A Novel Method of Charging Strategy Optimization using Evolutionary Algorithm in Battery Electric Vehicles 2020-01-1186
Electric vehicles (EV’s) are gaining increasing popularity because of fast depleting fossil fuels and increasing environmental hazards caused by vehicles that use fossil fuels as a source of propulsion. One of the key factors in EV development is battery management and charging time convenience. Constant-Current (CC) and Constant-Voltage (CV) are the most common charging strategies used for the charging of batteries. High charging current can cause the temperature of the battery to rise and shorten the battery life. At low State of Charge (SoC) levels, the open circuit voltage (OCV) of the battery is also low and CV strategy may cause high charging current adversely affecting the battery life. Therefore CCCV is the most commonly used charging strategy, in which battery is charged by CC at low SoCs and after the OCV reaches a limit, the CV method is applied for charging. A comparison between conventional CCCV profile charging and optimized CCCV profile charging is done. Three factors considered in the paper for optimization are charging time, battery life and energy losses during charging. A cost function is calculated for all the factors and weights are assigned for parameters. This will give the manufacturer the ability to set the priority for optimization parameters. Evolutionary algorithms (EA) is a general population-based algorithm inspired by the biological evolution of life form in nature. In this paper, EA is applied for finding the solution to the optimization problem in charging of EV battery.