Title: Fast Charging of Li-ion Batteries via Learning and Optimization
Abstract: Complex electrochemical processes of Li-ion batteries result in nonlinear and high-dimensional dynamics. With the increased presence in critical applications, there is a demand for advanced fast-charging strategies to reduce the charging time while maximizing the battery’s lifespan. Fast charging is limited by several factors, such as elevated temperature, since they accelerate electrochemical aging and, in turn, result in increased lithium plating, higher mechanical stresses, and an increased growth rate of the solid-electrolyte interface layer. Here, we propose an aggressive but efficient charging strategy using an adaptive control strategy that learns the closed-loop system’s Jacobian from input/output data and optimizes the response based on the learned dynamics. To avoid subjecting the cell to accelerated aging, we optimize the electrical current for minimum battery charge time while respecting constraints such as maximum cell temperature and voltage. The battery data was generated using the Doyle-Fuller-Newman (P2D) model with a thermal model to characterize the cell’s thermal effects. Our optimized charging strategy is comprised of a hybrid (mixed continuous-discrete) solution that fully charges a 5Ah 21700 NMC-811 cylindrical cell, 66% faster than the recommended 0.3C constant-current constant-voltage strategy while respecting safety constraints, including a maximum voltage of 4.2V and a maximum temperature of 57oC.
Mohsen presented at the 4th Modeling, Estimation and Control Conference, October 27-30, 2024, Chicago
Title: SOC-Dependency of the Time Constants and Polarizations of Li-ion Batteries
Abstract: This paper presents an analysis of the time constants of Li-ion batteries. Our experiments were conducted on batteries with positive electrode material of NMC811. The time constants were determined using a distribution function of relaxation times (DRT) from the impedance spectra. Batteries’ internal processes can be characterized using their time constants and associated polarizations. Therefore, they allow for the evaluation of Li-ion batteries for safety and performance, as well as other energy storage technologies. In this study, we investigated the effect of temperature and State of Charge (SOC) on the time constants and polarizations of eight cylindrical cells. After the initial cycling of the cells, their EIS (Electrochemical Impedance Spectroscopy) data were collected at different temperatures from -20 to +60 ◦ C and SOCs ranging from 100% to 0%. The EIS data were processed to determine the time constants. We identified four dominant peaks in the medium to low-frequency range, assigned to contact resistance, Solid Electrolyte Interphase (SEI), charge transfer (CT), and diffusion. Additionally, two dominant peaks were observed in the high-frequency range. Next, we studied the SOC-dependency of the polarizations and representative time constants of the processes. The representative time constants were defined as the local maxima. The charge transfer kinetics (CT) and diffusion processes showed strong SOC dependencies. For example, the time constant of CT dropped from 1s at 0% SOC to 7ms at 50% SOC and increased to about 65ms at 100% SOC. During this cycle, its polarization changed from 27 mΩ to 0.3 mΩ and 4.9 mΩ, respectively. In contrast, the time constant and polarization of the high-frequency processes showed very small variations with SOC levels.
The Center for Battery Safety will make battery-powered devices safer through the research it conducts for stakeholder companies and agencies.
On Wednesday, Sept. 25, Temple University’s College of Engineering will celebrate the launch of its new Center for Battery Safety (CBS) with a kickoff meeting for companies and institutions interested in becoming stakeholders. Several notable companies and institutions have already registered to attend, including Ford, Stellantis, Tesla, Amazon, Apple, and the National Highway Safety Transportation Administration.
The new center will lead research on the safety of batteries to not only improve the products made by its stakeholders but also enhance safety for people everywhere relying on lithium-ion battery-powered vehicles and devices.
Renato presented at the Electrochemical Society, Pacific Rim Meeting (PRiME) 2024.
Title: Learning-Based Fast Charging of Li-Ion Batteries
Abstract:
This paper presents an aggressive charging strategy for Lithium-ion batteries (LiBs) while maximizing their life using an adaptive control strategy and a physics-inspired, data-driven model. With the increased presence of Lithium-ion batteries in critical applications, there is a demand for advanced fast-charging strategies to reduce the charging time while maximizing the battery’s lifespan. Several factors, such as elevated temperature, limit fast charging. Elevated temperature accelerates electrochemical aging, which in turn results in increased lithium plating, higher mechanical stresses, and an increased growth rate of the solid-electrolyte interface layer. The fast charging problem has been explored through passive and optimal charging strategies, such as constant-current constant-voltage (CCCV) and model predictive control (MPC). The optimal charging variants allow for formulating a closed-loop optimization problem to minimize charging time and can more naturally include physics-based constraints. However, the efficacy of real-time implementations is hampered by model inaccuracies stemming from streamlined battery dynamics that fail to exploit the system’s capabilities, potentially resulting in conservative or infeasible solutions. Furthermore, MPC strategies that employ electrochemical models for improved estimation of the battery states exhibit considerable computational complexity, limiting their application.
