Optimal use of hybrid drivetrains in industrial environments thanks to task recognition
Optimal use of hybrid drivetrains in industrial environments thanks to task recognition
Whereas fully electric vehicles have difficulty finding their way to our roads, hybrid cars are a common sight for a number of years already. They are ideal for short trips into town, consume less fuel and are silent. Also in industrial environments such as factories or warehouses, hybrid vehicles are doing quite well. In some cases, for instance fork-lift trucks in a warehouse, these vehicles always take similar routes. By adjusting the control systems of such vehicles, fuel consumption can be reduced by 40%.
What is a hybrid drivetrain?
A hybrid drivetrain makes use of more than one drive technology. A hybrid vehicle typically combines a conventional internal combustion engine (petrol or diesel) with an electric motor. Both motors work together to power the vehicle. Which motor is used when depends on the driving pattern. For instance, the combination of both motors generates extra power when riding uphill or accelerating. On the other hand, when braking or slowing down, kinetic energy is released that will charge the battery of the electric motor. This is a major benefit as otherwise this energy would simply go lost.
It goes without saying that short distances with many start & stop manoeuvres (a trip into town for instance) are much more efficient with an electric motor whereas driving continuously at a fairly constant speed (think of driving on a highway) is more efficient with a combustion engine as in this scenario the electric motor is hardly ever being recharged. That is why a hybrid vehicle, other than a car with only a conventional combustion engine, has a supervisory controller that, based on the driving behaviour, determines which energy source is used so as to minimise fuel consumption and CO² emission.
CONTINUOUSLY ESTABLISHING WHICH DRIVE IS MOST EFFICIENT
Also for a hybrid vehicle in an industrial environment, several driving patterns are possible. Take for instance a fork-lift truck in a warehouse: it takes the same route several times a day, from parking area to parking area, to collect and deliver goods. Some routes are shorter and faster than others and some fork-lift trucks always cover the same identical route. It is therefore interesting to control the drives not only according to the driving behaviour but also taking into account the route that is covered. For short distances with many sharp turns and braking manoeuvres, the electric motor will be used more, for longer distances the combustion engine is a better option.
By taking into account the routes followed by a hybrid vehicle in an industrial environment, fuel consumption can be reduced by 30%. To this end, the control system must be geared to the specific vehicle and its typical tasks. If you succeed in making the controller task-dependent in real time, fuel savings may even rise to 40%. This requires a dynamical programming of the controller: based on speed profiles, the controller will recognize the route. Task recognition as it were. In this way, the controller can establish at any time which drive is most energy-efficient, based on what the vehicle will most probably do, and adjust when needed.
Such fuel economies generate huge savings for a company, not to mention the environmental impact this will have. It also has positive effects for employees as less CO² emissions will improve the air quality. A triple win-win-win in other words.
