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Kim, Youngki

Publications and source records attributed to Kim, Youngki.

7 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

State of battery health estimation based on swelling characteristics

There is disclosed an electrical device including a battery, and a battery management system. The battery management system includes a controller in electrical communication with a pressure sensor to monitor the state of health of the battery. The controller applies a method for determining the state of health that uses a non-electrical (mechanical) signal of force measurements combined with incremental capacity analysis to estimate the capacity fading and other health indicators of the battery with better precision than existing methods. The pressure sensor may provide the force measurement signal to the controller, which may determine which incremental capacity curve based on force to use for the particular battery. The controller then executes a program utilizing the data from the pressure sensor and the stored incremental capacity curves based on force to estimate the capacity fading and signal a user with the state of health percentage.

Stefanopoulou, Anna G.↗
DOE OSTI2021

Co-Optimization of Velocity and Charge-Depletion for Plug-In Hybrid Electric Vehicles: Accounting for Acceleration and Jerk Constraints

Abstract Recent advances in vehicle connectivity and automation technologies promote advanced control algorithms that co-optimize the longitudinal dynamics and powertrain operation of hybrid electric vehicles. Typically, a sequential optimization with the vehicle dynamics optimized followed by powertrain optimization is adopted to manage a number of complexities such as the inherent mixed-integer nature of the hybrid powertrain, the numerous state and control variables, the differing time scales of vehicle and powertrain subsystems, time-varying state constraints, and large horizon lengths. Instead, we solve the offline optimization problem in a centralize manner assuming exact knowledge of the lead vehicle's position over the entire trip by applying a discrete-time single shooting-based numerical approach, Discrete Mixed-Integer Shooting (DMIS), including a linearly increasing computational complexity to the problem horizon. In particular, the hierarchical problem structure is exploited to decompose the computationally intensive Hamiltonian minimization step into a set of low-dimensional optimizations. DMIS allows us to compute the direct fuel minimization problem including the vehicle and powertrain dynamics in a centralized manner to its full horizon while systematically tuning weighting factors that penalize passenger discomfort. For the first time, this study reveals that practically implemented sequential optimization exhibits similar fuel optimality as co-optimization when a certain level of passenger comfort is required.

Automation & Control Systems↗
DOE OSTI2021

Data-Driven Forgetting and Discount Factors for Vehicle Speed Forecasting in Ecological Adaptive Cruise Control

Abstract This paper investigates temporal correlations in human driving behavior using real-world driving to improve speed forecasting accuracy. These correlations can point to a measurement weighting function with two parameters: a forgetting factor for past speed measurements that the vehicle itself drove with, and a discount factor for the speeds of vehicles ahead based on information from vehicle-to-vehicle communication. The developed weighting approach is applied to a vehicle speed predictor using polynomial regression, a prediction method well-known in the literature. The performance of the developed approach is then assessed in both real-world and simulated traffic scenarios for accuracy and robustness. The new weighting method is applied to an ecological adaptive cruise control system, and its influence is analyzed on the prediction accuracy and the performance of the ecological adaptive cruise control in an electric vehicle powertrain model. The results show that the new prediction method improves energy saving from the eco-driving by up to 4.7% compared to a baseline least-square-based polynomial regression. This is a 10% improvement over the constant speed/acceleration model, a conventional speed predictor.

Automation & Control Systems↗
DOE OSTI2018

Bulk force in a battery pack and its application to state of charge estimation

There is disclosed an electrical device including a battery pack, a pressure sensor for measuring a volume change of the battery pack, a voltage sensor in electrical communication with a positive terminal and a negative terminal of the battery pack, a temperature sensor positioned in a cell of the battery pack, and a battery management system. The battery management system includes a controller in electrical communication with the pressure sensor, the voltage sensor, and the temperature sensor, the controller being configured to execute a program stored in the controller to determine a state of charge percentage of the battery pack based on a pressure reading from the pressure sensor, a terminal voltage reading from the voltage sensor, and a temperature reading from the temperature sensor.

Stefanopoulou, Anna G.↗
DOE OSTI2016

Battery Capacity Fading Estimation Using a Force-Based Incremental Capacity Analysis

Traditionally health monitoring techniques in lithium-ion batteries rely on voltage and current measurements. A novel method of using a mechanical rather than electrical signal in the incremental capacity analysis (ICA) method is introduced in this paper. This method derives the incremental capacity curves based onmeasured force (ICF) instead of voltage (ICV). The force ismeasured on the surface of a cell under compression in a fixture that replicates a battery pack assembly and preloading. The analysis is performed on data collected from cycling encased prismatic Lithium-ion Nickel-Manganese-Cobalt Oxide (NMC) cells. For the NMC chemistry, the ICF method can complement or replace the ICV method for the following reasons. The identified ICV peaks are centered around 40% of state of charge (SOC) while the peaks of the ICF method are centered around 70% of SOC indicating that the ICF can be used more often because it is more likely that an electric vehicle (EV) or a plug-in hybrid electric vehicle (PHEV) will traverse the 70% SOC range than the 40% SOC. In addition the Signal to Noise ratio (SNR) of the force signal is four times larger than the voltage signal using laboratory grade sensors. The proposed ICF method is shown to achieve 0.42% accuracy in capacity estimation during a low C-rate constant current discharge. Future work will investigate the application of the capacity estimation technique under charging and operation under high C-rates by addressing the transient behavior of force so that an online methodology for capacity estimation is developed.

25 ENERGY STORAGE↗