Battery and Energy Technologies

Battery Reliability

Bombshells and the Meaning of Life

Because traction batteries may cost as much as the vehicle in which they are used customers expect them to last for the lifetime of the vehicle. This is typically eight to ten years and unfortunately it is longer than the time high power lithium technology itself has been around. To meet customer expectations manufacturers are thus compelled to make predictions about battery performance for periods exceeding their experience. Without concrete data about actual cell performance, predictions about future performance and battery lifetime are likely to be inaccurate and since performance and lifetime are both affected by the operating environment and usage pattern these inaccuracies will tend to be magnified. Basing performance warranties on inaccurate lifetime assumptions can give rise to ruinous warranty liabilities. Unexpectedly high product return rates or catastrophic failures during the lifetime of the battery can destroy both the pack maker’s and the customer’s reputation and business.

Manufacturers’ liabilities in many countries are defined by statute but in practice customers demand much better protection than the minimal statutory responsibilities provide and performance guarantees are negotiated between the parties concerned. In the case of batteries, there are normally two steps in the supply chain so that two contracts are involved, one between the cell maker and the pack maker and the other between the pack maker and the end user. Fundamental to both of these contracts is an agreement on the expected life of the product. Despite the importance of this issue, battery lifetimes are not well defined and are thus subject to confusion and uncertainty leading to misinformation, wishful thinking and possibly disaster. The following questions should help to clarify matters.

		The Meaning of Life What is life? Is it calendar life or cycle life? Or is it the "characteristic life" as defined for Weibull lifetime probability predictions; that is "the time before 62.3% of the units fail" (see later). Basing warranties on this latter definition could result in huge warranty costs for the supplier.   What is death? Does a fatality mean sudden death in which case the unit ceases to function or does it mean wearout when the performance of functioning units is merely impaired and now outside of agreed tolerance limits?   Death and Disability? How does the death of associated components such as those in the Battery Management System (BMS) affect the performance of the battery?   Living conditions? Published lifetime specifications normally show performance at nominal temperature and usage rates. What lifetime can be expected over the full range of operating conditions (temperature and rate)? If abuse is excluded, how is it defined?   Lifestyle? Does the battery have an easy life (shallow cycles, intermittent use, comfortable conditions) or is it subject to hard labour, (deep cycles, continuous use, harsh environments)? Are there any usage restrictions?   Whole life or part life? What is guaranteed, the cells or the battery?   Life expectancy? What is regarded as an acceptable death rate before the specified lifetime is reached (1%, 10%)? What is the standard deviation of the times to failure?   Ageing? How is ageing defined (capacity drop off, impedance growth)? How many samples were used to determine the published ageing characteristic? Are the test samples truly representative of the entire population? How many failures occurred during the cycle life testing? Are ageing curves available for batteries operating at extremes of temperature range?   Hope and experience? Is life expectancy greater than period for which evidence available? (Were life tests carried out for the full duration of the expected lifetime of the product or are lifetimes based on extrapolating test data collected over a shorter period?)   Reincarnation ? Is there life after death? Continued use? Scrapped? Recycled? New low power applications?

Practical Problems in Estimating Battery Lifetimes

Determining battery lifetimes is beset with difficulties. Performance data are not generally available and costly to generate since large numbers of batteries must be tested to destruction. Furthermore, the required test period to verify the predictions is often greater than commercial decision lead time. Charge – discharge times for high capacity batteries are very long and using accelerated life testing to determine battery lifetime is most likely to lead to misleading results since battery life depends on temperature, rate and depth of discharge and the test conditions used to accelerate the occurrence of failures are quite likely to introduce new and unrepresentative failure modes.

Failure Predictions

The most commonly quoted measure of cell or battery life is the cycle life. It is defined as the number of cycles completed before the current capacity falls to less than 80% of the capacity when new. It is important that the test conditions specify the depth of discharge for the test cycles since battery cycle life increases exponentially as the depth of discharge is reduced. See graph of Life vs DOD. Specification sheets typically show a series of cell capacity measurements over time indicating a fairly linear reduction of capacity with age or cycles completed. The lifetime is specified as the time at which the capacity line crosses the 80% mark, (10 years in the example below).

Since cell ageing is reasonably linear during the measurement period, it is tempting to extrapolate long term performance from short term results to reach early conclusions avoiding prolonged or impractical test programmes. The linear performance degradation with age implies that wearout failures are due to a single ageing mechanism and unless the tests are continued till all the test samples have failed, there is no guarantee that a second failure mode will not come into play at a later time, accelerating the ageing rate. Furthermore, what the published graphs of capacity against time generally do not show is the dispersion of the results, neither do they show the dependence on temperature and rate.

