Range prediction algorithms for EV's currently may have significant inaccuracies, including 10-20%+ errors due to environmental conditions such as precipitation or wind.. One enabler to reducing this error is through connected data. An effective approach is to utilize fleet data and its historical information to improve prediction accuracy. This can be synergized with other predictions that use lookahead horizons.