Abstract:

Lithium-ion batteries, a crucial component of electric vehicles, directly influence vehicles' range, safety performance, and overall operational efficiency. Capacity, a key indicator of battery health, presents challenges in estimation and acquisition under real vehicle conditions. In response, this study introduced a method that integrates ampere-hour integration with the equivalent circuit model, treating capacity as a parameter to be identified through the particle swarm optimization algorithm. Building on this, the study focused on the peculiarities of lithium iron phosphate batteries and proposed a specific optimization approach for slow charging conditions to address issues of poor model voltage fitting during the capacity identification process, which is primarily implemented by deleting the voltage segment at the end of charging and employing a two-dimensional loss function. The method was precision-validated across two electric vehicle models equipped with lithium iron phosphate batteries. Given the absence of direct capacity labels in real vehicle data, the study first calculated capacity based on static charging segments as label values. Due to insufficient label quantities, the nominal capacity under small mileage was also used as a label for accuracy validation. The results show that the mean absolute percentage error for the two vehicle models was 2.33% and 3.38%, respectively. These results demonstrated the method's high accuracy and applicability, offering a new perspective and approach for estimating real-vehicle battery capacity.

Key words: electric vehicles, real vehicle data, capacity estimation, lithium iron phosphate battery