Here, we propose an efficient and fast charging strategy for Li-ion batteries via learning and optimization. This strategy substitutes the complex mechanistic model with a fast and control-oriented data-driven LiB model. Unlike mechanistic models, data-driven models offer accurate representations of system dynamics without relying on in-situ measurements and proprietary information. However, these models may perform poorly in unseen scenarios due to overfitting training data, which is typical. We developed a physics-inspired, data-driven approach to determine LiBs governing equations based on their electrochemistry rather than generic terms. We employ a sparse identification method achieved through sequential thresholding ridge regression to construct a nonlinear model from battery measurements, including electrical current (excitation input), voltage, and temperature. We formulate the problem to optimize the sparsification parameters as hyperparameters and minimize a cost function comprised of training and validation sets and the number of terms as a measure of complexity. Furthermore, we evaluate the identified model’s performance in unseen scenarios with the urban dynamometer driving schedule (UDDS) data, where a root mean square error of 1.26e-2 was achieved for SOC prediction, showcasing the model’s adaptability to new conditions.
The optimal charging strategy is developed using an adaptive control scheme that learns the closed-loop system’s Jacobian from input/output data and optimizes the response based on the learned dynamics. Here, we employ the data-driven LiB model to generate input/output data, leveraging its high accuracy and low complexity. The electrical current is optimized for minimum battery charge time while respecting constraints such as maximum cell temperature and voltage to avoid subjecting the cell to accelerated aging. Our optimized charging strategy is comprised of a hybrid (mixed continuous-discrete) solution that fully charges a 5Ah 21700 NMC-811 cylindrical cell, 66% faster than the recommended 0.3C constant-current constant-voltage strategy while respecting safety constraints, including a maximum voltage of 4.2V and a maximum temperature of 57 C corresponding to 90% of the maximum allowable surface temperature. We further tested our results against a fast-charging CCCV protocol with a maximum current of 2C rate. Under similar conditions, this strategy experienced upwards of 12% higher temperatures than our proposed charging strategy (similar charging time), reaching an excess of 64 C (compared to 57 C), which can result in significant damage to the battery’s health. In summary, we present an adaptive data-driven modeling and learning control strategy to keep the temperature within the manufacturer’s recommended range and achieve lower charging time compared to other strategies.
Title: The impact of lightweighting and battery technologies on the sustainability of electric vehicles: A comprehensive life cycle assessment
Abstract: We present a comprehensive analysis of the greenhouse gas (GHG) emissions of two battery electric vehicles (BEVs) using detailed teardown data and contrast them with those of four internal combustion engine vehicles (ICEVs). We used the teardown data to calculate the production and recycling phases as well as for the vehicle dynamics modeling and estimating the utilization phase GHGs. After validating the models and establishing a baseline, we analyzed the effect of new trends on their net carbon footprint. Specifically, we considered lightweighting, battery technology, and charging technologies and showed the tradeoff between longer-range BEVs and their sustainability as a green alternative to ICEVs. The GHGs were calculated based on a life cycle assessment, including the vehicles’ production, utilization, and disposal/recycling life. The GHGs of the production phase were calculated using detailed vehicle teardown data rather than general assumptions about the vehicles’ material composition. Similarly, the utilization phase GHGs were estimated by first creating accurate dynamic models of the vehicles and validating them against vehicle test data. Then, we analyzed the effect of charging type and electricity source on the sustainability of these technologies. These studies showed that the average (mixed) US electricity source accounts for about 50 % of GHGs, and changing charging from household to station or supercharging can save about 8 % of GHG emissions. Next, we studied the effect of battery technology and lightweighting on EVs’ net GHGs. OEMs have exploited both of these options to reduce the car’s weight and improve its electrical consumption during the utilization phase (driving). Our study showed that while the higher energy density of battery technologies like NMC and NCAs is attractive for reducing the vehicle’s weight and increasing its range, the use of rare materials significantly increases GHG emissions during production. Similarly, we showed that lightweighting by substituting steel with aluminum alloys (such as giga-casting) adds more production GHGs that significantly offset the savings in electrical consumption achieved during the vehicle’s lifetime. Therefore, this study proposes three pivotal considerations in the design and utilization of electric vehicles: battery material selection, trade-off analysis for vehicle lightweighting, and adoption of efficient charging methods and energy sources, all of which aim to reduce their overall global carbon footprint.
