Advanced batteries diagnostics resemble face recognition in which a camera takes a multi-dimensional image to identify a person’s feature with artificial intelligence (AI).
Similarly, a complex measuring device analyzes the electrochemical evidence of a battery to assess SoH with AI, of which capacity is the leading health indicator.
Modern battery analytics observe subtle changes in battery performance by tracking capacity fade to estimate the Remaining Useful Life (RUL). Cloud analytic systems are in development that will usher in “social networking in battery care,” one of which is RUBY. RUBY stands for Remaining Useful Battery Yield.
Batteries are commonly installed and forgotten. With RUBY, the fleet supervisor keeps inventory by tracking the RUL of each battery. Figure 2 demonstrates a system that observes the battery with a complex measuring device and displays the test results on a monitor, assisted by cloud analytics. The RUBY system will serve portable batteries and stationary installations.
Energy Allocation
When new, a battery delivers a capacity of 100%. On a 10Ah pack, this relates to a 10A discharge lasting one hour. Usage and age reduce the battery capacity and a typical end-of-life setting of 80%.
Most battery applications allow a 20% capacity loss before a replacement is needed. Device manufacturers also include a safety margin in Reserve Charge that is set to 20% in Figure 3. These provisions reduce the Energy Allocation of a battery from 100% to 60% in worst case scenarios.
Modern diagnostic chargers (Cadex) feature a Target Selector that passes good batteries and red-flags packs dropping below the 80%. SMBus batteries provide the SoH by the Full Charge Capacity (FCC); regular batteries use the Parser
based on the Extended Kalman Filter and coulomb counting.
Intelligent Target Selector
The end-of-life threshold is based on the “what-if.” Figure 4 illustrates pass/fail threshold settings for different applications, governed by risk management. The diagnostic charger checks the usable capacity of a battery against the target setting and passes packs with a green SoH Light when meeting the threshold; below target produces a red light. The SoH Light gives users a simple indication when to service or replace a battery, tailored for each industry.
“What pass/fail capacity do I select?” is a common question asked by users of portable batteries. With the intelligent Target Selector facilitated by RUBY, the diagnostic charger observes the Reserve Charge before charge. Think of an airline pilot who assures having enough fuel to enable a safe landing.
If, for example, the energy demands of a battery increase in a two-way radio, such as hosting the Olympics, the intelligent Target Selector raises the setting to retain the same Reserve Charge with higher traffic. This will prompt more batteries with marginal performance to fail. If, however, energy demands decreases, the Target Selector relaxes the setting and batteries can be kept in service longer.
Fleet Supervisor
RUBY displays the usable capacity of each fleet battery. Reserve Charge is in black and gray. With learned data, RUL can also be given in years. Failing packs are marked in red. Figure 5 illustrates the screen.
Conclusion
As society needs doctors; so will a battery-powered economy rely on battery diagnostics. RCM (Reliability-centered Maintenance) was a forerunner of RUBY in the 1960s to maintain the new 747 jumbo jet. The US military adopted RCM, followed by nuclear power plants, commuter rail and other industries. RUBY will improve stresses and longevity related knowledge in batteries
Last Updated: 28-Apr-2022
Batteries In A Portable World