Artificial Intelligence (AI) is the science of getting machines to think & take decisions like human beings do. AI requires algorithms that learn from human actions & habits without the need of manual programming. Then there is Machine Learning (ML), which is a subset of AI and uses algorithms to learn and improve performance.
Artificial Intelligence holds vital importance in improving electric vehicles and making the EV charging process efficient.
AI & EV Battery:
AI is being utilised in battery research to improve battery performance, battery pack management & energy management. Early detection of faults in a battery cell or module ensures the driver’s safety and EV longevity.
Safety, dependability & economic feasibility of EVs are vital for their mass adoption. Companies are significantly improving these through the implementation of AI. It helps dive deep into EV battery life cycle management. Machine Learning uses predictive intelligence to identify potential battery deterioration signs and their causes. The system gathers and monitors large amounts of data on battery life, performance, state of charge (SoC), stress from erratic acceleration & sudden braking, temperature, number of charging cycles, etc.
AI & EV Charging:
EV charging comes with its own set of challenges in the form of unexpected peak load & voltage fluctuations. For the EV charging network to function seamlessly within the energy limitations of the local grid, it needs to balance EV charging demand and available load supply. Hence, it is important to predict the energy needs and charging demands of charging stations and to use the available resources efficiently. So far, AI has been doing it successfully in western countries.
In the case of renewable energy-fed EV charging, AI has proven helpful in ensuring smooth power distribution taking into account renewable energy intermittency (especially solar power).
Apart from this, AI is also being used by EV charging companies for charging station site selection, congestion check & definitive energy scheduling. Netherlands-based ElaadNL, provides optimally placed charging spots throughout the country and promotes the use of data analytics, including the use of Machine Learning, to plan for EV charging infrastructure.
AI & Indian EV Players:
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- Indian EV startup ‘Tork Motors’ uses an AI system called ‘TIROS’ (Tork Intuitive Response System) which analyses vehicle performance parameters, riding pattern and GPS data. TIROS ensures that all the parameters are within the defined optimum range to facilitate healthy battery conditions. The system performs automatic onboard diagnostics when it senses any error. It ensures there are no electrical accidents, and shuts off the electrical systems if the temperatures reach beyond specified limits. This prevents fire accidents as well as sudden failures.
- Hyderabad-based Pure EV employs AI-driven software to work on lithium-ion batteries of its electric scooters remotely. The company has developed an Artificial Neural Network (ANN)-based algorithm to identify defects/faults in batteries.
AI & Tesla:
EV pioneer Tesla uses AI & machine learning for remote servicing and maintenance. Tesla technicians use the company’s AI system to update software and troubleshoot over-the-air, saving customers a trip to the service centre.
Internet of Things (IoT):
Internet of Things (IoT) is defined as a network of physical objects that are embedded with sensors & software which serve the purpose of exchanging data with a central server or other devices over the internet.
Data is key for operational efficiency. Real-time information is playing an important role in the EV industry and IoT is key to this. The use of IoT enables real-time monitoring of electric vehicles and its principal components.
The technology has application in EV charging infrastructure as well. EV chargers are becoming ‘smart’, connected and accessible for remote support & maintenance. With IoT-powered EV charging station apps, EV drivers can easily search for a nearby charging station and schedule a time slot to recharge.
Conclusion:
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