Online prediction for heat generation rate and temperature of lithium-ion battery using multi-step-ahead extended Kalman filtering

•The Bernardi equation is integrated into the multi-step-ahead EKF framework.•The prediction error of heat generation rate decreases by about 40%.•The prediction error of temperature is less than 0.1 K under UDC condition.•The proposed method is insensitive to battery entropy coefficient.•The run time of each prediction with 240 s horizon is just 8 ms.