Fuel gauging for all

Fuel gauging for all

Technology News |
By Julien Happich

Some of these inventions are taking the form of connected devices, taking advantage of feeding data into easy-to-access databases over the internet, with the possibility of analyzing trends using big data analytics. Many of these devices also have the convenience of being untethered from the wall by running on batteries, or having a battery backup in case of power failure.

Compared to the traditional way that organizations have developed products – in big teams with dedicated specialized engineers for every development task – the emphasis is increasingly on keeping the teams small and agile in order to bring these devices to market as quickly as possible and seeing how they are received in order to make further investment decisions into that product area. There are also many start-ups with creative engineers whose core competency is not necessarily electronic circuit design, but more along the lines of application development software or industrial design. These engineers sometimes view electronics design as something that they need to bring their ideas to fruition, with the software as the key element that distinguishes them from their competition. And then there is a growing maker movement, in which hobbyists are inventing things for sheer pleasure or to pursue their passion for a particular personal cause.

The intricacies of battery management might be very far from these sets of creative minds. They just need something that works well out of the box and is really easy to implement to go to production. Traditional methods of fuel gauging involve a power or battery specialist on the team to work with the fuel gauge vendor in order to find a suitable model that can be used with their battery. This often involves characterizing the battery under various load and temperature conditions, if the specialized battery test equipment including temperature chambers is available, or shipping the batteries to the fuel gauge vendor for characterization in their lab.

This can involve real as well as intangible costs. Simply the logistics of shipping the lithium ion batteries have come under increased scrutiny for air transport due to safety issues, as well as the time involved for shipments. Once the batteries are with the vendor, it can take a couple of weeks to fully characterize and model the batteries under the various load and temperature conditions of interest. Only then can the system designer plug the custom battery model into the fuel gauge to start running their evaluation and finalize the design.

Maxim Integrated has just introduced an innovative way of solving these problems by combining the latest advances in ultra-low power, mixed-signal IC technology with its fuel gauge algorithm called ModelGauge™ m5 EZ. This algorithm is built into the MAX1720x/MAX1721x ultra-low power stand-alone fuel gauge ICs. The MAX17201/MAX17211 monitor a single cell pack. The MAX17205/MAX17215 monitor and balance a 2S or 3S pack, or monitor a multiple-series cell pack. A Maxim 1-Wire® (MAX17211/MAX17215) or 2-wire I2C (MAX17201/MAX17205) interface provides access to data and control registers.

These fuel gauge ICs allow the system designer to simply walk through an easy-to-use Configuration Wizard in the evaluation kit software and generate a battery model suitable for their application without any complications related to custom battery characterization. The system designer needs to provide only three pieces of information:

1) what is the design capacity of the battery (often found on the label or data sheet of the battery);

2) what voltage per cell is to be considered as the empty point for the battery (depends on the application constraints); and

3) whether the battery charge voltage is above 4.275V (per cell in case of multiple series cells).


In addition to the battery model, the Configuration Wizard also walks the system designer through the various hardware configuration features such as:

  • Battery pack schematics (relevant for multiple series cells)
  • Number of series cells
  • Shutdown mode (relevant if the battery is detached from the system)
  • Sense resistor selection
  • Temperature measurement – IC internal or using external thermistors
  • Alerts based on various criteria like voltage, current, temperature, or battery state-of-charge (SOC %), over-current detection, alert polarity
  • Battery life logging
  • General purpose non-volatile memory usage

This eliminates the complicated and error-prone task of arranging various configuration bits manually by hand in order to prepare the registers for programming into the IC.

So how well does this work in practice? Being very mindful of Edison’s observation in 1883: “Just as soon as a man gets working on the secondary battery it brings out his latent capacity for lying,it is important to be very thorough and clear about the performance of this new technology, so as not to overstate the facts.

Maxim has developed a vast battery database consisting of cell characteristics and behavior over a variety of test conditions similar to the customers’ use cases. This allows Maxim to validate any new improvements in the fuel gauge algorithm, by running it on the real data collected previously. Using this data, Maxim analyzed the performance over hundreds of batteries of various sizes and plotted a histogram of the results in figure 3.

Figure 3

This shows that more than 94% of test cases at room temperature and above have less than 3% SOC error. These test cases do exclude certain battery types that are known to be quite different in terms of open-circuit voltage (OCV) vs SOC% table, compared to the more conventional and popular chemistries.

While these results look very good, how much are we giving up in terms of performance if we used a custom-tuned battery model in each case?

Figure 4 is a histogram that shows a comparison of the EZ model vs a “tuned” custom model, plotted as percentile of test cases vs the error bucket they fall into. While the tuned model indeed places a higher number of cases in the 1% bucket, the aggregate of all test cases up to 3% error shows that the EZ model covers 95% while the custom mode covers 97% of the test cases. Considering the extra effort, resources, and time required to prepare a custom model, the EZ model starts looking very attractive indeed.

Figure 4:

Another way to look at this is to compare the EZ vs custom tuned model at specific error budgets allowed in the system design.

Figure 5 shows how they compare with <3% error budget and <5% error budget.

Instead of simply looking at the worst case error everywhere between 0% and 100% SOC, look at the error near empty (e.g. 10%), where accurate fuel gauging really matters. If the battery is around 50% state, and the fuel gauge is indicating 40% or 60% (10% error), nothing bad is likely to happen, as no critical power management decisions are taken at that point. However, when the battery is at 10% and the fuel gauge indicates 5% SOC, then most likely, the system is going to shut down prematurely, and the battery will not be utilized fully.

