Honors & Awards

  • Centennial Teaching Assistant Award, Stanford University (2020)

Stanford Advisors


  • Anirudh Allam, Simona Onori. "United States Patent WO/2020/186269 BATTERY MONITORING SYSTEM", LELAND STANFORD JUNIOR UNIVERSITY, Mar 16, 2020
  • Anirudh Allam, Ashokkumar Velusamy. "India Patent 3652/CHE/2012 DETECTION OF UNDER-INFLATED TIRES", NISSAN MOTOR CO. LTD., Jul 3, 2015
  • Anirudh Allam, Ashokkumar Velusamy. "India Patent 3653/CHE/2012 DETECTION OF UNDER-INFLATED TIRES", NISSAN MOTOR CO. LTD., Jul 3, 2015

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


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


    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

  • 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
  • Second-life Lithium-ion batteries: A chemistry-agnostic and scalable health estimation algorithm arXiv preprint: 2203.04249 Takahashi, A., 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
  • 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
  • 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
  • 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
  • A Novel Lithium-ion Battery Pack Modeling Framework - Series-Connected Case Study Weaver, T., Allam, A., Onori, S., IEEE IEEE. 2020: 365–72