The diagram above also shows that basing performance warranties on a single line ageing characteristic could be very dangerous since by the target or quoted lifetime, half of the cells will have failed.

Similar arguments apply when the specified lifetime is based on the growth of the cell's internal impedance rather than its capacity reduction.

Failure Modes

Reasonably accurate failure predictions can be made for light bulbs and capacitors which are subject to a subject to simple, consistent failure modes and for which copious failure data are available. Unfortunately batteries are not simple devices and several failure mechanisms may exist simultaneously.

Fatalities may be due to short circuits resulting from contaminated materials, mechanical tolerance problems, burrs, dendrites, and lithium plating. They may also be due to open circuits caused by broken welds, loose connections, or cracks. External faults such as BMS failures can also cause failures in the cells they are supposed to protect.

Latent defects in the components used in the construction of the battery or workmanship defects may cause early failures, commonly called infant mortalities, or a series of random failures.

Wearout failures are due to the gradual deterioration of the cell which may be caused by the breakdown or loss of active chemicals causing a reduction in cell capacity. These failures may in turn result in a fatal condition, such as a short circuit, or they may simply cause out of tolerance performance of the cell. Wearout failures may be initiated or accelerated by the usage pattern to which the battery is subject.

The diagram below shows cell failure distributions due to a variety of failure modes. More details on failure modes can be found in the sections on Why Batteries Fail and Lithium Battery Failures.

The curves represent histograms showing the number of units failing at different lifetimes.

Wearout failures may occur over a short period or be spread over longer time or they may come into play after different periods as indicated by the three wearout distributions shown in blue in the above diagram.

Examples of wearout failures are dendrite growth, lithium plating of the anode, loss of electrolyte due to chemical breakdown or leaks, electrolyte dry-out, dissolving of cathode material, moisture ingress due to vent failure or case seal failure, or cracks in the active materials or the cell case. Each of these wearout failures has its own characteristic distribution.

The failure distribution for the cell is the sum of the distributions of all of the contributing factors as shown in the diagram below. The first curve shows the variation of the instantaneous failure rate over time due to the combination of all the active failure mechanisms. The result is the characteristic “bathtub curve” which is typical for electronic components. The second curve is the cumulative failure distribution corresponding to the instantaneous failure rates. The diagram also indicates possible lifetime specification which the manufacturer may choose to apply.

Components designated for high reliability applications are often subject to “burn in” to weed out the infant mortalities. In cell manufacturing, all cells must go through one or more charge–discharge cycles as part of the formation process and this can serve the dual purpose of identifying early failures.

See more on Ageing in the section on Battery Life

Reliability Predictions

To predict the cumulative number of components surviving or failing within a large population based on the failure rate of a representative sample it is useful to have a mathematical expression to calculate the probability of failures at any given time.

Reliability Concepts

The following curves show typical reliability behaviours represented by time varying functions F(t), R(t), f(t) and h(t).

Weibull Life Distribution Model

One problem with predicting battery lifetimes is that several failure modes exist each with its own characteristic shape and lifetime and this requires a different expression for each failure type. Here Dr. Waloddi Weibull the Danish born engineer came to the rescue. In 1939 he suggested a simple mathematical distribution, now named after him, which could represent a wide range of failure characteristics simply by changing two parameters or constants which are fairly easy to determine.

Cumulative Failure Distribution

For statistically independent failures of a given type the Weibull distribution is given by

F(t) = 1 – exp[-(t/α)^β]

Where F(t) is the cumulative percent failing after time t

α is the Characteristic life of the components (Also known as the Scale factor)

β is the Shape parameter describing the failure distribution curve

The parameters α and β are determined graphically from measured data gathered from life tests on a relatively small number of samples (see below). The expression is simply a mathematical model representing the shape of the distribution and does not imply any cause and effect.

The characteristic life is defined as the time when the cumulative failure percent of the population reaches 63.2%. It is given by making t = α in the above equation. Thus when t = α the cumulative failure percent is given by

F(t) = 1 - e ^-1 = 63.2% regardless of the value of β

If more than one failure mechanism exists within the population each with different characteristics, the appropriate α and β corresponding to each failure mode must be applied separately to obtain the total failure percent. Using the shape and scale parameters developed for similar products is not justified and is likely to lead to erroneous results.

Probability Density Function of Component Lifetimes

The distribution of component lifetimes within the total population is given by

f(t) = dF(t)/dt = (β/α ^β) t ^β-1 exp[-(t/α) ^β]

Examples of distributions with different shape and scale factors are given below. The probability density curves are histograms which represent the distribution of lifetimes of the components within the total population.

