ModelGauge™ is the proprietary technology used in our most versatile, highest-accuracy battery fuel gauge ICs. ModelGauge fuel gauges meet a wide range of design requirements, such as high accuracy, low cost and low power operation. Read below to learn how battery fuel gauges work and why ModelGauge technology provides the best results over a wide range of applications.
Battery Fuel Gauge Fundamentals
Battery fuel gauges provide an estimate of the charge remaining in a battery. They supply the user interface of a battery powered device with the information needed to display an indication of the battery status. Simple battery status indicators might consist of a 5 line LDC graph on a low cost device, to a more sophisticated visual indicator that also provides an estimate of operating time remaining and periodic battery alerts.
Early fuel gauges used coulomb counting technology to estimate the amount of charge used by the device. This would be subtracted from the battery total charge to provide an indication of remaining charge. Coulomb counting requires that the power path current be measured which requires a sense resistor in the output path. This sense resistor consumes a continuous amount of power while the device is in operation. This technique is still in use today, although it’s usually incorporated into proprietary algorithms to provide more accurate results.
Another way to monitor the state of charge of a battery is to measure the open circuit voltage of the battery. In most battery chemistries the open circuit voltage of the battery is related to the state of its charge. As a simple visualization, when a battery is fully charged its open circuit voltage is generally at its maximum. As the battery charge decreases the voltage decreases. The decrease is usually not linear and it can be dependent on temperature. To obtain an accurate state of charge using the open circuit voltage requires characterization of the battery. To obtain accurate results over time and temperature algorithms based on battery usage testing can be applied.
One advantage of using open circuit voltage to estimate the battery state of charge is that a current sense resistor is not required in the power path, reducing the power consumption.
Note that every type of battery fuel gauge requires some information about the battery it is monitoring in order to provide accurate results.
ModelGauge Algorithm Overview
ModelGauge battery fuel gauge algorithms are patented methods that offer the following advantages:
- Best State of Charge Accuracy
- Longest Battery Run-time
- Minimum Solution Size
- Robust Safety and Security
- Fastest Time to Market
ModelGauge incorporates multiple algorithms:
- ModelGauge m3
- ModelGauge m5
The original ModelGauge algorithm uses measured voltage to estimate the battery state of charge. This voltage based algorithm provided high accuracy in many conditions while offering multiple benefits such as low cost, low power usage, and minimal solution size.
The original ModelGauge algorithm has been enhanced to provide higher accuracy in more applications. ModelGauge m3 adds coulomb counting to the original voltage-based algorithm.
ModelGauge m5 offers improved accuracy over ModelGauge m3, and additional features including time-to-full (when charging), and Cycle+ age forecasting, which estimates when the battery is going to start deteriorating faster towards the end of its life cycle.
The original ModelGauge algorithm was developed to accurately estimate the battery state of charge without the need for a current sense resistor. Elimination of the current sense resistor, which is required on coulomb counting gauges, reduces power dissipation and solution size. Battery open circuit voltage (OCV) is reliably related to a battery’s state of charge (SOC). The patented ModelGauge algorithm estimates the battery open circuit voltage (OCV), even when the battery is under load, using battery characterization and real-time simulation. See Figure 1.
Figure 1. ModelGauge: Determining SOC from estimated OCV
The ModelGauge algorithm is more accurate than fuel gauges that measure current because it is based on estimated state-of-charge (SOC) and thus does not suffer from capacity drift over time. See Figures 2 and 3.
Figure 2. Battery capacity (mAh) drifts over time
Figure 3. State of charge (SOC) does not drift over time
ModelGauge m3 / ModelGauge m5
The ModelGauge m3/m5 algorithms incorporate coulomb counting into the original ModelGauge open circuit voltage (OCV) algorithm to provide increased accuracy. Coulomb counting by itself offers better short term accuracy than OCV, but over the longer term it injects offset drift that severely degrades accuracy. The ModelGauge m3/m5 algorithms use the OCV algorithm to periodically remove the offset error introduced by the coulomb counter.
Figure 4. State of Charge corrected 200,000 times a day using OCV
The combination of the two methods results in higher accuracy over a wide range of conditions and time, but at the expense of higher power usage, larger solution size, and higher cost. See Figures 4 and 5.
Figure 5. ModelGauge m3/m5 error over time vs. ModelGauge error and coulomb counter error.
The ModelGauge m5 algorithm is similar to the ModelGauge m3 algorithm with enhancements that provide higher accuracy in more conditions. It contains an age forecasting algorithm called Cycle+ to predict when the battery will start losing capacity due to age and use. It offers a converge-to-empty algorithm that eliminates the state of charge error that occurs at near empty. In addition it has multiple adaptive mechanisms to allow it to learn the capacity of the battery as the battery ages.
The additional outputs offered by the algorithm beyond state of charge (SOC) include time-to-full for recharging purposes, and time since first power up.
The algorithm is capable of logging 13 critical parameters over the lifetime of the battery. These parameters can be used for fault analysis should a concern arise about battery life or battery performance.
In addition, m5 provides an easy configuration interface. All fuel gauges need information about the battery they are monitoring in order to provide accurate results. The ModelGauge and ModelGauge m3 algorithms require data obtained from a factory-based battery characterization process. ModelGauge m5, however, allows field configuration using a software interface that allows the user to provide basic battery (pack) information. The software interface takes the user input and converts it to the register information needed by the m5 algorithm, saves the data, and loads the data into an m5 chip. This allows ModelGauge m5 EZ products to be programmed in the field by the customer without factory characterization. This configuration procedure provides very high accuracy without in-depth battery characterization. However, characterization services are still available for m5 products should the customer require them.
ModelGauge fuel gauge algorithms provide the industry’s most accurate battery state of charge estimates for a wide range of applications. With this introduction to ModelGauge technology you can now select the best product for your application. Please visit our Battery Fuel Gauge page to begin your selection process.