Fifty-four years after Karl Benz decided to put a gas-powered engine into a three-wheeled vehicle and filed a patent for his innovation which revolutionized the automobile industry in 1886, General Motors (GM) proposed an idea of autonomous maneuvering an electric vehicle (EV) ushered by radio controlled electromagnetic (EM) fields and assisted with a particular roadway suitable for the navigation. This idea soon became a reality in 1958 when GM deployed sensors on a vehicle that could spot the current flowing through a wire installed in the road. But it was not until the 80s that completely self-sufficient autonomous vehicles (AVs) were introduced by Mercedes Benz, Carnegie Mellon University’s Navlab, Autonomous Land Vehicle, and Eureka Prometheus Project. This can be marked as the next big revolution in the industry. Since then, remarkable innovations and strategies have been developed to a point where in today’s world, autonomy, if not complete, has been deployed on commercial vehicles. This has become so huge that now the autonomous industry can be studied separately from the conventional automobile industry. The power and efficiency of genetic algorithms (GAs) when paired with the technology of self-driving cars could only result in an autonomous environment that is lucrative enough to become completely self-sufficient. Apart from this, a ginormous amount of data has been gathered from the autonomous electric cars which Tesla, Inc. has produced. The data which we have gathered till now is almost sufficient for the self-driving vehicle industry and on top of that, we have collected information that demonstrates the behavior of electric maneuvers of all the vehicles including range, power delivery, efficiency, battery health, etc. One of the perks of genetic algorithms is that it is versatile, which means we can run several unique genetically optimized models to the data gathered by the EVs and enhance the electrical aspects of the vehicles as well.
Application of Genetic Algorithms to Electrical Vehicle Industries
Springer Tracts in Nature-Inspired Computing
2024-02-21
16 pages
Article/Chapter (Book)
Electronic Resource
English
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