Weibull Probability Density Function f(t) Descriptors

In batteries which are constructed from a series string of “N” components from the same distribution with independent failures, where the failure of one component causes the failure of the string, the shape and scale factors for the string are given by

β_N=β₁

α_N=α₁ / (N)^{1 / β}

For random failures (β = 1), the characteristic life of a battery with a string of N cells is 1/N times the characteristic life of the cells, or conversely, the failure rate of the battery is N times the failure rate of the individual cells.

Failure Rates

The hazard rate h(t), also called the failure rate, is given by

h(t) = f(t)/R(t) = (β/α ^β) t ^β-1

For a constant failure rate, β = 1, the mean time between failures (MTBF) is equivalent to the characteristic life and can be deduced from the above equation.

Β=1  and  α=MTBF  and  MTBF=1 / h

Thus the MTBF is the reciprocal of the failure rate.

Weibull Probability Plot To determine the α and β parameters for independent failures of a given type within a given population, it is necessary to conduct a life test on a small representative sample of units. The cumulative percent of the sample failing is then plotted against the time of failure, or number of cycles completed, on Weibull probability paper. (See the example opposite)       The characteristic life α of the population is defined as the time when 63.2% of the sample or population has failed and this is obtained directly from the graph.   The slope β of the graph is given by drawing a parallel line on the β scale outlined on the graph and corresponds to the shape factor of the distribution.

Battery Failures

The reliability of a battery pack can never be as high as the quoted reliability of a single cell. The more components (cells) used in a battery, the less its reliability since the incorporation of more components creates more opportunities to fail.

Additionally, interactions between cells can also cause small production variations between the cells to be magnified resulting in over stress and an increase in failure rates resulting in premature failures. Cell balancing can reduce but not eliminate this. These failures are not independent and are not considered here.

The following table is an example of failure predictions with different cell shape and life parameters relating to wearout of a batch of 80,000 cells used to construct 1000 batteries each containing 80 cells.

Wearout failures are generally due to the gradual deterioration of, or reduction in, the active chemicals resulting in reduced cell capacity. Cell lifetime is defined as the age when the capacity reduction, or the increase in internal impedance, reach predetermined, unacceptable levels. Such a cell failure will not itself result in a battery failure since all the cells in the battery continue to function and the effect of reduced capacity, or increased impedance, of a single cell on the battery will not show until many of the cells are below the capacity or impedance tolerance. In the example below a battery is considered to have failed when at least 20% of cells have failed due to wearout.

A similar table could also represent 3 (or more) separate, simultaneous failure mechanisms active in the battery, for example, one for failures due to dendrite growth, another for electrode plating and another for electrolyte breakdown. In such cases the cumulative cell failures in any year would be the sum of the failures due to each factor.

The table shows that with an 8 year characteristic life for the cells, over half of the batteries will fail before the 8 years are completed and in order to achieve an 8 year life time with a reasonably low failure rate it will be necessary to use cells with a characteristic life of 12 years or more.

Latent faults which could result in the sudden death of the battery rather than gradual wearout are more difficult to characterise. They tend to be random in nature and thankfully occur with a very low frequency. Of particular concern is the occurrence of internal short circuits which could result in fires.

Wishful Thinking

Oft quoted industry references are a prime examples of wishful thinking. It is claimed that the rate of “incidents” with consumer cells (18650s), which are used in their millions, is 1 in 5 million, equivalent to a failure rate of 0.00002%. Assuming this to be true, the same failure rate is then applied to large format cells used in EVs and HEVs. With an average of 80 cells per vehicle, this translates to 1 incident or fire in 60,000 cars or 16 fires per year for 1 million cars on the road. This is of the same order of magnitude as engine compartment fires due to electrical or other faults in conventional cars and while not strictly acceptable it is considered to be tolerable for the time being.

Engine Compartment Fire

This completely ignores the fundamental differences between consumer cells and large format cells, and the environment in which they operate, which make such comparisons invalid. The probability of the occurrence of a short circuit due to contamination between the electrodes is quite possibly proportional to the area of the electrodes. The area of the electrodes used in 200 Ah cells commonly used in EVs will be about 100 times the area of the electrodes used in 2 Ah 18650 cells used in laptop computers. This means we could expect 1 incident in 600 cars unless much stricter process controls were implemented by the cell manufacturers. Besides this, there are most likely to be differences in the active chemical mixes used in the low and high power cells. In addition, EV batteries carry much higher currents and as we know, the Joule heating effect is proportional to the square of the current, creating increased thermal stress within the cells and, following Arrhenius Law, increasing the rate at which both the wanted and unwanted chemical reactions occur in the cells. Furthermore, on a daily basis, automotive batteries are subject to much greater temperature extremes and mechanical stresses such as vibration and shock. As already noted, using the shape and scale parameters developed for one product on other, similar products is not justified.

The very low failure rates due to these random defects means that the corresponding characteristic life will be over 1000 years making Weibull predictions impractical for such low fault levels.

