arXiv subjects

Prashanth Ramesh

Publications and source records attributed to Prashanth Ramesh.

8 recordsLinked to original sources

arXiv2025

Optimal Design of Experiment for Electrochemical Parameter Identification of Li-ion Battery via Deep Reinforcement Learning

Accurate parameter estimation in electrochemical battery models is essential for monitoring and assessing the performance of lithium-ion batteries (LiBs). This paper presents a novel approach that combines deep reinforcement learning (DRL) with an optimal experimental design (OED) framework to identify key electrochemical parameters of LiB cell models. The proposed method utilizes the twin delayed deep deterministic policy gradient (TD3) algorithm to optimize input excitation, thereby increasing the sensitivity of the system response to electrochemical parameters. The performance of this DRL-based approach is evaluated against a nonlinear model predictive control (NMPC) method and conventional tests. Results indicate that the DRL-based method provides superior information content, reflected in higher Fisher information (FI) values and lower parameter estimation errors compared to the NMPC design and conventional test practices. Additionally, the DRL approach offers a substantial reduction in experimental time and computational resources.

eess.SY↗

arXiv2025

Real-Time Optimal Design of Experiment for Parameter Identification of Li-Ion Cell Electrochemical Model

Accurately identifying the parameters of electrochemical models of li-ion battery (LiB) cells is a critical task for enhancing the fidelity and predictive ability. Traditional parameter identification methods often require extensive data collection experiments and lack adaptability in dynamic environments. This paper describes a Reinforcement Learning (RL) based approach that dynamically tailors the current profile applied to a LiB cell to optimize the parameters identifiability of the electrochemical model. The proposed framework is implemented in real-time using a Hardware-in-the-Loop (HIL) setup, which serves as a reliable testbed for evaluating the RL-based design strategy. The HIL validation confirms that the RL-based experimental design outperforms conventional test protocols used for parameter identification in terms of both reducing the modeling errors on a verification test and minimizing the duration of the experiment used for parameter identification.

eess.SY↗

arXiv2024

Improving Low-Fidelity Models of Li-ion Batteries via Hybrid Sparse Identification of Nonlinear Dynamics

Accurate modeling of lithium ion (li-ion) batteries is essential for enhancing the safety, and efficiency of electric vehicles and renewable energy systems. This paper presents a data-inspired approach for improving the fidelity of reduced-order li-ion battery models. The proposed method combines a Genetic Algorithm with Sequentially Thresholded Ridge Regression (GA-STRidge) to identify and compensate for discrepancies between a low-fidelity model (LFM) and data generated either from testing or a high-fidelity model (HFM). The hybrid model, combining physics-based and data-driven methods, is tested across different driving cycles to demonstrate the ability to significantly reduce the voltage prediction error compared to the baseline LFM, while preserving computational efficiency. The model robustness is also evaluated under various operating conditions, showing low prediction errors and high Pearson correlation coefficients for terminal voltage in unseen environments.

eess.SY↗

arXiv2024

Magnetotaxis in droplet microswimmers

Magnetotaxis is a well known phenomenon in swimming microorganisms which sense magnetic fields e.g. by incorporating crystalline magnetosomes. In designing artificial active matter with tunable dynamics, external magnetic fields can provide a versatile method for guidance. Here, it is the question what material properties are necessary to elicit a significant response. In this working paper, we document in experiments on self-propelling nematic microdroplets that the weak diamagnetic torques exerted by a sub-Tesla magnetic field are already sufficient to significantly affect the dynamics of these swimmers. We find a rich and nontrivial variety of dynamic modes by varying droplet size, state of confinement and magnetic field strength.

cond-mat.soft↗

arXiv2024

Diagnosing and Decoupling the Degradation Mechanisms in Lithium Ion Cells: An Estimation Approach

Understanding battery degradation in electric vehicles (EVs) under real-world conditions remains a critical yet under-explored area of research. Central to this investigation is the challenge of estimating the specific degradation modes in aged cells with no available information on usage history, bypassing the conventional yet invasive method of tear-down tests. Using an electrochemical model, this study pioneers a methodology to decouple and isolate the aging mechanisms in batteries sourced from EVs with varying mileages. A robust correlation is established between the model parameters and distinct degradation processes, enabling the diagnosis and estimation of each mechanism's impact on the battery's parameters. This paper sheds light on battery degradation in real-world scenarios and demonstrates the feasibility of their identification, isolation, and approximate quantification of their effects.

math.OC↗

arXiv2023

Arrested on heating: controlling the motility of active droplets by temperature

Self-propelling active matter relies on the conversion of energy from the undirected, nanoscopic scale to directed, macroscopic motion. One of the challenges in the design of synthetic active matter lies in the control of dynamic states, or motility gaits. Here, we present an experimental system of self-propelling droplets with thermally controllable and reversible dynamic states, from unsteady over meandering to persistent to arrested motion. These states are known to depend on the P\'eclet number of the molecular process powering the motion, which we can now tune by using a temperature sensitive mixture of surfactants as propulsion fuel. We quantify the droplet dynamics by analyzing flow and chemical fields for the individual states, comparing them to canonical models for autophoretic particles. In the context of these models, we experimentally observe, in situ, the fundamental first broken symmetry that translates an isotropic, immotile base state to self-propelled motility.

physics.flu-dyn↗

arXiv2022

Interfacial activity dynamics of confined active droplets

Active emulsions can spontaneously form self-propelled droplets or phoretic micropumps. It has been predicted that the interaction with their self-generated chemical fields can lead to multistable higher-order flows and chemodynamic phenomena. However, it remains unclear how such reaction-advection-diffusion instabilities can emerge from the interplay between chemical reactions and interfacial hydrodynamics. Here, we simultaneously measure the flow fields and the chemical concentration fields using dual-channel microscopy for oil droplets that dynamically solubilize in a supramicellar aqueous surfactant solution. We developed an experimentally tractable setup with micropumps, droplets that are pinned between the top and bottom surfaces of a microfluidic reservoir, which we compare directly to predictions from a Brinkman squirmer model to account for the confinement. With increasing droplet radius, we observe (i) a migration of vortex flows from the posterior to the anterior of the droplet, analogous to a transition from pusher- to puller-type swimmers, (ii) a bistability between dipolar and quadrupolar flow modes, and, eventually, (iii) a transition to multipolar modes. We also investigate how the dynamics evolve over long time periods. Together, our observations suggest that a local build-up of chemical products leads to a saturation of the surface, which controls the propulsion mechanism. These multistable dynamics can be explained by the competing time scales of slow micellar diffusion governing the chemical buildup and faster molecular diffusion powering the underlying transport mechanism. Our results are directly relevant to phoretic micropumps, but also shed light on the interfacial activity dynamics of self-propelled droplets and other active emulsion systems

cond-mat.soft↗

arXiv2021

Spontaneously rotating clusters of active droplets

We report on the emergence of spontaneously rotating clusters in active emulsions. Ensembles of self-propelling droplets sediment and then self-organise into planar, hexagonally ordered clusters which hover over the container bottom while spinning around the plane normal. This effect exists for symmetric and asymmetric arrangements of isotropic droplets and is therefore not caused by torques due to geometric asymmetries. We found, however, that individual droplets exhibit a helical swimming mode in a small window of intermediate activity in a force-free bulk medium. We show that by forming an ordered cluster, the droplets cooperatively suppress their chaotic dynamics and turn the transient instability into a steady rotational state. We analyse the collective rotational dynamics as a function of droplet activity and cluster size and further propose that the stable collective rotation in the cluster is caused by a cooperative coupling between the rotational modes of individual droplets in the cluster.

cond-mat.soft↗