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
BOSTON & TURIN, Italy–(BUSINESS WIRE)–Electra Vehicles, Inc., a leading provider of predictive battery management and battery design software, today 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.
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).
“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 warranty 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 warranty 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.
To learn more about the demonstration methodology, please watch an overview via Electra’s Vimeo. To learn more about the demonstration results, please download the case study here or reach out to an Electra sales representative via email@example.com
About Electra Vehicles, Inc.
Electra Vehicles is on a mission to maximize the full potential of battery power to enable electric mobility to take us further.
Electra is a leading provider of predictive battery management and battery design software that combines adaptive electrochemical battery modeling with advanced artificial intelligence and machine learning algorithms to more accurately predict battery performance, health and failures. Electra’s software solutions are both cloud-based and ‘hybrid’ – embedded in the battery management system (BMS) with cloud connectivity – and enable battery developers, battery integrators and fleet managers the ability to more accurately estimate battery state-of-charge (SoC), state-of-health (SoH), remaining useful life (RUL) and fault risk to improve the lifetime and reliability of batteries.
Learn more at: https://www.electravehicles.com