Optimizing fault diagnosis for electric vehicle battery systems: Improved Giza pyramids construction and advanced gradient boosting decision trees

This manuscript proposes an online multi-fault diagnostic technique for the series string batteries in electric vehicles to identify and diagnose external/internal short circuits, sensor faults, and connection fault detection. The proposed technique combines the Improved Giza Pyramids Construction and Gradient Boosting Decision Trees. Many variables, including road conditions, electromagnetic interference, and driving habits, may complicate the battery fault system, multi-parameter or non-linear coupling, making it challenging to diagnose faults properly by using a single parameter in the real operation of an electric vehicle. The voltage fluctuation data is collected by simulating a battery discharging and charging investigation in a vibration setting. By deconstructing and rebuilding the discrete wavelet transform, the proposed approach removes voltage signal noise. To categorize the fault state, the voltage change, co-variance and variance matrixes, and voltage characteristics are employed as input-values in the improved Giza pyramids construction, and gradient boosting decision trees technique. The Improved Giza pyramids construction technique is utilized to detect fault signals and determine the degree of the defect. The cell faults are separated from other faults using the Gradient Boosting Decision Trees approach by identifying the surrounding voltages with fault flags. Furthermore, the correlation coefficient of the nearby voltage difference and current isolates connection and voltage sensor defects. The multifault diagnosis approach may prevent erroneous fault detection and offer high resilience to battery inconsistencies and normal measurement errors of state of health, ambient temperature, and state of charge. The projected control theme is evaluated on the MATLAB platform and its performance is assessed. The performances of the proposed method are compared with existing techniques like Artificial Bee Colony, Differential Evolution, Improved Giza Pyramids Construction and Particle Swarm Optimization. The efficiency of the Artificial Bee Colony, Differential Evolution, Particle Swarm Optimization, and Improved Giza Pyramids Construction and proposed technique is 82.237 %, 79.265 %, 87.1029 %, 89.023 % and 95.4501 %.