Achieving Battery Intelligence in EVs via Software
The electrification of transport is accelerating at an exponential rate. Major auto manufacturers are launching more than 50 battery-electric–vehicle models in 2023, and governments around the globe have introduced regulations to phase out internal-combustion engines in favor of battery EVs, some by 2035.
There are also governmental incentives to accelerate the transition, such as the U.S. Inflation Reduction Act of 2022, which offers a $7,500 tax credit against the purchase of new EVs. Additionally, in 2023, the U.S. EPA aims to finalize its rules to phase out greenhouse emissions from heavy-duty vehicles and trucks under the Clean Truck Plan.
Charging capacity needs to keep up with this incoming tidal wave of battery EVs, but infrastructure and sheer logistics are major headaches. For instance, suburban consumers often charge their vehicles at home in their garage, but in cities, where the majority of charging needs to happen and people live in apartment buildings stacked on top of one another, overnight charging may not be possible.
Fleet operators, on the other hand, do not have the luxury of taking their vehicles offline for several hours to charge them. According to the PBS program “Taxi Dreams,” a New York taxi drives, on average, 180 miles in a 12-hour shift, which means that vehicle must charge only during a few short breaks.
The answer to these challenges is fast charging and stations that deliver a minimum of 250 kW for a 25-minute charge (normally from 10% to 80% of state of charge). These are being deployed throughout the U.S., Europe and Asia. However, they continue to suffer their own sets of challenges and present serious dangers.
First, fast charging severely degrades the energy intensity of the battery, which compromises driving range and longevity. Charging a lithium-ion battery requires lithium ions to traverse inside the battery from the cathode to the anode, then embed themselves within the matrix of the graphite material of the anode. This is the primary mechanism of storage. Fast charging means that the anode must accept a large flux of lithium ions within a short time. Historically, battery manufacturers produced batteries that can fast charge by making the anode ultra-thin, then stacking alternating anode layers with cathode layers. Unfortunately, this is what degrades energy intensity and range, which is a game of “whack a mole” that battery designers often go through.
Second, charging conditions and the physical attributes of a battery affect consumer safety. The high flux of lithium ion risks, under certain conditions (such as fast charging at lower temperatures below 15°C, or 59°F), the formation of metallic lithium dendrites that can lead to internal short-circuits and disastrous outcomes (battery fires). The presence of minute manufacturing defects may also cause dendrite formation. Increasing energy density in lithium-ion batteries also leads to a higher ion flux, further exacerbating the challenges during fast charging.
Aside from reinventing the battery, the most optimal and affordable way to address these challenges and enable fast charging is battery intelligence. In the form of software, it augments the standard battery management system in three ways:
- Measuring in real time the health of each individual cell in the vehicle battery pack and determining its propensity for developing dendrites
- Estimating the battery’s longevity using extensive battery models and field data and determining how longevity changes with fast charging and other operating conditions
- Making minute and dynamic adjustments to the charging current to simultaneously optimize the charge time, as well as the battery’s longevity and safety
The software runs on the battery management electronic control unit. It must seamlessly integrate into the auto manufacturer’s battery management platform and operate with a minimal computational footprint, often using under 10 kB of memory.
Battery pack configurations are unique to a manufacturer’s vehicles. For example, the battery pack of a Tesla Model 3 LR weighs 469 kg and contains 4,416 individual cells. That of a Porsche Taycan Turbo S is 633 kg with 396 cells. Thermal gradients across the vehicle exacerbate slight manufacturing differences among the cells. Battery cells will also age differently. Fast charging makes a complex situation more complex. The role of battery intelligence is to address this complexity and all the unique challenges of pack and cell design, seamlessly scaling to enable auto manufacturers to differentiate their vehicles with the safest and most optimal implementation of fast charging.
Software will increasingly become key to advance the state of the battery. As an example, Qnovo’s software solutions enable EVs to fully utilize 250-kW charging stations for charge times under 25 minutes, at any time, while maintaining an exceptional battery longevity that may reach nearly 1 million kilometers (>621,000 miles).
As we look to a future of batteries everywhere (in your car, in your home, in the grid), software is the only tool that will manage the optimal flow of energy between these nodes. It will manage the batteries in fleets of EVs and commercial trucks. Software will ensure that your vehicle battery will not degrade abnormally if you use it to power your home (what is known as vehicle-to-grid). It will also guarantee that your battery warranty will be good for many years, on and off the road.
Software will be the backbone to the charging infrastructure, so you don’t have to worry about your next charging stop.
Author: Nadim Maluf, CEO of Qnovo