...

PATH Researcher John Spring Retires

July 16, 2025

John Spring Retirement Congratulations to Partners for Advanced Transportation Technology (PATH) Research and Development Engineer John Spring on his retirement! He worked at Berkeley for 26 years, with 19 years at PATH. Most recently, he worked on the Cooperative Adaptive Cruise Control (CCAC) Truck Platooning Project.

John has been a real-time...

Pack Level State-of-Power Prediction for Heterogeneous Cells

Dangwal, Chitra
Zhang, Dong
Couto, Luis D.
Gill, Preet
Sebastien, Benjamin
Zeng, Wente
Moura, Scott J.
2022

Accurate prediction of available power in battery packs is crucial for managing performance in automotive and grid storage applications. A battery pack is composed of many cells, which have inherent cell-to-cell variation. This not only complicates the power estimation problem, but also adds complexity in ensuring that all cells remain in a safe operating regime. This paper presents a methodology to estimate the state of power (SOP) of a battery pack, composed of series connected heterogeneous cells. The presented SOP framework combines an interval prediction algorithm, with a modified...

Exploration vs. Exploitation in Airborne Wind Energy Systems via Information-Directed Sampling Control

Goujard, Guillaume
Keyantuo, Patrick
Badoual, Mathilde
Moura, Scott J.
2022

Airborne Wind Energy systems (AWEs) are an emerging wind generation technology. They differ from conventional turbines in that they are attached to the ground by a tether and can evolve from low to high altitudes (approx. 1km). Informed altitude control of AWEs is key to track favorable wind speed and maximize power output in a time-varying and partially-observable environment. Leveraging recent advances in Multi-Armed Bandit problems, we recursively estimate the wind profile distribution and use the residuals to fit the noise covariance in an online fashion. This filtering approach paves...

A Deep Reinforcement Learning Framework for Fast Charging of Li-Ion Batteries

Park, Saehong
Pozzi, Andrea
Whitmeyer, Michael
Perez, Hector
Kandel, Aaron
Kim, Geumbee
Choi, Yohwan
Joe, Won Tae
Raimondo, Davide M.
Moura, Scott
2022

One of the most crucial challenges faced by the Li-ion battery community concerns the search for the minimum time charging without damaging the cells. This goal can be achieved by solving a large-scale constrained optimal control problem, which relies on accurate electrochemical models. However, these models are limited by their high computational cost, as well as identifiability and observability issues. As an alternative, simple output-feedback algorithms can be employed, but their performance strictly depends on trial and error tuning. Moreover, particular techniques have to be adopted...

A Neural Network-Based Approximation of Model Predictive Control for a Lithium-Ion Battery with Electro-Thermal Dynamics

Pozzi, Andrea
Moura, Scott
Toti, Daniele
2022

Lithium-ion batteries are complex systems that require suitable management strategies to work properly, achieve fast charging, mitigate ageing mechanisms and guarantee safety. Among the different model-based charging strategies, the use of predictive control has shown promising results, due to its ability to deal with nonlinear systems subject to safety constraints. However, although many implementations have been proposed in the literature, little attention has been paid to their practical feasibility, which is limited by the high computational cost required online. In this paper, we...

Customer-Centric Method and System for Pricing Options and Pricing/Charging Co-Optimization at Multiple Plug-In Electric Vehicle Charging Stations

Moura, Scott
Zeng, Teng
Bae, Sangjae
Zeng, Wente
Lenox, Carl
Travacca, Bertrand
2022

A station-level framework to operate one or multiple plug-in electric vehicle (PEV) charging stations with optimal pricing policy and charge scheduling, which incorporates human behavior to capture the driver charging decision process. The user is presented with menu of price-differentiated charging services, which differ in per-unit price and the energy delivery schedule. Involving human in the loop dynamics, the operation model results in the alleviation of the overstay issue may occur when a charging session has completed. A multi-block convex transformation is used to reformulate...

Reinforcement Learning Versus PDE Backstepping and PI Control for Congested Freeway Traffic

Yu, Huan
Park, Saehong
Bayen, Alexandre
Moura, Scott
Krstic, Miroslav
2022

We develop reinforcement learning (RL) boundary controllers to mitigate stop-and-go traffic congestion on a freeway segment. The traffic dynamics of the freeway segment are governed by a macroscopic Aw–Rascle–Zhang (ARZ) model, consisting of 2 \times 2 quasi-linear partial differential equations (PDEs) for traffic density and velocity. The boundary stabilization of the linearized ARZ PDE model has been solved by PDE backstepping, guaranteeing spatial L<sup>2</sup> norm regulation of the traffic state to uniform density and velocity and ensuring that traffic oscillations are...

Deep Learning-Based Predictive Control for the Optimal Charging of a Lithium-Ion Battery with Electrochemical Dynamics

Pozzi, Andrea
Moura, Scott
Toti, Daniele
2022

The fast charging of a lithium-ion battery is a complex task, which needs to be addressed by a proper control methodology to find the highest charging current while guaranteeing safety. Among the different approaches, model predictive control appears particularly suitable due to its ability in dealing with nonlinear systems and constraints. However, its use in a realistic scenario is limited due to the high computational burden required by the online solution of an optimal control problem. To overcome this issue, we consider a neural network-based algorithm, which can reduce the online...

Faster and Healthier Charging of Lithium-Ion Batteries via Constrained Feedback Control

Couto, Luis D.
Romagnoli, Raffaele
Park, Saehong
Zhang, Dong
Moura, Scott J.
Kinnaert, Michel
Garone, Emanuele
2022

A constrained feedback control strategy designed on the basis of a simplified electrochemical–thermal model is considered for the fast and healthy charging of a lithium-ion battery cell. The constraints ensure avoidance of side reactions and operating modes that yield premature aging (healthier charging). They are enforced through a reference governor approach, hence requiring a low computational burden. A systematic approach is presented for model identification and control law design. The method is first validated on a detailed battery simulator based on the Doyle–Fuller–Newman model...