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Battery Informatics, Inc. (Bii) is developing the next generation Battery Management System (BMS) based on using a model-based and application-aware approach. Our solutions optimize energy storage batteries to increase value and lifetime by using physics-based dynamic models of the internal battery behavior. The BMS is also tailored to the specific needs of the applications. EV applications have many unique requirements that are different from grid and EV applications.

Our Core Technologies

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Real-Time Models

Bii’s BMS uses physical models to compute the internal state of the batteries; e.g., state of charge, internal temperature, and degree of degradation.

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Battery Degradation

The model is used to optimize the trade-off between cost of battery degradation versus the value from provided services.

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Diagnostics

Even state-of-the-art ten years from now will not accurately predict end of life for Li-ion batteries. Thus, Bii will provide automated testing to determine battery health for comparison with model-based results.

Our Features

Model-Based

The Li-ion batteries are modeled by solving a P2D (pseudo-two-dimensional) set of equations. The model estimates the state of charge, capacity fade, side reactions, electrode utilization efficiency and temperature distribution. UW has developed a method of model reduction, simplification and reformulation. While the model reduction and simplification ignores some of the physics, it significantly reduces the computational cost.

Cost versus Benefits

Battery degradation is computed in terms of marginal change in Levelized Cost of Storage (LCOS). As result, you can easily make a monetary cost comparison between the benefits of the service performed by the battery and the cost of delivering that service. The degradation cost can be obtained from specific aging tests or from our model.

State Estimation

You normally only measure voltage, current, and external temperature. With those inputs, the model can estimate what is happening inside the batteries. An Extended Kalman Filter is used to make adjustments between measured versus predicted voltage, current, and temperature to obtain an optimal state estimate; particularly SoC.

Optimized Charging

For those applications (e.g., e-bus) where you have a non-standard charger, our BMS will automatically determine a path that results in much less degradation than standard CC-CV (traditional method), step-wise CC-CC (common Super Fast charging), and other charging protocols.

Faster than Real-Time

To be useful for state estimation and control, the model must run faster than the data sampling interval. For use in EV drivetrains, the sampling interval is sub-seconds. For grid applications the time interval is typically 15 minutes.

Self-Learning

P2D model needs up to 28 parameters to adequately characterize the particular battery chemistry. Some of these parameters change during the life-time of the batteries. We are developing a self-learning capability to automatically estimate these parameters.

Runs on Small Micro-Processors

If you only need to monitor the average cell, you can run all the necessary calculations on a small processor. That will only add a few dollars to the overall cost of a BMS solution. If you want to monitor many cells, you presumably do it because your application warrants the cost of advanced control.

Superior Solution

If you use all the capabilities provided by Bii’s BMS, you will be have the most economic and safe operation of Li-ion batteries that state-of-the-art battery control can provide. The solutions run on commonly available chips like TI and Xilinx.

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