Estimation of annual electricity cost of electric forklift: Take BYD's 1.8-ton lithium battery model as an example

In the industrial field, electric forklifts have become the mainstream choice of handling equipment due to their environmental protection and low energy consumption. The annual electricity cost is one of the important reference indicators for users when selecting models. We take the 1.8-ton lithium battery forklift as an example to dismantle the calculation logic of the annual electricity cost to help enterprises clearly grasp the core energy consumption expenditure.

Basic parameter setting for measurement

This calculation is based on the industry's conventional operating scenarios, with an annual working day of 250 days and a daily full-load operation time of about 4 hours (including loading, unloading, transshipment and other core links); the unit energy consumption of a 1.8-ton lithium-ion forklift at full load is about 1.5 degrees per hour, which is in line with the average level of similar models; the unit price of industrial electricity is 0.6 yuan/kWh of the domestic regional average, and a small amount of loss during the charging process of lithium batteries is taken into account, resulting in a charging efficiency of about 90%.

Specific measurement steps and results

First calculate the total operating time: 250 days 4 hours/day = 1000 hours; followed by calculating the theoretical power consumption: 1000 hours 1.5 degrees/hour = 1500 degrees; combined with the charging efficiency adjustment, the actual need to invest electricity is about 1500 degrees ÷ 0.91667 degrees; finally calculate the annual electricity cost: 1667 degrees 0.6 yuan/degree 1000 yuan.

The calculation results only cover the core electricity cost of electric forklifts, excluding other expenses such as maintenance, depreciation, and charging equipment investment. The actual cost will fluctuate slightly due to factors such as work intensity, local electricity prices, and charging habits. Enterprises can adjust the measurement parameters according to their actual operating conditions to obtain accurate cost data that better fits their own needs.