Artificial intelligence in vehicle development to make drive systems more efficient and sustainable
Porsche says it uses artificial intelligence (AI) in vehicle development to make drive systems even more efficient and sustainable and further optimise development processes.
The company uses AI to give computers the unrivalled ability to automatically analyse problems and solve them—much like a human being. This potential is being exploited in powertrain development, both in e-mobility and in the internal combustion engine as a traditional mechanical engineering domain.
In the development of traditional combustion engines, for example, AI predicts the gas content in the engine oil. Since high gas content leads to oil foaming and thus to reduced lubricity, the oil system must be designed for the lowest possible gas content. However, measurements can hardly be carried out in the vehicle with the engine running. A new AI process from Porsche now provides reliable forecasts of the gas content in the engine oil.
In the development of classic combustion engines, a new AI process now provides reliable forecasts about the gas content in engine oil. Porsche first used this when developing the engine of the Porsche Cayman GT4.
“In the process, we were able to keep the computing capacity required for the AI algorithm so low that we can easily integrate it into the engine development process,” says development engineer Hong Truc Jung, who is responsible for AI tools in the powertrain at Porsche. “During standard bench tests, it runs continuously and provides us with the desired data in the process.” The first engine for which Porsche applied the new AI process was the six-cylinder boxer engine for the Porsche Cayman GT4.
Battery state analysis for electric vehicles
One AI application from the Porsche technology subsidiary Porsche Engineering determines the ageing behaviour of the lithium-ion battery, which drivers of electric vehicles are already using to make predictions about battery range while driving.
Continuous health check: an AI application from Porsche Engineering evaluates the condition of the lithium-ion battery and can thus predict the remaining range of the electric vehicle.
The AI algorithm uses the internal resistance of the battery to infer its aging. Among other things, it takes into account influences such as temperature and state of charge and the results of long-term and fleet tests. Furthermore, the AI adapts to the driver’s user profile to make the prediction increasingly precise in the vehicle.
Porsche Engineering Reinforcement Learning (PERL)
Porsche Engineering has developed a particularly flexible development methodology with high potential for various use cases based on the AI method deep reinforcement learning. “Our PERL methodology, short for Porsche Engineering Reinforcement Learning, goes beyond the specific solution of individual tasks, because it understands systemic relationships and learns to make strategic decisions,” explains Matthias Bach, Senior Manager of Engine Application and Mechanics at Porsche Engineering.
Since the neural networks of the AI algorithm can vary several parameters simultaneously, including in combination with each other, and predict the resulting effects, PERL is ideal for complex engine application tasks and many other areas of vehicle development. “With PERL, we can reduce development time while achieving better application results than would be possible with conventional methods,” adds Bach. The method is currently undergoing practical testing in drive development. In the medium term, Porsche Engineering will also use it in other areas such as the complete vehicle, chassis, and electrics/electronics at Porsche Engineering.