Holistic Management of Energy Storage System for Electric Vehicles

While electric vehicles (EVs) have recently gained popularity owing to their economic and environmental benefits, they have not yet dominated conventional vehicles in the market. This is due mainly to their short driving range and/or quick battery performance degradation. One way to reduce these shortcomings is to optimize the driving range and the degradation rate with a more efficient battery management system (BMS).
This dissertation explores how a more efficient BMS can extend EVs driving range during their warranty periods. Without modifying the battery capacity, the driving range and the degradation rate can be optimized by adaptively regulating main operational conditions: battery ambient temperature (T), the amount of transferred battery energy, discharge/charge current (I), and the range of operating voltage (min/max V). To this end, we build a real-time adaptive BMS from a cyber-physical system (CPS) perspective. The updated system calculates target operation conditions (T, I, min/max V) based on: (a) a battery performance model that investigates the effects of operational conditions on the degradation rate and the driving range; (b) a real-time battery power predictor; and (c) a temperature and discharge/charge current scheduler to determine target battery operation conditions that guarantee warranty period and maximize the driving range. Physical components of the CPS actuate battery control knobs to achieve the target operational conditions scheduled by the batteries cyber components of CPS. There are two sub-components for each condition (T, I): (d) a battery thermal management system and (e) a battery discharge/charge current management system that consist of algorithms and hardware platforms for each sub-system.
This dissertation demonstrates that a more efficient real-time BMS can provide EVs with necessary energy for period of time while slowing down performance degradation. Our proposed BMS adjusts temperature and discharge/charge current in real time, considering battery power requirements and behavior patterns, so as to maximize the battery performance for all battery types and drivers. It offers valuable insight into both current and future energy storage systems, providing more adaptability and practicality for various mobile applications such as unmanned aerial vehicles (UAV) and cellular phones with new types of energy storages.