Embracing AI at Porsche: innovative data analysis ensures high-voltage performance

Artificial intelligence (AI) is becoming increasingly important in vehicle development. It has become an indispensable tool, especially for complex systems in a network, such as the battery-electric energy storage system.

The increasing number of highly developed sensors provides a volume of data that could no longer be processed with conventional software. For Porsche, using machine learning and AI for data analysis is helpful for understanding huge amounts of information and varying contexts. Using these tools helps to provide reliable insights into component behaviour and interaction.

AI in battery development

A high-voltage battery is a complex system that is exposed to a wide range of external and internal influences. These influences are made visible by Porsche’s engineers through data analysis and the use of AI in connection with the effects on the energy system. The knowledge gained in this way is an essential basis for developing ever-better components and systems for Porsche customers.

Technical graphics, Macan Turbo – Rear Axle Drive with Transmission

AI supports developers in particular in detecting implausible behaviour within a battery. This allows the algorithms to analyse the balancing behaviour of individual cells and the entire battery as early as the development stage. Balancing refers to the charge balance between the cells of a battery module. If the values deviate from the expected state, the data allows faster conclusions to be drawn about the causes and underlying processes. At the same time, the data quality in the development process is improved, so that later findings from customer vehicles are even more reliable.

In addition to the known main drivers of battery ageing, modern analysis methods can also be used to identify other influences. Through the coupled application of state-of- the-art data-analysis methods and physicochemical models, forecasts and analyses of the ageing of high-voltage batteries in the customer fleet can be created. With the understanding of the various ageing influences on the basis of data analyses, the system developers work on the further optimisation of the operating strategy. All optimisation criteria such as range, charging time, system performance, weight, durability and consumption are worked out.

Functionality of preventive anomaly detection (HVB), 2025, Porsche AG

The results of analyses based on AI must be understandable and explainable in order to create a reliable basis for decision-making for development. For this purpose, what are known as ‘explainable AI’ methods are used. For Porsche, AI is a tool that helps the team to understand complex relationships and take all relevant aspects into account. In combination with the expertise of the sports car manufacturer’s development engineers, this enables a precise classification of the situation at the end of the analysis.

Through an intelligent and adapted system design, the ageing influences identified by AI can be reduced in a targeted manner. Customers benefit from this as the service life of a vehicle battery can be extended significantly.

Preventative anomaly detection – direct to the customer

A particularly innovative data analysis method, which is being applied for the first time to data from the high-voltage battery of the Porsche Macan, is preventive anomaly exploration. This assesses the technical cause and relevance if any anomalies are detected in the data. It ensures the long-term performance of the high-voltage system while helping the development of future products through the findings.

Preventive anomaly detection, 2025, Porsche AG

Preventive anomaly detection uses detectors that use intelligent algorithms to extract, for example, a change in the behaviour of the battery in the online data. The detected anomalies are analysed, deciphered and evaluated in the cloud.

However, if a relevant anomaly should occur, Porsche proactively informs the driver – including specific instructions, including via the MyPorsche app. What is particularly impressive is that this method can evaluate the data of each cell of the battery individually.

Preventive anomaly detection aims to use data-analysis methods to ensure the reliability and performance of vehicles on the one hand and to predict potential limitations on the other. This function is therefore one of the central elements of the quality work of the future.

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Consumption data

Macan 4 Electric

WLTP*
  • 0 g/km
  • 21,1 – 17,9 kWh/100 km
  • 516 – 612 km

Macan 4 Electric

Consumo di carburante / Emissioni
emissioni CO₂ combinato (WLTP) 0 g/km
consumo elettrico combinato (WLTP) 21,1 – 17,9 kWh/100 km
Gamma elettrica combinata (WLTP) 516 – 612 km
Gamma elettrica in aree urbane (WLTP) 665 – 782 km
Classe di efficienza: C

Macan 4S Electric

WLTP*
  • 0 g/km
  • 20,7 – 17,7 kWh/100 km
  • 512 – 606 km

Macan 4S Electric

Consumo di carburante / Emissioni
emissioni CO₂ combinato (WLTP) 0 g/km
consumo elettrico combinato (WLTP) 20,7 – 17,7 kWh/100 km
Gamma elettrica combinata (WLTP) 512 – 606 km
Gamma elettrica in aree urbane (WLTP) 660 – 776 km
Classe di efficienza: C

Macan Electric

WLTP*
  • 0 g/km
  • 19,8 – 17,0 kWh/100 km
  • 536 – 641 km

Macan Electric

Consumo di carburante / Emissioni
emissioni CO₂ combinato (WLTP) 0 g/km
consumo elettrico combinato (WLTP) 19,8 – 17,0 kWh/100 km
Gamma elettrica combinata (WLTP) 536 – 641 km
Gamma elettrica in aree urbane (WLTP) 696 – 831 km
Classe di efficienza: C

Macan Turbo Electric

WLTP*
  • 0 g/km
  • 20,7 – 18,9 kWh/100 km
  • 518 – 590 km

Macan Turbo Electric

Consumo di carburante / Emissioni
emissioni CO₂ combinato (WLTP) 0 g/km
consumo elettrico combinato (WLTP) 20,7 – 18,9 kWh/100 km
Gamma elettrica combinata (WLTP) 518 – 590 km
Gamma elettrica in aree urbane (WLTP) 670 – 762 km
Classe di efficienza: C

Taycan Turbo GT

WLTP*
  • 0 g/km
  • 21,2 – 20,5 kWh/100 km
  • 540 – 559 km

Taycan Turbo GT

Consumo di carburante / Emissioni
emissioni CO₂ combinato (WLTP) 0 g/km
consumo elettrico combinato (WLTP) 21,2 – 20,5 kWh/100 km
Gamma elettrica combinata (WLTP) 540 – 559 km
Gamma elettrica in aree urbane (WLTP) 656 – 679 km
Classe di efficienza: C

Taycan Turbo GT with Weissach package

WLTP*
  • 0 g/km
  • 20,8 – 20,7 kWh/100 km
  • 550 – 555 km

Taycan Turbo GT with Weissach package

Consumo di carburante / Emissioni
emissioni CO₂ combinato (WLTP) 0 g/km
consumo elettrico combinato (WLTP) 20,8 – 20,7 kWh/100 km
Gamma elettrica combinata (WLTP) 550 – 555 km
Gamma elettrica in aree urbane (WLTP) 691 – 699 km
Classe di efficienza: C