Electric vehicles (EVs) are central to the future of automotive development, with high-voltage insulation performance critical for operational safety. Existing insulation detection methods face challenges such as limi...
详细信息
Electric vehicles (EVs) are central to the future of automotive development, with high-voltage insulation performance critical for operational safety. Existing insulation detection methods face challenges such as limited scope, low accuracy, poor interference resistance, and slow response. This study introduces two insulation detection models based on the unbalanced bridge method and low-frequency signal injection, analyzing their theoretical effectiveness and confirming superior detection capability in the unbalanced bridge method. Furthermore, to address feedback voltage waveform issues in this method, an adaptive Levenberg- Marquardt (ALM) algorithm is proposed to prevent the divergence typically seen in traditional approaches. Additionally, a decoupling algorithm utilizing a Third-order variable forgetting factor recursive least squares (TVFF-Decouple) simplifies algorithm complexity significantly while enabling anomaly detection. Finally, AEKF and SRCKF algorithms were used for observation, identifying an optimal combination that reduces noise interference effectively. Simulations and bench tests demonstrate that the proposed methods swiftly and accurately detect positive and negative insulation resistances and equivalent Y capacitance under various conditions.
The development of a secure battery management system (BMS) for electric vehicles depends heavily on the correct assessment of the online state-of-charge (SOC) of Li-ion batteries. The ternary lithium battery is used ...
详细信息
The development of a secure battery management system (BMS) for electric vehicles depends heavily on the correct assessment of the online state-of-charge (SOC) of Li-ion batteries. The ternary lithium battery is used as the research object in this paper, and a second-order RC equivalent circuit model is developed to characterize the dynamic operating characteristics of the battery. In order to solve the problem that the adaptive unscented Kalman filter (AUKF) algorithm is easy to fail SOC estimation because the error covariance matrix is not positively definite due to the incomplete accuracy of the equivalent circuit model, a corresponding solution is proposed. Considering the poor real-time battery SOC estimate caused by the battery model's fixed parameters, therefore we propose the variable forgetting factor recursive least squares (VFFRLS) algorithm for joint estimation of Li-battery SOC and the Singular Value Decomposition-AUKF (SVD-AUKF) algorithm. The SVD-AUKF algorithm can accurately estimate the SOC of the battery when the error covariance is negative. The algorithm can be adaptively adjusted in both the parameter identification and SOC estimation stages, which can effectively solve the problem of poor estimation accuracy caused by fixed parameters. According to experiments, under two separate dynamic operating situations, the joint estimation algorithm's error is less than 2%, and its stability has also been greatly enhanced. At the same time, when the initial SOC value is set incorrectly, the convergence time of the algorithm proposed in this paper can reach within 2.1 seconds for BBDST and DST conditions, which can be well adapted to complex working conditions.
暂无评论