Skip to main content

GE, Ford, University of Michigan working to extend EV battery life

GE researchers, in partnership with Ford Motor Company and the University of Michigan, are working together to develop a smart, miniaturised sensing system that has the potential to significantly extend the life of car batteries over conventional battery systems used in electric vehicles today.
August 6, 2012 Read time: 2 mins

940 GE researchers, in partnership with 278 Ford Motor Company and the University of Michigan, are working together to develop a smart, miniaturised sensing system that has the potential to significantly extend the life of car batteries over conventional battery systems used in electric vehicles today.

“The car battery remains the greatest barrier and most promising opportunity to bringing EVs mainstream.” said Aaron Knobloch, principal investigator and mechanical engineer at GE Global Research. “Improvements in the range, cost and life of the battery will all be needed for EVs to be competitive. With better sensors and new battery analytics, we think we can make substantial progress at increasing battery life. This, in turn, could help bring down its overall cost and the cost entitlement of buying an electric car.”

To improve the life and reduce the lifecycle cost of EV batteries, GE will combine a novel ultrathin battery sensor system with sophisticated modelling of cell behaviour to control and optimise battery management systems. Today’s sensors on EVs and plug-in hybrid vehicles (PHEVs) measure the health of the battery by looking at factors such as its temperature, voltage, and current. However, these measurements provide a limited understanding of a battery’s operation and health. The goal of the ARPA-E project will be to develop small, cost effective sensors with new measurement capabilities. Due to their small size, these sensors will be placed in areas of the battery where existing sensor technologies cannot be currently located. The combination of small size and ability to measure new quantities will enable a much better understanding of battery performance and life.

A group of scientists from the 5594 University of Michigan, led by Anna Stefanopoulou, a professor of mechanical engineering, will use the data generated by GE sensors to verify advanced battery models. They will ultimately create schemes that use instantaneous sensor data to predict future battery-cell and battery-pack behaviour.

The use of sensors in conjunction with real-time models will enable novel algorithms that optimise how the battery system is managed to extend its life. To demonstrate the capabilities of the sensor system and analytics, Ford will integrate them into one of their vehicles for validation.

For more information on companies in this article

Related Content

  • CCAM innovation at ITS World Congress 2021
    September 27, 2021
    We live in an era of increasingly cooperative, connected and automated mobility (CCAM) but there’s still a huge way to go - visitors to ITS World Congress in Hamburg will be able to see projects, innovations and real-life solutions showcased in the city
  • Cut freight deliveries – improve Southampton’s air quality
    November 23, 2018
    Taking the pressure off cities’ road networks can have a beneficial effect on the environment. David Crawford looks at a new economic model which seeks to quantify the societal effect of freight traffic in Southampton, one of the UK’s five most polluted cities Cuts of 60% or more in volumes of freight deliveries are being predicted - along with badly-needed improvements in air quality - from a load consolidation scheme currently being introduced in the UK port city of Southampton. The forecasts are based o
  • Want intelligent transit? Then share data
    March 2, 2022
    How will the US deploy intelligent transit networks that enable connected vehicles? Data sharing is crucial if urban mobility users are to benefit, explains Timothy Menard of Lyt
  • Simulating the effects of optimal mobility
    May 30, 2024
    Simulation-based optimisation is the foundation for real-time predictive analytics when it comes to optimal traffic signal programming, explain Sunny Chakravarty of Econolite and Lorenzo Meschini of PTV Group