Ahmadzadeh, O., Rodriguez, R., Getz, J., Panneerselvam, S., & Soudbakhsh, D. (2025). The impact of lightweighting and battery technologies on the sustainability of electric vehicles: A comprehensive life cycle assessment. Environmental Impact Assessment Review, 110, 107668. https://doi.org/10.1016/j.eiar.2024.107668
This paper presents a voltage correlation method for real-time detection of the early onset of internal short circuits (ISCs) in battery modules. The lack of balancing circuitry can result in the over-discharge of a single bank (cells in parallel) in a module that may lead to a possible internal short circuit that can eventually result in high temperatures and thermal runaway We formulated a statistical framework to determine the threshold voltage correlation factor, below which it is possible to create an internal short inside a cell. Through experimental data investigation, we showed that the method is robust with respect to factors such as voltage measurement noise, variations in rest time during cycling, and short-duration current pulses. The threshold determined from a control module successfully captured the early onset of ISC due to over-discharge and the specific bank at which it occurred in a module. The low computational and hardware costs make it particularly attractive for implementation in battery management systems.
O. Ahmadzadeh, D. Tewari, J. Jeevarajan, and D. Soudbakhsh, “Real-Time Internal Short Circuit Detection in Li-ion Battery Modules During Field Use,” 2024 American Control Conference (ACC), Toronto, ON, Canada, 2023, pp. 3480-3485,
Renato‘s research on “Accurate State-of-Charge Prediction of Li-ion Batteries”, was awarded 1st place at Temple University’s Graduate Research Poster Competition, on February 21th, 2024.
Rodriguez, R., Ahmadzadeh, O., Wang, Y., & Soudbakhsh, D. “Data-Driven Discovery of Lithium-Ion Battery State of Charge Dynamics.” ASME. J. Dyn. Sys., Meas., Control. January 2024; 146(1): 011101. https://doi.org/10.1115/1.4064026.
Rodriguez, R., Ahmadzadeh, O., Wang, Y., & Soudbakhsh, D. (2023, May). Discovering governing equations of li-ion batteries pertaining state of charge using input-output data. In 2023 American Control Conference (ACC) (pp. 3081-3086), IEEE, https://doi.org/10.23919/ACC55779.2023.10156114.
Ahmadzadeh, O., Rodriguez, R., Wang, Y., & Soudbakhsh, D. (2023, May). A physics-inspired machine learning nonlinear model of li-ion batteries. In 2023 American Control Conference (ACC) (pp. 3087-3092). IEEE, https://doi.org/10.23919/ACC55779.2023.10156368.
Title: A data-driven framework for learning governing equations of Li-ion batteries and co-estimating voltage and state-of-charge
Abstract: This paper presents a reduced-order nonlinear model for Lithium-ion batteries (LiBs). Unlike mechanistic models, data-driven models offer accurate representations of system dynamics without relying on in-situ measurements and proprietary information. However, these models may perform poorly in unseen scenarios due to overfitting training data, which is typical. We propose a physics-inspired, data-driven approach to determine LiBs governing equations based on their electrochemistry rather than generic terms. We employ a sparse identification method achieved through sequentially thresholding ridge regression to construct a nonlinear model from electrical current (excitation input) and measured voltage. We formulate the problem to optimize the sparsification parameters as hyperparameters and minimize a cost function comprised of training and validation sets and the number of terms as a measure of complexity. We augment the model with a joint unscented Kalman filter to handle noisy experimental data, enabling a more accurate estimate of the state of charge (SOC) and voltage. Model performance in unseen scenarios is evaluated with urban dynamometer driving schedule (UDDS) data, where the identified model achieves a root mean square error of 1.26e−2 for SOC and voltage prediction.
Ahmadzadeh, O., Wang, Y., & Soudbakhsh, D. (2024). A data-driven framework for learning governing equations of Li-ion batteries and co-estimating voltage and state-of-charge. Journal of Energy Storage, https://doi.org/10.1016/j.est.2024.110743
