skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


  • NSF Public Access
  • Search Results
  • A Method for Charging Electric Vehicles with Battery-supercapacitor Hybrid Energy Storage Systems to Improve Voltage Quality and Battery Lifetime in Islanded Building-level DC Microgrids
Title: A Method for Charging Electric Vehicles with Battery-supercapacitor Hybrid Energy Storage Systems to Improve Voltage Quality and Battery Lifetime in Islanded Building-level DC Microgrids
This paper proposes a methodology to increase the lifetime of the central battery energy storage system (CBESS) in an islanded building-level DC microgrid (MG) and enhance the voltage quality of the system by employing the supercapacitor (SC) of electric vehicles (EVs) that utilize battery-SC hybrid energy storage systems. To this end, an adaptive filtration-based (FB) current-sharing strategy is proposed in the voltage feedback control loop of the MG that smooths the CBESS current to increase its lifetime by allocating a portion of the high-frequency current variations to the EV charger. The bandwidth of this filter is adjusted using a data-driven algorithm to guarantee that only the EV's SC absorbs the high-frequency current variations, thereby enabling the EV's battery energy storage system (BESS) to follow its standard constant current-constant voltage (CC-CV) charging profile. Therefore, the EV's SC can coordinate with the CBESS without impacting the charging profile of the EV's BESS. Also, a small-signal stability analysis is provided indicating that the proposed approach improves the marginal voltage stability of the DC MG leading to better transient response and higher voltage quality. Finally, the performance of the proposed EV charging is validated using MATLAB/Simulink and hardware-in-the-loop (HIL) testing.  more » « less
Award ID(s):
1757207 2214441
PAR ID:
10416887
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
IEEE Transactions on Sustainable Energy
ISSN:
1949-3029
Page Range / eLocation ID:
1 to 14
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; (, IET Generation, Transmission & Distribution)
    Abstract This paper proposes and develops the idea of using a community supercapacitor (SC) in an islanded DC multiple nano‐grids (MNG) system. In the proposed structure, the community SC works in tandem with the community/cloud battery energy storage system (CBESS) of the DC MNG. This combination forms a grid‐forming battery‐supercapacitor cloud hybrid energy storage system (CHESS), which is responsible for maintaining the voltage stability and power balance at the common DC bus of the MNG system. Also, to effectively utilize the SC capacity, this paper proposes a modified control structure for each DC nano‐grid enabling the local BESS units to coordinate with the community SC. Then, it is shown that, in the proposed grid‐forming CHESS technology, the output power of all the local and community BESS units has significantly smoother power variations leading to a higher battery lifetime. Additionally, it is shown that the proposed CHESS technology can improve the voltage stability of the system leading to higher voltage quality. Moreover, it is discussed analytically that the proposed CHESS technology requires less energy storage capacity for the community SC compared to its equivalent MNG with a distributed SC architecture. Finally, these results are verified by simulating two case‐study MNGs in MATLAB/Simulink. 
    more » « less
  2. ; ; ; ; ; (, 2022 IEEE Energy Conversion Congress and Exposition (ECCE))
    Power conversion is a significant cost in second-use battery energy storage systems (2-BESS). 2-BESS is a sustainable pathway for retired batteries of electrical vehicles (EV) to provide energy storage for the grid and EV fast charging. We present and demonstrate the optimization of Lite-Sparse Hierarchical Partial Power Processing (LS-HiPPP) for battery degradation over the potential lifetime of the 2-BESS. LS-HiPPP has a significantly better performance tradeoff with lower power processing than other partial and full power processing architectures. 
