Prior-knowledge-independent equalization to improve battery uniformity with energy efficiency and time efficiency for lithium-ion battery

To improve battery uniformity as well as energy efficiency and time efficiency, a SOC (state of charge)-based equalization by AGA (adaptive genetic algorithm) is proposed on basis of two-stage DC/DC converters. The simulation results indicate that compared with FLC (fuzzy logic controller) equalization, the standard deviation of final SOC is improved by 78.7% while energy efficiency is improved by 6.01% and equalization time is decreased by 20% for AGA equalization of extreme dispersion. Additionally, AGA improves the battery uniformity by 30.77% with shortening equalization time by 16.29% and saving energy loss by 1.51% compared with FLC for equalization of regular dispersion. For further validation, the equalization optimization is verified by experiment based on the data-driven parameter identification method which is used to enhance the real-time capability of AGA. For AGA equalization of extreme dispersion, the standard deviation of final SOC is just 0.41% while equalization time prolongs only 14 min and energy efficiency is decreased by 0.81% compared with simulation results. Moreover, not only the standard deviation of final SOC is just 0.28% but also the energy efficiency is decreased by 0.69% and equalization time prolongs by 10.4 min compared with the simulation results for equalization of regular dispersion. •Issues of over equalization, time consumption and energy loss are addressed.•A SOC-based equalization is proposed based on adaptive genetic algorithm.•The equalization aims to improve battery uniformity, efficiency of energy and time.•Data-driven parameter identification is used to enhance the real-time capability.