# Battery Management System Hardware Concepts: An Overview
## 2 Battery Management System Requirements
### Voltage Acquisition  
A classical battery management system for lithium based batteries needs to have at least one voltage acquisition channel per serially connected cell. Most automotive applications additionally have a secondary protection built in that does not reach the accuracy of the primary voltage acquisition: it usually has a programmable window comparator device that can alert the BMS-master in case a cell is operated outside the allowed voltage range. These over- and undervoltage ranges are usually preprogrammed via resistor arrays [8; 9].  
The data conversion rate that has to be considered for voltage acquisition varies widely depending on the application. Rates up to several Kilohertz are possible; however, they are rarely used because battery time constants are rather large in many use cases. High data acquisition rates are advisable, if oversampling is chosen to have a positive effect on the system behaviour. In addition, when large pulse currents occur in the application, faster monitoring can be necessary. Usually, commonly available BMS front-end chips have an absolute accuracy of 1 mV and a full range scale of 12 to 16 bits, which leads to a resolution of about 380 mV, (14 Bit resolution for BQ76 PL536A, [9]). This can be critical if estimation algorithms are used on battery chemistries that have a very small voltage response regarding SOC, like lithium-iron-phosphate based cells. Figure 1 shows two plots that each represent a different type of lithium chemistry: Lithium Nickel Manganese Cobalt Oxide (NMC)- and Lithium Iron Phosphate (LFP)-type. The graph shows the Cell Terminal Voltage (CTV) over each cell type's SOC. Any hysteresis effects are neglected for this example. The effect of a BMS's limited voltage accuracy and its impact on SOC accuracy is the focus: for both chemistries, the CTV value at the 50% SOC point is taken as an example for the acquisition voltage (the absolute voltage value is of no relevance here). Horizontal lines for the \(\pm\)1 mV inaccuracy are each added around the CTV(50% SOC) point. The intersections of the voltage inaccuracy lines with the actual CTV curve then result in vertical lines that represent the according SOC inaccuracy. As can be seen, if a \(\pm\)1 mV voltage accuracy system is used to calculate the SOC on NMC type lithium cells, then a base error of 0.2 % can be expected. If the same acquisition mimic is used to acquire an LFP cell's SOC, then a base error of 5.9 % has to be expected. This simple example shows the large impact of voltage acquisition accuracy on SOC estimation for various cells: the steeper the CTV-vs.-SOC curve is, the less impact a given voltage inaccuracy has. This leads to two conclusions:  
1. The better the voltage accuracy, the better the SOC estimation.
2. Using only voltage data to determine a cell's SOC might not be sufficient.  
A better accuracy can always be achieved at the expense of higher costs. As mentioned before, typical BMS Analog Frontends (AFEs) have voltage accuracies of about \(\pm\)1 mV. The details on voltage data acquisition are laid out in the paragraph _Voltage Measurement_ on page 14.