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Experimental results show that the proposed optimal charging strategy is capable of offering desirable effective optimal charging current profiles and a proper trade-off among the conflicting objectives.
Further, the proposed optimal charging strategy can be easily extended to other battery types.
Optimal charging conditions were established by cyclic voltammetry measurements and battery cycling tests.
In this paper an offline optimal charging strategy is developed to minimize the energy cost.
It is shown that the vehicle's driving range has a great influence on the optimal charging station locations.
Detailed simulations and case studies demonstrate the performance of the proposed algorithms to find optimal charging strategies.
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Additionally, for the second and third formulations, namely, SoC and temperature constraints, by using a qualitative aging approach, some enhancements in the battery health is observed when compared to the non-optimal charging strategy.
In Section 6, it concludes that, unregulated EV charging deteriorates the operation condition, whereas optimal charging/discharging of EV induces many benefits and reduces the operation burdens.
The optimal charging/discharging power in each time and SOC curve of BESS in centralized configuration mode are shown in Fig. 7 (power greater than 0 represents charging; otherwise, discharging) to observe the performance of the proposed operation strategy.
In day-ahead scheduling, with the estimated power generation and load demand, the optimal charging/discharging scheduling of EVs during 24 h is achieved by serial quadratic programming.
Under the optimal charging/discharging strategy of EVs, load curve including the charging/discharging of EVs can track the generation curve well not only at night but also during the peak load period.
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