Anirudh Allam

All Publications

• Linearized Versus Nonlinear Observability Analysis for Lithium-Ion Battery Dynamics: Why Respecting the Nonlinearities Is Key for Proper Observer Design IEEE ACCESS Allam, A., Onori, S. 2021; 9: 163431-163440

  View details for DOI 10.1109/ACCESS.2021.3130631

  View details for Web of Science ID 000731135600001

• Pushing the Eenvelope in Battery Estimation Algorithms. iScience Allam, A., Catenaro, E., Onori, S. 2020; 23 (12): 101847

  Abstract

  Accurate estimation of lithium-ion battery health will (a) improve the performance and lifespan of battery packs in electric vehicles, spurring higher adoption rates, (b) determine the actual extent of battery degradation during usage, enabling a health-conscious control, and (c) assess the available battery life upon retiring of the vehicle to re-purpose the batteries for "second-use" applications. In this paper, the real-time validation of an advanced battery health estimation algorithm is demonstrated via electrochemistry, control theory, and battery-in-the-loop (BIL) experiments. The algorithm is an adaptive interconnected sliding mode observer, based on a battery electrochemical model, which simultaneously estimates the critical variables such as the state of charge (SOC) and state of health (SOH). The BIL experimental results demonstrate that the SOC/SOH estimates from the observer converge to an error of 2% with respect to their true values, in the face of incorrect initialization and sensor signal corruption.

  View details for DOI 10.1016/j.isci.2020.101847

  View details for PubMedID 33313491

• Online Capacity Estimation for Lithium-Ion Battery Cells via an Electrochemical Model-Based Adaptive Interconnected Observer IEEE Transactions on Control Systems Technology Allam, A., Onori, S. 2020: 16

  View details for DOI 10.1109/TCST.2020.3017566

• Exploring the dependence of cell aging dynamics on thermal gradient in battery modules: A PDE-based time scale separation approach European Control Conference (ECC) Allam, A., Onori, S. 2019

  View details for DOI 10.23919/ECC.2019.8795843

• An Interconnected Observer for Concurrent Estimation of Bulk and Surface Concentration in the Cathode and Anode of a Lithium-ion Battery IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS Allam, A., Onori, S. 2018; 65 (9): 7311–21

  View details for DOI 10.1109/TIE.2018.2793194

  View details for Web of Science ID 000431397500047

• Characterization of Aging Propagation in Lithium-ion Cells Based on an Electrochemical Model Allam, A., Onori, S., IEEE IEEE. 2016: 3113–18

  View details for Web of Science ID 000388376103031

• Battery Health Management System for Automotive Applications: A retroactivity-based aging propagation study Allam, A., Onori, S., Marelli, S., Taborelli, C., IEEE IEEE. 2015: 703–16

  View details for Web of Science ID 000370259200115

• Lithium-ion battery aging dataset based on electric vehicle real-driving profiles. Data in brief Pozzato, G., Allam, A., Onori, S. 2022; 41: 107995

  Abstract

  This paper describes the experimental dataset of lithium-ion battery cells subjected to a typical electric vehicle discharge profile and periodically characterized through diagnostic tests. Data were collected at the Stanford Energy Control Laboratory, at Stanford University. The INR21700-M50T battery cells with graphite/silicon anode and Nickel-Manganese-Cobalt cathode were tested over a period of 23 months according to the Urban Dynamometer Driving Schedule (UDDS) discharge driving profile and the Constant Current (CC)-Constant Voltage (CV) charging protocol designed at different charging rates - ranging from C/4 to 3C. Ten (10) cells are tested in a temperature-controlled environment (23 ∘ C). A periodic assessment of battery degradation during life testing is accomplished via Reference Performance Tests (RPTs) comprising of capacity, Hybrid Pulse Power Characterization (HPPC), and Electrochemical Impedance Spectroscopy (EIS) tests. The dataset allows for the characterization of battery aging under real-driving scenarios, enabling the development of models and management strategies in electric vehicle applications.

  View details for DOI 10.1016/j.dib.2022.107995

  View details for PubMedID 35252504

• Second-life Lithium-ion batteries: A chemistry-agnostic and scalable health estimation algorithm arXiv preprint: 2203.04249 Takahashi, A., Allam, A., Onori, S. 2022
• Extending Life of Lithium-ion Battery Packs by Taming Heterogeneities via an Optimal Control-based Active Balancing Strategy arXiv preprint: 2203.04226 Azimi, V., Allam, A., Onori, S. 2022
• Offline multiobjective optimization for fast charging and reduced degradation in lithium ion battery cells Lam, F., Allam, A., Joe, W., Choi, Y., Onori, S., IEEE IEEE. 2021: 4441-4446

  View details for Web of Science ID 000702263304084

• Fast Charging-Minimum Degradation Optimal Control of Series-Connected Battery Modules with DC/DC Bypass Converters IEEE American Control Conference (ACC) Azimi, V., Allam, A., Joe, W., Choi, Y., Onori, S. 2021

  View details for DOI 10.23919/ACC50511.2021.9482982

• Stochastic capacity loss and remaining useful life models for lithium-ion batteries in plug-in hybrid electric vehicles JOURNAL OF POWER SOURCES Chu, A., Allam, A., Arenas, A., Rizzoni, G., Onori, S. 2020; 478

  View details for DOI 10.1016/j.jpowsour.2020.228991

  View details for Web of Science ID 000589931500002

• Offline Multiobjective Optimization for Fast Charging and Reduced Degradation in Lithium-ion Battery Cells using Electrochemical Dynamics IEEE Control Systems Letters Lam, F., Allam, A., Joe, W., Choi, Y., Onori, S. 2020

  View details for DOI 10.1109/LCSYS.2020.3046378

• A Novel Lithium-ion Battery Pack Modeling Framework - Series-Connected Case Study Weaver, T., Allam, A., Onori, S., IEEE IEEE. 2020: 365–72

  View details for Web of Science ID 000618079800053