Search Results

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.

Validation of the State-Of-Function (SOF) algorithm and associated cell models are critical for battery management as they are responsible for optimal pack power utilization as well as safety protection and life. The SOF accomplishes this optimization task by communicating pack level operation limits related to power, current, voltage and temperature. These operation limits are, in some cases, estimated via parameters and equations derived from cell models. Correspondingly, any errors within the cell models will propagate into the model-dependent SOF limits. Understanding the source of errors and thus finding areas for improvement requires a visualization-based SOF validation strategy.