Controlled charging of battery electric vehicles is one instrument of smart grids in order to intelligently use the electricity load generated by electric vehicles (EVs). However, battery constraints as well as effects of the charging processes on the battery should not be neglected. This work elaborates an EV charging model, which optimizes the charging process while considering cycle battery aging effects. Formulated as a quadratic constraint program, it minimizes total charging cost, consisting of charging electricity cost and battery aging cost. Cycle battery aging tests are conducted and used to analyze and model the battery aging behavior. The optimization model is applied to a sample of EVs in Singapore and four different scenarios are evaluated. The resulting battery aging cost accounts for a substantial share of the total charging cost, i.e., between 52% and 93%. Therefore, an inclusion of battery aging into the intelligent controlling of EV charging is crucial.

To predict the remaining useful life (RUL), commercially available lithium-ion cells of 63 Ah with nickel manganese cobalt oxide (NMC) cathode material underwent cycle life ageing tests at different current rates (C) and temperature. The key performance parameters such cell capacity, discharge capability and impedance were measured for fresh and aged cells to evaluate the RUL of the cells. The results of ageing tests showed, cell capacity was not influenced by impedance increase slightly regardless of the cycling conditions, rather power fade significant in determining the RUL of the cells. Detailed study of impedance characteristics revealed series resistance (R ser ) and solid electrolyte interface resistance (R sei ) as main contributors to power fade.

The performance of lithium ion battery cells is influenced besides the cell chemistry, also by microscopic and macroscopic parameters. In short format cells, the macroscopic variables such as cell geometry, aspect ratio, material thickness, tab configuration etc. do not significantly affect current, voltage, state of charge (SOC) and temperature distribution. However, they cannot be neglected as the cell size increases, especially in large format cells, where the distributions becomes non homogeneous. These inhomogeneities adversely affect the cell’s performance and a long exposure leads to localized aging. This paper presents a spatially resolved modelling technique in Matlab/Simulink to evaluate cell inhomogeneity. It compares current, voltage and SOC distribution of 8 Ah and 75 Ah cells and how the distribution changes from a fresh cell to its end of life (EOL).

No abstract

No abstract