Integrating Feedback Control Algorithms with the Lithium-Ion Battery Model to Improve the Robustness of Real Time Power Limit Estimation 2017-01-1206

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 and state of health estimation errors. This causes a distribution of power capability among battery packs that are built to the same design specification. We demonstrated that real time power limit estimation can only partially address the system variability due to the errors introduced by itself.

Integrating feedback control algorithms with the lithium-ion battery model maximizes the battery power capability, improves the battery robustness to variabilities, and reduces the real time estimation errors. In this work, we compare true system power capability and variability with the real time estimations. Then, we demonstrate the robustness of real time power limit estimation with feedback control algorithms incorporated with real time power limit estimation.