Battery Safety is considered in more depth in a separate section.

System Reliability Improvements

The overall system reliability can be improved by adopting design and operating principles to minimise the stress on the battery.

• The obvious policy is to use the most reliable cells available.
• Carry out thorough cell qualification which is representative of the expected battery operating environment. The tests should include mechanical stress (vibration and shock) and abuse as well as abuse, temperature and electrical stress. The BMS may also be susceptible to conditions of high humidity.
• Burn in can improve cell reliability by ensuring that the infant mortalities occur in the cell or pack maker's plant and not in the customer's battery.
• In general, lower voltage designs will be more reliable than high voltage designs. This applies at the cell level and the system level.
• At the cell level, operating cells slightly below their maximum specified level reduces the stress on the cell and can significantly increase the cell life time. See the effect of reducing the cell charging cut off level.
• At the system level, reliability can be increased by reducing the system voltage but maintaining the system power by increasing the corresponding current. This allows fewer cells in the series chain but it needs cells with higher current carrying capacity or more parallel cells. The system reliability is inversely proportional to the number of cells in the series chain.
• Another way of increasing cycle life by reducing the stress on the cells is by specifying cells with a slightly higher capacity than absolutely necessary. This small capacity reserve reduces the effective maximum operating DOD. The graph of DOD vs Cycle life shows the potential for improvement.
• Instead of large cells, use parallel strings of smaller cells in . This has the following benefits.
• Smaller cells tend to be less stressed and consequently have a lower failure rate.
• Because less energy is stored in smaller cells, the energy released in case of the catastrophic failure of an individual cell will be less. Any failure will thus be easier to contain and less likely to cause fault propagation throughout the battery.
• The failure of an individual cell in a parallel configuration will not cause the failure of the whole battery which could possibly continue functioning at lower power.
• Control the operating environment. Both high and low temperatures are cell killers. The system should incorporate thermal management with heating and cooling circuits, where necessary, to keep the cells operating within their temperature sweet spot.
• HEV batteries suffer the harshest environmental conditions but at least for temperature control there are more options since the thermal management can be combined with the vehicle's conventional engine cooling and passenger climate control systems.
• The main stress on EV batteries comes from the requirement to operate with deep discharge levels.
• Provide redundancy so that the failure of a single cell does not incapacitate the battery allowing it to continue working in emergency situations.
• Use parallel cell strings
• Provide standby or cycling redundancy
• Divide the battery into two or more sections, each with bypass paths which can be switched in to enable a section with a failed cell to be circumvented allowing the battery to continue to function at low power.
• Carry out regular maintenance and use the battery management system (BMS) to monitor the state of health (SOH) of each of the cells to identify any weaklings for replacement.
• See also Battery Safety

Battery Warranty Policy

To develop a suitable policy we need to know

• The warranty period – This is negotiated with the customer and the expectation may be longer than the current generation of battery technology has existed.
• The probability of failure -The percentage of the population which will fail for reasons other than abuse before the proposed warranty period expires.
• The magnitude of the risk event  - The consequences of the failure for any reason
• The immunity from abuse – This is provided by the BMS
• Record of abuse – If it can be shown by data logging in the BMS that the battery has been abused, this can be grounds for voiding the warranty.

Abuse is not considered a warranty issue however it could result in catastrophic failures and it’s no comfort to claim “It’s not my fault”. Cells should be designed to fail in a benign way as the result of abuse and battery packs should be designed to prevent abuse.

Once the failure rates are known, the cost of honouring the agreed warranty liabilities such as repairs or replacements can be calculated. The cost of consequential damage and the damage to the supplier’s reputation caused by selling unreliable or dangerous products is incalculable.

Warranty Contractual Issues

Warranty conditions must be negotiated between three interested parties each with conflicting objectives.

• Customers require lifetime performance guarantees in order to justify their purchase of an expensive battery. They expect the warranty to cover the life time of the packs, not the cells and they want to use the cells in a relatively uncontrolled environment. (The BMS can provide a degree of control at the expense of performance flexibility)

 

• Cell manufacturers are only prepared to provide limited performance guarantees for the cells. They set strict limits on the acceptable cell usage and operating environment. The guaranteed cell lifetime performance will most likely be less than the performance they quote in their specification sheets, or only applicable under strictly controlled operating conditions. There is always a risk that the cell supplier will not honour the guarantee.

 

• Pack Makers are in the middle and subject to the first claim from the end user. By putting the cells into a pack the responsibility for lifetime performance is unavoidably muddied. Because of the mismatch between what the customer wants and what the cell supplier is prepared to guarantee, the pack make is obliged to assume responsibility for the uncovered risk between the two parties.

 

Such is life