ai-charge technologies
AI-Charge Technologies
Sustainable charging and energy management for electric vehicles
ai-charge technologies
AI-Charge Technologies
Sustainable charging and energy management for electric vehicles
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AI-Charge Energy Management

Artificial intelligence as key technology for charging of electric vehicles.

The charging and energy management services by AI-Charge Technologies GmbH enabling intelligent solutions for real-time prediction and optimization of sustainable energy and mobility systems.

We are supporting vehicle owners as well as public and commercial institutions in operation of electric vehicles in order to achieve the best possible ecological and economic benefits.

Our approach is based on the idea that individual energy flows are predictable. Optimal energy consumption and transfer scenarios will controlled with the help of self-learning systems. Various energy resources such as renewable or stored energies will be considered.

The underlying IoT platform provides always/everywhere connectivity and intelligence for green and sustainable solutions. Thus smart components can be integrated quickly and easily for the optimal control of energy flows.

We deliver a cross-platform app and intelligent charging points customized for your e-mobility challenge.

#Energy #Efficient #Emotion​

Solar Mobility

Electric Vehicle interconnected to renewable energy sharing networks

Micro Grid

Energy concepts for smart home to scaled smart cities

Charging Eco-System

Smart wallbox and smart charging

Power Grid

Concepts for load and reactive power controls

Energy Efficiency

Cloud-based energy analysis and real-time optimization maximizing renewable resource efficiency

Using new methods like artificial intelligence and increasing connectivity introduce essential challenges for future energy management architectures.

IoT networking enable the systematic ​recording and forecasting of energy flows in plants, buildings or vehicles and thus creates an important basis for maximizing energy efficiency and reliability as well as lower asset costs and CO2 emissions.

Particularly in the case of self-powered systems based on renewable energy sources, a high potential return can be achieved through the maximum use of self-generated electricity and the lowest possible purchase of conventional electricity from the power grid.

Our approach to maximizing energy efficiency is based on the intelligence of self-learning systems. The basis is formed by artificial neural networks predicting the best energy-optimal scenario from a variety of information sources.

In addition, prosumers receive a detailed energy report with information, e.g. on the degree of self-sufficiency achieved or on operating economics.

#Energy #Efficient #Emotion​

Scaling Up Smart Cities

Understanding of how the application of disruptive technology can solve urban challenges

Smart Sensing

Climate & Environment Sensing and Evaluation

Smart Mobility

Infrastructure and intermodal transport

Smart Energy

Building, environment and grid integration