Discover developments in non-invasive rapid-test technologies.
There are no shortages of battery testers, but a closer look reveals that most lack accuracy. The leading health indicator of a battery is capacity, a value that is difficult to obtain on the fly. Stating that a battery tester measuring the internal resistance will also provide capacity estimation is misleading. Advertising features that are outside the equipment’s capabilities confuses the industry into believing that complex test results are attainable with basic methods. Resistance-based instruments work well in identifying a dying or dead battery — but so does the user. Vendors often overstate the ability of their incumbent battery testers and an analogy is promoting a shampoo that promises to grow lush hair on a man’s baldhead.
Without reliable test devices on hand, battery testing becomes guesswork, resulting in good packs being replaced too soon and passing weak ones, causing some to fail soon after checking. Lack of accurate battery testing also causes unnecessary replacements under the battery warranty program. Examining warranty returns reveals that less than 10 percent of these batteries have a manufacturing fault. (See BU-901: Difficulties with Testing Batteries) Furthermore, a battery may be exchanged repeatedly without knowing the cause of the repeat failure. The problem is often outside the battery and the battery manufacturer is being held ransom.
The challenge arises when assessing a battery as part of routine service before performance degradations are noticeable. Such a test is only effective when including a capacity measurement. Capacity oversees the energy storage, governs the runtime and predicts the end-of-life. Internal resistance, on the other hand, is responsible for the power to crank the engine and deliver high current under load on demand. A snapshot taken with a CCA tester on a starter battery refers to the resistive battery condition only. Better electrolytes and corrosion-resistant electrode materials are keeping the resistance on modern batteries low. Failure due to elevated resistance has become rare.
No universal instrument exists that is capable of measuring the capacity of any battery that may come along, as is possible measuring the voltage. Among current methods, scalar is the most simplistic. Scalar takes a battery reading and compares the result with a stored reference is often a resistive value. Most single-frequency AC conductance testers measuring CCA use the scalar concept.
The vector method applies signals of different currents or excites the battery with several frequencies, and evaluates the results against preset vector points to study the battery under various stress conditions. This adds complexity without gaining major benefits and the vector method is not as commonly used as the scalar in form of single-frequency AC conductance.
Spectro™ combines electrochemical impedance spectroscopy (EIS) with complex modeling. It scans the battery with a frequency spectrum as if to capture the topography of a landscape and compares the imprint with a matrix. (See also BU-902a: How to Measure CCA) With appropriate matrices, Spectro can estimate battery capacity, CCA and SoC, providing the most in-depth battery analysis. In spite higher complexity, scientists bet the future in this developing new technology. Figure 1 summarizes the three battery test methods.
|Scalar||Single reference point; pulses or single-frequency excitation||Automotive, stationary; simple, commonly used||Voltage, CCA, internal resistance, no capacity|
|Vector||Multiple frequencies, currents; compares against vector||Automotive, stationary; less commonly used||As above. More complex with marginal gain|
|Spectro™||Combines EIS with complex modelling; fuses data to derive at capacity, CCA, SoC||Lead- and lithium-based batteries||Provides CCA, capacity and SoC with appropriate matrices|
Figure 1: Methods of data collection. The table compares scalar, vector and Spectro™ which combines electrochemical impedance spectroscopy (EIS) with complex modelling.
A matrix is a multi-dimensional lookup table against which the readings are compared. Text recognition, fingerprint identification and visual imaging operate on a similar principle. In battery analysis, matrices are primarily used to estimate capacity; however, CCA and state-of-charge will also benefit from using a matrix.
Spectro™ correctly predicts 8 out of 10 batteries on capacity, and 9 out of 10 on CCA. Combining these two classifications provides significant improvement in test accuracies over units measuring only CCA. Many resistance-based testers deliver state-of-health predictions that are not much better than 5 correct in 10, a result that can be compared with tossing a coin. Many service personnel are unaware of the low prediction rate as lab verifications are seldom done.
There is a desire for higher accuracies but the industry must understand that a battery can only be diagnosed if measurable indicators are present. Best results are achieved when testing a battery that is pulled from the field. New batteries that have not been fully formatted or have been in storage provide less accurate results because the symptoms may be skewed. In addition, a battery with low capacity and a battery with a partial charge have similar visible pointers and the test method must distinguish between these two deficiencies.
Separating capacity, CCA and SoC as separate battery identities is a challenge and cannot be fully satisfied. Most battery testers work within a SoC range from 60–100 percent. If too low, the device prompts to charge and retest.
Creating a matrix involves scanning many batteries at different state-of-health levels. The more batteries that can be included in the mix of same model but different capacity, the stronger the matrix will be. A well-developed matrix should include battery samples with capacities ranging from 50 to 100 percent. This provides a solid span that resembles a bridge with many pillars. The population should include batteries from hot and cold climates with diverse user patterns. For example, a starter battery in a Las Vegas taxi experiences different stress levels than in grandma’s car cruising in the countryside of northern Germany.
It can be difficult to obtain faded batteries, especially with newer models. Forced aging by cycling in an environmental chamber is of some help but age-related stresses are not being presented accurately and the formed symptoms can fool the system.
Generic matrices serving a group of similar batteries are most practical and there is a move towards the generic solution. One tester can service most car batteries by simply selecting the capacity and CCA rating but the result is in a pass/fail classification based on a capacity threshold. This is acceptable for most service personnel as the instrument makes the final decision, eliminating uncertainties and customer interference.
A battery check must undergo multiple tests and an analogy is a medical doctor who examines a patient with several instruments to find the diagnosis. A serious illness could escape the doctor’s watchful eyes if only blood pressure or temperature was taken. While medical staff is well trained to evaluate data points taken, most battery personnel do not have the knowledge to read a Nyquist plot and other data on a battery scan. Nor are test devices available that give reliable diagnosis of all battery ills. A user of a battery test device must be reminded that a battery test is not a measurement per se but estimation by looking at several symptoms. Some basic education in testing batteries is a prerequisite.
Last updated 2015-05-21
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