A BI-DIRECTIONAL ACTIVE CELL BALANCING OPTIMIZATION BASED ON STATE-OF-CHARGE ESTIMATION

Abstract

Recently, Electric Vehicles (EVs) have received extensive consideration since they offer a more sustainable and greener transportation alternative compared to fossil-fuel propelled vehicles. Lithium-ion batteries are increasingly being considered in EVs due to their high energy density, slow loss of charge when not in use, and for lack of hysteresis effect. Conventionally, the batteries are connected in series to achieve the load voltage requirements. However, for the batteries with intrinsic discrepancies or different initial states, cell balancing is a concern because it is the weakest cell that determines the empty point for the battery and an undercharged series cell will shorten the lifetime of the entire pack. The imbalance potential of the battery behaves as the way of State-of-Charge (SOC) mismatch and it’s also temperature dependent. Therefore, in this thesis, an active cell balancing optimization was proposed and conducted in MATLAB to optimize battery unused capacity and thermal effect simultaneously based on bi-directional balancing system and pre-estimated SOC. The bi-directional balancing system was physically built based on “Fly-back” converter to compare balancing performance in discharging, idle, and plug-in charging mode. Moreover, a battery combined model worked collaboratively with robust state and parameter estimation strategies, namely Extended Kalman Filter (EKF) and Smooth Variable Structure Filter (SVSF) in order to estimate SOC for cell balancing. As a result, the proposed method can effectively optimize SOC mismatch around 2.5%. Meanwhile, more uniform temperature was achieved and the maximum temperature can be reduced about 7 ℃.