Design a cell-monitoring system to optimize accuracy, lower costs, or both

December 02, 2013 //By Jeremy Georges
Design a cell-monitoring system to optimize accuracy, lower costs, or both
Jeremy Georges, MTS, Maxim Integrated discusses cell-measurement architectures for cell balancing and battery-measurement applications and presents example designs that meet diverse accuracy and cost requirements.


You are designing a battery-operated system and must juggle the cost, space, and accuracy trade-offs. “Accuracy” for us here is not the accuracy of the end application. Rather we are focusing on the accuracy of the cell-measurement system - if this fails, so might the application. Systems operating with a lithium phosphate cell require highly accurate monitoring because of the nearly-flat discharge curve of that battery chemistry. Other common chemistries do not have as much flatness on their discharge curves, so the accuracy of measurements can be less precise, less accurate.
The accuracy required for a battery-monitoring system is tightly linked to the battery chemistry and the specific application.

Optimizing Cell Monitoring: the Process of Accuracy

No one will argue that we need to optimize the accuracy of cell monitoring in battery-powered systems. The task is most easily accomplished when broken into simple steps focused on meeting specific application requirements. The design process
can be divided into three stages: first, select the cell architecture for an application; second, determine the critical performance parameters; and third, select the system components.
The system architecture is the primary factor that will drive any effort to optimize cell-monitoring accuracy. As we examine architectures, we will also address the interrelated stages two and three, the critical performance parameters and optimal
system components, respectively. The selection of suitable components is wide and each architecture can be used in a range of systems with very different needs. Consequently, the discussion of system components will include examples.

Architectural Analysis

Table 1 shows the relative cost, maximum expected six-sigma error, and maximum expected three-sigma error of the four cell architectures considered in this analysis. The six-sigma error denotes the maximum error statistically expected on 99.99966%
of all systems built using the respective architecture. The three-sigma error denotes the maximum error statistically expected on 99.73% of all boards built using the same architecture.

Table 1. Cost/Performance
Design category: 

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