High precision SOC estimation of LiFePO4 blade batteries using improved OCV-based PNGV model - Tao, Zhen (Nanyang Technological University, 2023)
It holds significant importance for an electric vehicle (EV) to possess the capability of making real-time assessments regarding its remaining driving range, which is contingent on the state of charge (SOC) of its battery. In contemporary discussions, SOC estimation has grown progressively intricate but holds tremendous relevance. The precise estimation of SOC plays a pivotal role in mitigating range anxiety, a pressing concern for potential EV buyers. This issue is of utmost importance in facilitating the widespread adoption of EVs, in line with global green energy initiatives. Currently, there are numerous approaches available for SOC estimation, underscoring the multifaceted nature of this challenge. Nevertheless, it is evident that there is no universally applicable solution that can address the wide variety of batteries available in the market. This dissertation engages in a comprehensive evaluation of three primary SOC estimation methodologies and seeks to develop a customized equivalent circuit model tailored specifically to BYD's Blade battery technology, which is prominently utilized in the Dynasty series of BYD EVs under the PNGV (Partnership for a New Generation of Vehicle) initiative. The research outcomes demonstrate an exceptionally high level of accuracy in SOC estimation, achieving an impressive accuracy rate of 99.15%.