On the other hand, if the battery is at 5% and the fuel gauge indicates 10% SOC, then it is likely that the system will crash unexpectedly without the benefit of a graceful planned shutdown. Both result in poor user experience – the former results in a shorter run-time than expected, while the later results in an abrupt shutdown that is very annoying for the user.

Figure 5


If the application has more demanding requirements, and also needs good accuracy at cold temperatures (0 degrees Celsius), then a similar analysis shows that the results are nearly the same for SOC error budget of <5%. Thus, for a large category of applications, the simplicity of implementing the EZ configuration performance becomes a game-changer for new product development.

So what allows ModelGauge m5 EZ configuration to deliver such good results?

The magic lies in how the patented ModelGauge m5 algorithm uses the real time electrical measurements and converts these into usable SOC% and other battery information. The algorithm has multiple mechanisms of desensitizing the errors due to model mismatch with the actual cells in use. These mechanisms also desensitize any errors in the electrical measurements from having adverse effects on the SOC % output. In addition, there are several adaptive mechanisms that helps the fuel gauge learn about the battery characteristics and improve its accuracy.

The ModelGauge m5 algorithm combines the short-term accuracy and linearity of a coulomb counter with the long-term stability of a voltage-based fuel gauge. The core of the algorithm combines the OCV state estimation with the coulomb counter. The OCV value of Li+ cell correlates to the SOC% and this relationship persists largely independent of the age of the cell (see figure 6).

Figure 6: SOC% vs OCV of a battery does not change with age.

As the cell cycles during the application, this process of traversing up and down through this curve largely desensitizes any local errors resulting from any model to cell mismatch. At the start, when the cell is first connected to the fuel gauge IC, the OCV state estimation is weighted heavily compared to the coulomb count output. As the cell is cycled in the application, coulomb counter accuracy improves and the mixing algorithm alters the weighting so that the coulomb counter result is dominant. From this point forward, the IC switches to servo mixing.

Servo mixing provides a fixed magnitude continuous error correction to the coulomb count, up or down, based on the direction of error from the OCV estimation. This allows differences between the coulomb count and OCV estimation to be corrected quickly. The resulting output from the mixing algorithm does not suffer accumulation drift from current measurement offset error and is more stable than a stand-alone OCV estimation algorithm (see figure 7).

Figure 7

This correction to the coulomb counter takes place continuously while the application is active as well as when it is in standby condition. In practical terms, this means that the coulomb counter corrections happen more than 200,000 times per day – in tiny steps that are almost invisible to the user. These corrections happen when the battery is under load, as well as at no-load condition, regardless of whether the cell is relaxed or not, and this is a significant advantage over other competing algorithms.

As the temperature and discharge rate of an application changes, the amount of charge available to the application also changes. The ModelGauge m5 algorithm distinguishes between remaining capacity of the cell and remaining capacity of the application, and reports both results to the user.

The algorithm periodically makes internal adjustments to cell model and application information to remove initial error and maintain accuracy as the cell ages. These adjustments always occur as small corrections to prevent instability of the system and prevent any noticeable jumps in the fuel gauge outputs. Learning occurs automatically without any input from the host. In addition to estimating the battery’s state of charge, the ICs observe the battery’s relaxation response and adjusts the dynamics of the voltage fuel gauge.

The ModelGauge m5 algorithm includes a feature that guarantees the fuel gauge output converges to 0% as the cell voltage approaches the empty voltage. As the cell voltage approaches the expected empty voltage, the IC smoothly adjusts the rate of change of SOC % so that the fuel gauge reports 0% at the exact time that the cell voltage reaches empty. This prevents unexpected shutdown or an early 0% SOC reported by the fuel gauge. This also provides an additional mechanism of desensitizing the SOC % error from any model mismatch errors.

The ICs automatically compensate for cell aging, temperature, and discharge rate, and provide accurate state of charge (SOC) in milliampere-hours (mAh) or percentage (%) over a wide range of operating conditions. The ICs provide accurate estimation of time-to-empty and time-to-full, Cycle+™ age forecast, and three methods for reporting the age of the battery: reduction in capacity, increase in battery resistance, and cycle odometer.

The ICs provides precision measurements of current, voltage, and temperature. Temperature of the battery pack is measured using an internal temperature measurement and up to two external thermistors supported by ratiometric measurements on auxiliary inputs. The ICs can provide alerts by detecting a high or low voltage, current, temperature, or state of charge. The ICs also contain two programmable, fast overcurrent comparators that allow spikes in system current to be detected and warn the system to make appropriate adjustments to prevent such conditions that could cause the battery to crash abruptly. Both comparators have programmable threshold levels and programmable debounced delays.

To prevent battery pack cloning, the ICs are the only stand-alone fuel gauges that integrate SHA-256 authentication with a 160-bit secret key. Each IC incorporates a unique 64-bit ID.

The ICs are available in a manufacturing handling friendly, lead-free, 3mm x 3mm, 14-pin TDFN package.

In summary, the latest advances in fuel gauge technology based on decades of battery management and design experience, are bringing world-class fuel gauge accuracy to all customers, from large manufacturers to start-ups and makers. ModelGauge m5 EZ products allow designers to focus on what they’re good at and not have to worry about gas gauge implementation.


About the author:

Bakul Damle is a Business Director at Maxim Integrated, and is responsible for the company’s battery management product line –

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