Although the speed variable charging method can reduce the average charging rate, it does not account for the effect of real-time solar generation and power dispatching. Accordingly, this method does not maximize the use of abundant solar power generated during maximal solar irradiation.
The paper has introduced the speed-variable charging method above. When compared with traditional constant speed charging, the speed-variable method can reduce the average charging speed and the pressure exerted on the battery packs to extend their life cycles thus lowering the total cost of BSS systems. Nevertheless, some problems still exist.
The difference between the speed charging and traditional charging methods is that calculation of Pe (t,n), takes into account the forecast traffic flow E (t,n) while Pe1 (t,n) does not. As a result, if the EV flow is not large enough, the charging rate of Pe (t,n) will be even smaller than Pe1 (t,n).
With the predictions obtained from the Internet in real time and processed by computer, the battery charging station would optimize its charging rate; it should not be too quick (i.e. greater than 2C-rate) to extend battery packs lifecycle; however, it should not be too slow.
Nonlinear programming was used for complete optimization of photovoltaic-battery swapping station operation aiming to simultaneously reduce the daily cost, shift peak grid power to valley, and cut down the wasted photovoltaic power. PSO model predictions were tested against available solar irradiation patterns and variable traffic flows.
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