Review by Max Fuchs, BIKE editor
Motor power alone is no longer the sole determinant of an e-MTB’s quality. Software, apps and regular updates are becoming key selling points. Even today, smart riding modes, digital anti-theft systems and customisable motor settings make everyday life significantly easier. However, truly adaptive AI is still in its infancy. The most exciting developments are therefore likely to hit the trails in the coming years.
Is AI really already being used in modern e-MTBs?
The term is currently used quite loosely. In fact, most systems to date have relied on sensor fusion, algorithms and adaptive control – not generative AI. Nevertheless, artificial intelligence is likely to play a greater role in the future.
Possible examples include:
- self-learning assistance modes that adapt to your personal driving style
- Predictive energy management based on route profile and temperature
- automatic chassis tuning
- early detection of wear or faults
- smart route suggestions based on previous journeys
The bike becomes a computer
Bosch, Specialized, Avinox and others are investing more and more in software. Rather than simply developing new motors, they are adding new functions to existing systems via updates. For riders, this means that their bikes can continue to receive new features for years to come without the need to replace any hardware.
Automatic engine control
One of the most useful developments is intelligent assistance modes. Systems analyse power output, cadence and speed – and sometimes even the gradient via incline sensors – and automatically adjust the motor output. This means riders need to switch between assistance levels less often and enjoy a smoother riding experience.
Bosch calls this mode ‘Auto’, Specialized uses ‘Auto Mode’ and ‘Dynamic Micro Tune’, whilst Avinox also employs adaptive power control.
Custom engine tuning
Almost all manufacturers now offer a wide range of customisation options. The level of assistance, maximum power, responsiveness and acceleration can all be adjusted via an app to suit your own riding style. As a result, e-MTBs now offer a much more personalised riding experience than they did just a few years ago.
Navigation and route planning
Many apps now do much more than just control the motor. Rides can be planned, displayed on the screen and calculated, including a battery range estimate. Some systems even estimate whether the planned route can be completed with the current battery charge.
Over-the-air updates
In the past, you often had to visit a dealer to get new software. Nowadays, many updates are downloaded directly to the bike via a smartphone. This allows faults to be rectified or new features to be added without incurring workshop costs.
Anti-theft measures are becoming smarter
Digital locking features are now standard on many premium systems. The smartphone acts as a key, the motor is locked electronically, and some manufacturers incorporate GPS tracking or motion alarms. Whilst these features do not replace a traditional bike lock, they do enhance security.
Maintenance via the app
Apps display service intervals, battery health, error messages and system data. Some systems even allow the dealer to carry out a remote diagnosis, which helps to pinpoint problems more quickly.
Conclusion by Max Fuchs, BIKE editor
There is still no sign of genuine artificial intelligence in today’s e-mountain bikes. Although terms such as ‘AI’ or ‘smart’ are often used as marketing buzzwords, most systems currently rely on sophisticated algorithms, sensor fusion and adaptive control systems – not on machine learning AI models.
However, this does not detract from their usefulness. On the contrary: software is increasingly becoming a key quality feature of modern e-MTBs. Intelligent assistance modes, over-the-air updates, digital anti-theft protection and customisable motor characteristics already demonstrate the potential that well-developed software holds.
The next step is therefore only a matter of time. As soon as more powerful AI models find their way onto the bikes, the drive system, chassis and energy management could go beyond simply assisting the rider to actually understanding their riding style, learning from it and continuously optimising the bike. The hardware for this is already in place in many systems – now the software needs to catch up.
Max Fuchs
Editor