Integrating feedback control algorithms with the lithium-ion battery models to improve the robustness of real time power limit estimation

Paper #:
  • 2017-01-1206

Published:
  • 2017-03-28
Abstract:
Power limit estimation of a lithium-ion battery system plays an important balancing role of optimizing the battery design cost, maximizing for power and energy and protecting the battery from abusive usage to achieve the intended life. The power capability estimation of any given lithium-ion battery system is impacted by the variability of many sources, such as cell and system components resistance, temperature, cell capacity and real time state of charge (SOC) and state of health (SOH) estimation errors. The combination of worst case scenarios approach causes overly conservative power limit estimation for majority of lithium ion battery packs. By integrating feedback control algorithms with the lithium-ion battery models, we can maximize the use of true power capability of the battery system and also improve the system robustness to potential variability and estimation errors. In this work, we compared the power limit estimation difference based on conservative approaches versus true system power capability for offline and real time estimations. Then, we demonstrate the robustness of real time power limit estimation of the feedback control algorithms in combination of lithium-ion battery models at the operational boundary, such as voltage and temperature operating limits.
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