Optimal design and control of battery-ultracapacitor hybrid energy storage system for BEV operating at extreme temperatures

The battery energy storage system (BESS) is a critical and the costliest powertrain component for battery electric vehicles (BEVs). Extreme operating temperatures distort the battery's electrochemical reactions, causing permanent capacity loss, shortening operational life, and increasing lifecycle costs (LCC). In this work, new methods for optimizing battery and ultracapacitor (UC) hybrid energy storage system (HESS) design and the HESS' energy management strategy (EMS) and thermal management strategies (TMS) are introduced. In addition to altering the batteries' use pattern to extend operational life, this combination also improves battery performance and reduces the impacts on the batteries' operational life under low temperatures. First, performance and degradation data of commonly used lithium-ion (Li-ion) batteries under extreme temperatures and various use patterns are collected to form advanced battery performance, degradation and thermal models to facilitate the HESS's optimal design and energy management for a BEV under different operation conditions. Effective EMS and TMS are introduced to play the batteries and UCs to their strength and to use energy from the UCs to improve the batteries' operating temperature to extend battery life and minimize the BEV's LCC. The optimal sizing of the batteries and UCs and the HESS baseline optimal EMS are simultaneously generated using empirical data-based battery performance and degradation models and the BEV's operation cycle through global design optimization and dynamic programming (DP)-based optimal energy management. The accurate prediction of vehicle propulsion power is made through the extended Kalman filter (EKF) using both statistical and real-time data. The batteries' performance and degradation models are dynamically updated using the online battery voltage data and continuously calibrated state-of-health (SOH) model. These updates enable precise optimal control and EMS through model predictive control (MPC). The optimized battery-UC HESS design and dedicated optimal EMS and TMS extended BEV battery life by 47 %. In addition, the newly introduced real-time control reduced battery degradation by up to 23 %. • Optimal design and control of battery-UC HESS to extend performance and life of batteries under harsh operation conditions • Li-ion battery performance, thermal and degradation models under extreme temperatures and different operation conditions • A method for integrated HESS global optimal design and baseline optimal EMS generation • Real-time optimal control of BEV's HESS based on battery performance and degradation models and instant operation data • Optimal battery-UC HESS design and optimal EMS/TMS extended BEV battery life by 47%, and reduced degradation up to 23%