    more » « less
  3. ; ; ; ; ; ; ; (, Advanced Science)
    Abstract Developing fast‐charging, high‐temperature, and sustainable batteries is critical for the large‐scale deployment of energy storage devices in electric vehicles, grid‐scale electrical energy storage, and high temperature regions. Here, a transition metal‐free all‐organic rechargeable potassium battery (RPB) based on abundant and sustainable organic electrode materials (OEMs) and potassium resources for fast‐charging and high‐temperature applications is demonstrated. N‐doped graphene and a 2.8 m potassium hexafluorophosphate (KPF6) in diethylene glycol dimethyl ether (DEGDME) electrolyte are employed to mitigate the dissolution of OEMs, enhance the electrode conductivity, accommodate large volume change, and form stable solid electrolyte interphase in the all‐organic RPB. At room temperature, the RPB delivers a high specific capacity of 188.1 mAh g−1at 50 mA g−1and superior cycle life of 6000 and 50000 cycles at 1 and 5 A g−1, respectively, demonstrating an ultra‐stable and fast‐charging all‐organic battery. The impressive performance at room temperature is extended to high temperatures, where the high‐mass‐loading (6.5 mg cm−2) all‐organic RPB exhibits high‐rate capability up to 2 A g−1and a long lifetime of 500 cycles at 70–100 °C, demonstrating a superb fast‐charging and high‐temperature battery. The cell configuration demonstrated in this work shows great promise for practical applications of sustainable batteries at extreme conditions. 
    more » « less
  4. ; ; ; (, IEEE Open Journal of Power Electronics)
    Unfolding-based single-stage ac-dc converters offer benefits in terms of efficiency and power density due to the low-frequency operation of the Unfolder, resulting in negligible switching losses. However, the operation of the Unfolder results in time-varying dc voltages at the input of the subsequent dc-dc converter, complicating its soft-switching analysis. The complication is further enhanced due to the nonlinear nature of the output capacitance ( Coss ) of MOSFETs employed in the dc-dc converter. Furthermore, unlike two-stage topologies with a constant dc-link voltage, as seen in high-frequency grid-tied converters, grid voltage fluctuations also impact the dc input voltages of the dc-dc converter in unfolding-based systems. This work comprehensively analyzes the soft-switching phenomenon in the T-type primary bridge-based dc-dc converter used in unfolding-based topologies, considering all the aforementioned challenges. An energy-based methodology is proposed to determine the minimum zero-voltage switching (ZVS) current and ZVS time during various switching transitions of the T-type bridge. It is shown that the existing literature on the ZVS analysis of the T-type bridge-based resonant dc-dc converter, relying solely on capacitive energy considerations, substantially underestimates the required ZVS current values, with errors reaching up to 50%. The proposed analysis is verified through both simulation and hardware testing. The hardware testing is conducted on a 20-kW 3- ϕ unfolding-based ac-dc converter designed for high-power electric vehicle battery charging applications. The ZVS analysis is verified at various grid angles with the proposed analysis ensuring a complete ZVS operation of the ac-dc system throughout the grid cycle. 
    more » « less
  5. ; ; ; ; (, Society of Automotive Engineers International)
    Range anxiety and lack of adequate access to fast charging are proving to be important impediments to electric vehicle (EV) adoption. While many techniques to fast charging EV batteries (model-based & model-free) have been developed, they have focused on a single Lithium-ion cell. Extensions to battery packs are scarce, often considering simplified architectures (e.g., series-connected) for ease of modeling. Computational considerations have also restricted fast-charging simulations to small battery packs, e.g., four cells (for both series and parallel connected cells). Hence, in this paper, we pursue a model-free approach based on reinforcement learning (RL) to fast charge a large battery pack (comprising 444 cells). Each cell is characterized by an equivalent circuit model coupled with a second-order lumped thermal model to simulate the battery behavior. After training the underlying RL, the developed model will be straightforward to implement with low computational complexity. In detail, we utilize a Proximal Policy Optimization (PPO) deep RL as the training algorithm. The RL is trained in such a way that the capacity loss due to fast charging is minimized. The pack’s highest cell surface temperature is considered an RL state, along with the pack’s state of charge. Finally, in a detailed case study, the results are compared with the constant current-constant voltage (CC-CV) approach, and the outperformance of the RL-based approach is demonstrated. Our proposed PPO model charges the battery as fast as a CC-CV with a 5C constant stage while maintaining the temperature as low as a CC-CV with a 4C constant stage. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email