With consumers demanding accurate and longest possible battery run-time for wearables, the author talk about managing battery capacity and Maxim's ModelGauge m5 EZ algorithm.
Editor's Note: Last month, Maxim introduced the MAX17055 fuel gauge that the company claims draws the industry's lowest quiescent current (7µA) in the low power operating mode. In this article, the authors talk about Maxim's ModelGauge m5 EZ algorithm, which is implemented in this part.
Optimal battery performance relies on a high-quality battery model that drives the fuel gauging algorithm. Taking the time to do this customised characterisation work yields more accurate battery performance, minimizing state of charge (SOC) errors and correctly predicting when the battery is nearly empty.
The energy stored in the battery (capacity in mAh) is dependent upon several parameters such as load and temperature. As a result, developers must characterize the battery under a variety of conditions. Once a model tuned to the battery behavior has been extracted, it is loaded into the fuel gauge chip. This closely supervised process results in safer battery charging and discharging.
Fuel gauge characterisation presents both a time-to-market issue and a barrier to growth for manufacturers, due to the difficulty in serving any but the highest volume customers. IC vendors have traditionally focused on high-volume applications since extensive lab work is often required for model extraction and only a few IC manufacturers have the required resources.
Battery run-time challenge
One important consequence of a poorly modeled battery is an inaccurate run-time estimate. A typical smart watch usage model includes 5 hours in an active state (including activities such as time checks, notifications, app use, music playback, talk, and workout) and 19 hours in a passive state (time check only) over the course of a single day. If the device consumes 40mA in active mode and 4mA in passive mode over the course of a day, it will consume a total of 276mAh, which is just about the capacity of a typical smart watch battery. Accurate prediction of the battery run-time is necessary to avoid unexpected or premature interruptions of the device operation.
The run-time duration is equally important. In passive mode, the same battery can sustain up to 69 hours of operation (276mAh/4mA). A typical fuel gauge that consumes 50µA will shorten the battery passive run-time by about 52 minutes, which is not a negligible amount of time.
The EZ algorithm
Maxim Integrated has developed an algorithm to accurately estimate the battery state of charge and safely handle most batteries. The algorithm was developed after studying the characteristics of common lithium batteries.
The ModelGauge m5 EZ algorithm (EZ, for short) uses a battery model tuned to a specific application and is embedded into the fuel gauge IC. Designers can generate battery models using a simple configuration wizard included in the evaluation kit software. The system designer needs to only provide three pieces of information:
- Capacity (often found on the label or data sheet of the battery)
- Voltage per cell, considered the empty point for the battery (application dependent)
- Battery charge voltage (if it is above 4.275V)
With EZ, the system designer no longer needs to perform characterisation work, as it has essentially been done by the fuel gauge vendor.
Several adaptive mechanisms included in the EZ algorithm increase the fuel gauge accuracy even more by helping it learn about the battery characteristics. One such mechanism guarantees that the fuel gauge output converges to 0% as the cell voltage approaches empty. Thus, the fuel gauge reports 0% SOC at the exact time that the cell voltage reaches empty.
If we assume a system error budget of 3% in the SOC prediction, the EZ model passes 95.5% of the entire discharge test cases—very close to the performance of labor-intensive custom models that pass 97.7% of test cases. As shown in Figure 1, the EZ mechanism performs at about the same level of accuracy when the battery is near empty, which is where it matters most.
Figure 1: The EZ System error performance.
For many users, simply knowing the SOC or the remaining capacity is not enough. What they really want to know is how much run-time is left from the residual charge. Simplistic methods, such as dividing the remaining capacity by the present or future load, can lead to overly optimistic estimates. The EZ algorithm can provide a much more accurate time-to-empty prediction based on battery parameters, temperature, load effects, and the empty voltage of the application.
High-volume manufacturers can use EZ as a starting point for quick development. Once they have a working prototype, a finely tuned battery model can be selected. The small-volume manufacturer can use EZ to model the best available battery with the confidence that most batteries will be compatible.