Bettering battery analysis

Advance testing for batteries. Source: Cadex

Battery testers are readily available, but we ask: How well do they work? Not everybody is familiar with the test results given, other than knowing that voltage relates to state-of-charge (SoC) and internal resistance (Ri) to power and loading conditions. Capacity readings are outside the capability of most battery testers.

Capacity is the leading health indicator that governs end-of-life when the battery has faded. Loss of energy storage goes mostly unnoticed while the readings on a battery tester remain unchanged. This leaves service personnel unable to decide if a battery needs replacement or not.

Battery performance and symptoms also change with temperature and SoC level. A good battery with a partial charge performs similar to a fully charged battery that is weak. A battery tester should identify these conditions, but most cannot do this.

A battery is like a 'black box'. The pack looks similar if fully charged or empty, new or faded. In comparison, a car tire distorts when low on air and is replaced when the treads are worn. In spite of this disguise, the battery reveals characteristics that instruments can measure to various levels of accuracy.

SoC is checked by voltage, but inaccuracies occur if the battery has been agitated. Voltage readings after a charge stay high for a time and a discharge depresses the voltage. Normalizing takes a few hours. Most battery testers measure Ri by applying a calibrated discharge pulse and observe the voltage drop. Ohm's Law (V=I x R) provides the Ri by dividing the voltage drop with the applied current.

The Ri of a battery does not correlate well with capacity. Tests at Cadex labs reveal a correlation between capacity and Ri of only 51 percent. Ri alone does not provide the full picture. Some battery testers also analyze voltage recovery that relates to state-of-health (SoH). A good battery recovers quicker than a faded one. Similarly, these phenomena apply to lead-acid and lithium-based chemistries.

Figure 2 demonstrates the causes of failure for lead-acid starter batteries. At 48 percent, capacity fade is the most common failure mode induced by wearing out of the active material. In comparison, the rise of Ri only accounts for 12 percent. The test-pool also includes batteries that are over-discharged (23 percent) and have no fault (15 percent). Mechanical and manufacturing defects are at a low 2 percent.

Better test methods will keep batteries in service longer, as packs are often returned with no specific defect. Customer-induced failures, such as low charge, could be eliminated. 

Advanced battery test methods

Battery manufacturers are seeking advanced battery-test methods to improve fabrication and identify user-induced failures. Industries also seek solutions as part of Reliability-Centered Maintenance (RCM). Defined by the Society of Automotive Engineering (SAE) standard JA1011, RCM is a maintenance protocol that ensures continued reliability on mechanical parts by observing wear-and-tear. Introduced in the 1960s, airlines were first to adopt the standard to reduce invasive maintenance. The military accepted the system in 1967, which led to civil users in nuclear power plants, oil & gas, subways, and hospitals.

Currently, RCM does not include batteries. Part of the omission maybe a lack of suitable test methods. A Battery Management System (BMS) could be used, but monitoring voltage, current, and temperature only detect anomalies. The Remaining Useful Life (RUL) representing capacity is not available. Nor does a data stamp provide a reliable indication as to when a battery should be replaced. Batteries are often swapped too soon; however, most stay in service too long.

Checking batteries beyond measuring voltage and Ri, gets complex. A battery resembles a living organism that consists of components that consume active materials, build-up performance-inhibiting films robbing power, and undergoes mechanical stress leading to elevated self-discharge. Battery longevity can be defined by these characteristics:

• Capacity fade, manifested by diminishing energy storage

• Elevated internal resistance, reflecting in decreased loading capabilities

• High self-discharge, induced by stress can lead to safety concerns in Li-ion batteries.

Undetected aging effects may cause thermal runaway, which is a concern, especially for Li-ion batteries. Installing RCM for a critical battery system warrants answer to these questions:

1. At what capacity does the battery need replacement?

2. What other battery anomalies must a monitor identify?

3. What is the consequence of a battery failure?

Figure 4 illustrates ANN that analyzes a vast volume of data and provides a classified output with the help of hidden layers. The connecting units called neurons form a biological brain that simulates animal instinct. One might argue: 'garbage in; garbage out', but volume of user patterns can produce amazing results with ANN.

The Cadex lab is testing the accuracies of ANN and machine learning algorithms such as Gaussian Process Regression to determine battery SoH. The Gaussian method is more effective on the lead-acid battery dataset (~90 percent accurate) and gets consistently better results. These findings are also verified by research scientists at the University of British Columbia. Typical applications for the Advanced Battery Analytic System (ABAS) are fleet supervision by tracking battery performance in the field with EIS-based testers and EIS-backed BMS installations. With ABAS, the supervisor sets the Target Selector to calculated capacity thresholds for each battery system with the intent to keep the batteries in service as long as possible while maintaining confidence in the system. Such a system utilizes each battery fully while improving reliability and lowering the environmental impact of discarding batteries too early.