Hello people. This article is about artificial intelligence in electric vehicles. Choosing an EV can help reduce harmful air pollution from exhaust emissions. An EV has zero exhaust emissions. Better for the environment.
Range anxiety and charging time are a cause for concern for electric vehicle users. Battery technology is benefiting from the power of artificial intelligence through predictive analytics and data intelligence.
Data science, AI, and big data tools are used to improve the performance of battery packs. The predictive maintenance facilitates high battery efficiency and operational reliability.
Machine Learning helps to explore the opportunity of battery life cycle management. The key to improving battery life lies in the data. By blending advanced electronics with IoT, data science and digital twin, Machine Learning uses the power of predictive intelligence to predict battery life, identify potential breakdown and their causes, fix errors even before they arise. All this data can be stored in the cloud.
Ford is planning to use Google’s AI, data science and analytical capabilities to improve customer experiences. It also uses AI to accelerate the modernisation of product development, manufacturing and supply chain management, and fast track the implementation of data-driven business models.
Tesla uses machine learning and artificial intelligence for their over-the-air fix. The engineers could use the AI system to update the software over cloud, saving users a trip to the service center.
PURE EV, an IIT Hyderabad-incubated startup, has come up with an artificial intelligence-driven hardware to repair lithium-ion batteries of electric vehicles automatically. The researchers have developed an Artificial Neural Network (ANN)-based algorithm called ‘BaTRics Faraday’ to identify defects in batteries.
A study, published by Nature on February 19, 2020, a collaboration among scientists from Stanford, MIT and the Toyota Research Institute had a goal of finding the best method for charging an EV battery in 10 minutes that maximizes the battery’s overall lifetime.
The researchers wrote a program that, based on only a few charging cycles, predicted how batteries would respond to different charging approaches. The software also decided in real time what charging approaches to focus on or ignore. By reducing both the length and number of trials, the researchers cut the testing process from almost two years to 16 days.
During their research, machine learning reduced the number of methods they had to test. Instead of testing every possible charging method equally, or relying on intuition, the computer learned from its experiences to quickly find the best protocols to test.
By testing fewer methods for fewer cycles, the researchers quickly found an optimal ultra-fast-charging protocol for their battery. In addition to dramatically speeding up the testing process, the computer’s solution was also better.
Hope this article on Artificial intelligence in electric vehicles is useful to you. To read about jobs in the Electric Vehicle Industry, please visit Job opportunities in Electric Vehicle Sector and its Charging Infrastructure industry