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| State of Charge Estimation Based on Constructing a Reliable Battery Model with Data Missing |
| Mao Ling1, Zhao Jianhui1, Lin Tao1, Zhao Jinbin2, Hu Qin1 |
1. College of Electrical Engineering Shanghai University of Electric Power Shanghai 200090 China;
2. Engineering Research Center of Offshore Wind Technology Ministry of Education Shanghai University of Electric Power Shanghai 200090 China |
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Abstract Reliable measurement data is essential for lithium-ion batteries' state of charge (SOC) estimation. However, data missing and noise interference can affect the reliability of SOC estimation in complex industrial environments where data missing is inevitable. Data missing occurs when the controller does not fully receive sensor measurements, which is caused by sensor failure and communication failure. In the case of electric vehicles, the data undergoes several intermediate steps in testing and storage, and any error in these steps may lead to data being missing. In data transmission, the interference of remote wireless transmission signals may cause data loss. When data is stored in the data center, memory or hardware faults may cause data loss.
This paper simulates data missing due to random sensor faults by introducing random sequences obeying a Bernoulli distribution. Since the voltage and current data come from different sensors in the battery pack, both are missing, but the data is missing due to random voltage and noise. Thus, this paper focuses only on the random missing case of the voltage data.
The effect of random missing data on the SOC estimation of conventional batteries is analyzed. The SOC estimation by the traditional discrimination method (RLS) with the extended Kalman filter (EKF) method under the missing random voltage data makes the SOC value deviate entirely from the reference value, with the maximum error exceeding 20% at 1% missing rate, the maximum output voltage error reaching 5 V, and the ohmic internal resistance and polarization resistance-capacitance under the parameter discrimination enlarged by several times. The traditional method can no longer reflect the real situation of the battery's internal state parameters and SOC.
This paper achieves a more accurate SOC estimation and higher accuracy of the terminal voltage output for unreliable environments by combining relevance vector machine (RVM) and random forest (RF). The extended Kalman filter (EKF) algorithm increases computational complexity and reduces estimation accuracy because its linearization process requires a first-order Taylor expansion and Jacobian matrix. In contrast, untraceable Kalman filtering (UKF) provides high accuracy and adaptability by employing the untraceable transform to generate a series of Sigma points to directly compute the state estimates and error covariance, avoiding the linearization step and the computation of the Jacobian matrix. Therefore, the UKF is used for SOC estimation and noise reduction. Finally, the experimental platform validates the method. Experimental results show that the proposed method maintains the mean absolute error (MAE) within 1% under various working conditions and missing rates.
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Received: 25 September 2024
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2025, Vol. 40 
