The variety of future powertrain concepts has drastically increased the development cost for automotive manufactures. Profitable investment requires a significantly leaner and efficient powertrain development process. Traditional methods of test and model based development need to be assisted by big data and data analytics. For this purpose, a valuable tool is available at the right time - artificial intelligence (AI). But what does AI really mean in a narrower sense? What concepts lie behind it? And how are the methods and algorithms transferable to powertrain applications? For the first time, this book aims to bridge the gap between automotive engineering and computer science, by illuminating the complexity of current AI concepts and demystifying it for powertrain applications. By elaborating on work processes, it shows how AI could be implemented and how completely novel methods can help us reshape the future of mobility.
The variety of future powertrain concepts has drastically increased the development cost for automotive manufactures. Profitable investment requires a significantly leaner and efficient powertrain development process. Traditional methods of test and model based development need to be assisted by big data and data analytics. For this purpose, a valuable tool is available at the right time - artificial intelligence (AI). But what does AI really mean in a narrower sense? What concepts lie behind it? And how are the methods and algorithms transferable to powertrain applications? For the first time, this book aims to bridge the gap between automotive engineering and computer science, by illuminating the complexity of current AI concepts and demystifying it for powertrain applications. By elaborating on work processes, it shows how AI could be implemented and how completely novel methods can help us reshape the future of mobility.
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