This paper presents a daisy-chain integrated Battery Management System (BMS) architecture, along with a combined State of Charge (SOC) and State of Power (SOP) co-estimation algorithm for a Formula Student Electric Car’s accumulator. The proposed design employs NXP’s S32K series microcontroller as the primary control unit and NXP’s MC33771C as the analog front-end (AFE) chip. Key features of the BMS include real-time monitoring of battery voltage and current, precise SOC and SOP estimation, cell voltage balancing, charge/discharge and pre-charge management, thermal regulation, and insulation detection. The BMS software development process follows a Model-Based Development (MBD) methodology, encompassing system modeling, analysis, validation, and the automated generation of code, test cases, and documentation. Accurate monitoring of SOC and SOP is essential for both optimal performance and safety. To address the challenges of estimating SOC and SOP under highly dynamic conditions, a second-order RC equivalent circuit model with parameter identification was established. An Adaptive Extended Kalman Filter (AEKF) algorithm is employed to dynamically adjust the process noise covariance, thereby enhancing the accuracy of SOC estimation. Building on SOC estimation, the system performs joint SOP estimation under multiple constraints, including temperature, terminal voltage, and vehicle state. This approach enables dynamic, automatic regulation of the battery’s maximum output power in accordance with racing conditions and safety requirements. In parallel, hardware-in-the-loop (HIL) testing verifies the accuracy and robustness of the implemented algorithms. HIL test results indicate that the proposed system achieves an SOC estimation error of less than 2.5% across a range of driving scenarios, demonstrates robust performance under rigorous racing conditions, and effectively balances maximum power utilization with stringent safety requirements.