High-power charging strategy within key SOC ranges based on heat generation of lithium-ion traction battery

A high-power charging strategy is proposed, which considers charging time and current as constraints, and minimizes heat generation as the optimization objective. Due to the minimal fluctuation of the internal resistance measured by the Hybrid Pulse Power Characteristic (HPPC) method in the range of 20 % SOC to 80 % SOC, it is selected as the optimal range for high-power charging. Within this key SOC range, six stages were divided at equal intervals of 10%SOC. The temperature rise at charging rates of 1.0C to 3.0C is measured to form a threedimensional matrix of SOC, charging rate, and temperature, and the random value iteration method is used to find the optimal solution. The characteristic of this strategy is to segment and combine different high powers, and match Stage1 with higher charging power during the initial stage of low battery temperature, thereby effectively controlling the maximum temperature rise of the battery. In an experiment conducted at an ambient temperature of 25 degrees C, it was found that the performance was improved by using an optimized charging strategy compared to a constant current constant voltage (CC-CV) charging rate of 1.8C.The maximum temperature decreased from 48.7 degrees C to 45.3 degrees C, and the charging time reduced from 1196 s to 1181 s. The charging capacities were 2749.6 mAh and 2732.4 mAh, with a slight increase in loss of 0.63 %. The heat generation decreased from 1413.1 J to 1662.9 J, a relative reduction of 15.02 %. In addition, the experiments verified that the charging strategy was also suitable for ternary lithium batteries and lithium iron phosphate batteries.

Automated summary

A high-power charging strategy is proposed, which considers charging time and current as constraints, and minimizes heat generation as the optimization objective. Due to the minimal fluctuation of the internal resistance in a specific SOC range, the optimal range for high-power charging is selected. Six stages are divided at equal intervals within this SOC range, and a three-dimensional matrix is formed to find the optimal solution using the random value iteration method. The strategy segments and combines different high powers and effectively controls the maximum temperature rise of the battery.