Electra Vehicles’ AI Software Demonstrates 2x Accuracy of EV Driving Range Estimates

Electra more accurately predicted battery state-of-charge (SoC) in battery pack using embedded and cloud-connected Adaptive Cell Monitoring System

Electra’s solution outperformed traditional EV SoC estimation method

Electra Vehicles a leading provider of predictive battery management and battery design software announced the results of a demonstration to showcase accuracy improvements to electric vehicle driving range estimations. Electra’s core technology – EVE-Ai Adaptive Cell Modeling System – outperformed the industry standard for estimating battery charge, resulting in 2x reduction in estimation error.

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“With Electra’s EVE-Ai software, the vehicle’s battery management system is constantly retrained to showcase the most accurate battery metrics, alleviating range anxiety and battery w******* concerns for EV customers.”

Electra partnered with a semiconductor provider to construct a battery pack that was capable of delivering real-time battery cell data from the pack to Electra’s cloud-based EVE-Ai software through a battery management system and IoT gateway hardware. Using this setup, Electra showcased that its integrated software solution could retrain the battery management system using artificial intelligence and machine learning to predict a battery’s state-of-charge more accurately than the industry standard method, known as Extended Kalman Filtering (EKF).

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“By showcasing significant improvements in predicting a battery’s state-of-charge, Electra has demonstrated how using artificial intelligence in battery management can translate to longer lasting and better performing batteries,” said Fabrizio Martini, Electra CEO and Co-Founder. “With Electra’s EVE-Ai software, the vehicle’s battery management system is constantly retrained to showcase the most accurate battery metrics, alleviating range anxiety and battery w******* concerns for EV customers.”

The test battery pack was repeatedly charged and discharged over a 12-week period in order to quickly age the pack to roughly half of its w******* for electric vehicle usage. Throughout the testing, Electra compared three sets of results – estimates from Electra’s EVE-Ai Adaptive Cell Modeling System, estimates from the industry standard EKF and the reference values from an electrochemical reference data set.

The results showed that Electra’s solution better predicted the battery’s state of charge at the beginning of life, but more importantly, as the battery reached half-life, which is where Electra’s accuracy improved significantly over EKF.

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