Discover developments in non-invasive rapid-test technologies.
There are no shortages of battery testers, but a closer look reveals that most lack accuracy. Capacity, the leading health indicator of a battery, is difficult to obtain on the fly. Stating that a battery tester measuring 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 tests can be done with basic methods. Resistance-based instruments can identify a dying or dead battery – so does the user. Vendors often knowingly overstate the ability of battery testers, and an analogy is promoting a shampoo that promises to grow lush hair on a man’s bald head.
Without reliable test devices on hand, battery testing becomes guesswork, resulting in good packs being replaced too soon and passing weak ones, only to fail on the road 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. Most faults are user-inflicted. (See BU-901: Difficulties with Testing Batteries)
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 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 through most of the life. Failure due to elevated resistance has become rare and may only develop at the end-of-life. (See BU:901: Fundamentals of Battery Testing.)
Unlike reading a voltage, no universal instrument exists that can measure the capacity of any battery that comes along. There are three common testing concepts: Scalar, vector and EIS with complex modelling (Spectro™).
Scalar is the most simplistic of the three. It takes a battery reading and compares it with a reference that is often a resistive value. Most single-frequency AC conductance testers measuring CCA are based on the scalar concept.
The vector method applies signals of different currents or it excites the battery with varied frequencies and then evaluates the results against preset vector points to study the battery under various stress conditions. This adds complexity and the added benefits are marginal.
Spectro™ scans the battery with a frequency spectrum as if to capture the topography of a landscape and compares the imprint with a matrix to estimate battery capacity, CCA and SoC. Spectro™ promises the most in-depth battery analysis but it is also the most complex. (See also BU-904: How to Measure Capacity.). 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 for battery rapid-testing. 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. Most resistance-based testers deliver state-of-health predictions that are not much better than 5 correct in 10, results that can be compared with tossing a coin. Many service technicians are unaware of the low prediction rate, as lab verifications are seldom done.
There is a desire for higher accuracies but a battery can only be diagnosed if measurable symptoms are present. While packs pulled from field give the most reliable results, outliers often lack formatting or had been in prolonged storage. To also test these batteries with certainty, matrices can be developed that include the anomalies.
State-of-charge also plays an important role and the tester must distinguish between low charge and low capacity. Both conditions lower battery performance and are difficult to identify. Most battery testers work down to 70 percent SoC; Spectro™ goes down to 60 percent.
Creating a matrix involves scanning many batteries at different state-of-health levels. The more batteries with different capacities of the same model are included in the mix, the stronger the matrix will become. A well-developed matrix should include naturally-aged battery samples with capacities ranging from 50 to 100 percent. An analogy is a bridge with many pillars to eliminate weak spots.
The population should also include batteries from hot and cold climates and different uses. For example, an aging starter battery in a Las Vegas taxi will show different symptoms than grandma’s car battery cruising in northern Germany taking her grandchildren for a ride.
Obtaining faded batteries is difficult. Forced aging by cycling in an environmental chamber is of some help but age-related stresses are not presented accurately and the learned symptoms can fool the system. This is especially visible with Li-ion batteries. Although the capacity is down, the Nyquist plot does not follow the signature of natural aging as part of daily usage. (See BU-907: Testing Lithium-based Batteries.)
A generic matrix is most practical as it serves a group of batteries. Generic matrices for the Spectro™ system are available for most car batteries; the user simply enters the capacity and CCA ratings. Instead of a numeric readout, the generic matrix provides pass/fail classification based on a capacity threshold. This solution is acceptable for most service personnel as the instrument makes the decision, eliminating uncertainties and customer interference.
A battery must undergo multiple checks and an analogy is a medical doctor examining a patient with several tests to find the diagnosis. A serious illness could escape the doctor’s watchful eyes if only blood pressure or temperature were taken. While medical staff are well trained to evaluate the data points taken, most battery personnel do not have the same knowledge and only want to know if the battery is dead or alive. Nor are battery test devices capable of providing a detailed diagnosis of all battery ills. The battery user must be reminded that a battery tester is not a universal test tool but an estimation device that works for a designated battery population.
Last updated 2016-01-22
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