Battery Informatics, Inc. (Bii) is developing the next generation Battery Management System (BMS) by using a model-based and application-aware approach. Bii’s BMS can interface to your energy storage products with the result that the levelized cost of storage (LCOS) can be in the range of 4 to 8 cents per kWh. This low cost will cause disruptive changes in the market of energy storage.
Our Core Technologies
Bii's BMS uses a variety of models to compute the internal state of the batteries; e.g., state of charge and cost of battery degradation.
Empirical models, partially validated by our own battery testing, are used to optimize the trade-off between cost of battery degradation versus the value from provided services.
Bii is developing automated testing procedures to experimentally determine battery health.
You can only measure voltage, current, and external temperature. Information like internal temperature. SoC, SoE, and SoH need to be estimated. Bii is developing methods to get the best possible estimates of these values. The batteries are modeled by empirical formulas supported by Bii’s battery testing that continually is producing more results that we are integrating into the BMS.
Cost versus Benefits
Bii represents degradation as function of depth of discharge, charging rate and temperature. The degradation is computed in terms of marginal cost that Bii expresses by Levelized Cost of Storage (LCOS). The BMS makes a comparison between the benefits of the service performed by the battery versus the cost of delivering that service. These features get their information from the BMS which provides it to higher-level functions that perform energy management of buildings and grid optimization. Using the same definitions of LCOS and very similar use cases as Lazard, we can demonstrate a levelized cost of storage of 4 to 8 cents per kWh for 10 years lifetime, 5% discount rate, and 92% battery round-trip efficiency.
Second Use Batteries
Bii’s first product was deployment of a 9 kWh storage solution using Li-phosphate (LFP) batteries previously used in hybrid buses. This deployment gets its source of electricity from a combination of solar panels and a generator. The energy is used for telecommunications on a remote island where is no connectivity to the electric grid. Bii is preparing for future projects of second-use batteries for solar applications. Bii has developed its own chips to perform balancing of the cells in the battery pack which is particularly important for second-use batteries.
Research and Talents
Bii has been funded by grants from State of Washington, University of Washington, and two contracts from Department of Energy. We have a close relationship with the University of Washington where we have access to technology, research, and talents. Our solar project was funded by a local investor. A strategic partner is paying us to use our technology for their portable storage product. We are working with some of the most progressive grid and energy storage companies in the NW region of the US to enhance their products.
If you use the capabilities provided by Bii's BMS, you will have the most economic and safe operation of Li-ion batteries that state-of-the-art battery control can provide. Feel free to contact us regarding how we can add value to your project.
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.
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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