Please Rotate Your Device
While EV uptake is on the rise, maintaining optimal EV battery performance is a pressing issue that must be addressed.
For automakers, the focus is on ensuring all batteries perform to the agreed standard for the duration of the warranty period, at the very minimum. Vehicle reliability is also crucial from a reputational perspective, while preventing or minimising faults can have a major influence on their outlay on warranty costs. Equally the used market faces similar challenges, with many potential buyers harbouring concerns around the long-term health of batteries, further proof that ensuring EV battery maintenance is widely accessible is essential.
This article will examine how the use of digital twins can enable optimal EV battery health, resulting in better outcomes for manufacturers, vehicle owners, and the environment.
According to IBM, a digital twin is a virtual model designed to accurately reflect a physical object, in this case, an EV battery. Data is gathered from innumerable EV batteries to create a digital model of how EV batteries degrade over time. This data can be used as a reference for battery state-of-health, informing us of routine battery, cell, and module behaviour so that we can identify when a cell or module is performing below the desired level and where an intervention may be required.
It is worth noting that the benefits of digital twins do not apply exclusively to failed battery packs; the insights they provide can also optimise the health of any battery pack showing signs of degradation.
For OEMs, there is the ever-present risk of faults occurring during the warranty period. In some cases, degradation may be related to an inherent flaw in either the assembly or design, while usage patterns such as fast charging and more aggressive driving styles may equally contribute to a drop in performance. Our unique testing capability can highlight the weakest performing cells, which allows us to pinpoint the problem so that precise action can be taken. Exactly how precise, is dependent on the scenario.
Autocraft’s digital twins can predict the performance impact of replacing each module, for instance, ‘if we replace modules 3 and 7, this will increase range by x amount’. Having this level of granular detail allows for customers to take more informed decisions on the best possible repair approach based on their cost and performance goals. So, while replacing multiple modules may yield a greater improvement in range, it might be more cost-effective to replace only the modules that will restore performance to the desired level. It is an iterative process that can be undertaken to drive incremental improvements. This also comes with a fraction of the environmental cost of replacing entire packs with new ones.
Such benefits are also transferrable to the used EV market, where sellers will be able to make commercial decisions based on the costs involved and how subsequent performance improvements will enhance the value to prospective buyers.
Having a reliable, scalable process for tackling in-warranty battery faults is essential for OEMs. Of even greater value, however, is being able to spot impending issues so that they can be prevented in the first place. Most conventional testing methods do not offer this kind of predictive capability, which leaves manufacturers playing catch-up and potentially vulnerable to additional repair costs and damage to their brand arising from repeat failures. By comparing test results with the digital twin, we can extrapolate how each cell will perform and replace any cells where there are any doubts around future performance. Any action that can reinforce the perceived reliability of electric vehicles can only be a positive for the evolution of the industry, overcoming yet another barrier to widespread EV adoption.
As early entrants to the EV battery testing and repair market, Autocraft has been continually refining our methodology for restoring optimal EV battery health for many years. Our pioneering REVIVE™ solutions harness ground-breaking dynamic testing technologies which incorporate the use of digital twins as a way to achieve greater accuracy, speed, and predictive capability within the repair and battery health optimisation process.
Since developing our digital twin in collaboration with several world-class academic research partners, we acquired invaluable data on battery performance from thousands of battery packs, each one informing our model and providing us with greater ability to predict future battery health.
Our cutting-edge testing capability allows us to gain an accurate picture of battery health on a cellular level and where specific issues are located. This is crucial in both spotting outlier cells which are hampering overall performance and for identifying and grading existing cells from other packs that can be used as replacements. Our industrialised process for swapping modules with healthy ones to restore optimal health is crucial to carrying out fault-free repairs at scale, directly addressing the root cause of sub-optimal performance. In doing this, we can halt premature decline and maximise EV longevity, which are both crucial to unlocking the performance and environmental benefits of electric vehicles.