Liteng Zeng

The actual discharge capacity of a battery is a key indicator for evaluating its discharge performance. Although existing studies have considered the influence of temperature on battery performance, they have mostly focused on aging rates, internal resistance characteristics, or empirical correction terms, rather than establishing an explicit temperature-capacity function for real-time embedded applications. Based on the Arrhenius equation, this paper establishes a model that can accurately predict the available capacity of 18650 lithium batteries under conditions of −20°C to 50°C and current rates of 1/3 C, 1/2 C, 1C, and 2C. The influence of discharge rate and ambient temperature on capacity degradation is also investigated. To obtain the parameters required for the Arrhenius equation capacity prediction model, this paper uses the least squares method for fitting and builds a data acquisition platform based on the STM32F446RCT6 embedded single-chip microcontroller. The experimental results show that within the temperature range of −20°C to 50°C, the maximum relative error occurs at −20°C, reaching 5.4%. The errors at other temperatures are all less than 3.0%, and the error at temperatures above 0°C is as low as 1.1% or less. The improved Coulomb counting method for SOC estimation based on this model reduces the error by 5.6 percentage points compared to the traditional Coulomb counting method. Overall, this paper proposes a battery available capacity prediction method that combines real-time temperature and verifies it using an embedded platform.

In battery management systems, accurate estimation of the State of Charge (SOC) of the battery pack is essential. The Coulomb counting method is a commonly used approach for SOC estimation, but it is susceptible to initial SOC errors and current accumulation errors, leading to estimation drift. This paper establishes a second-order RC battery model, employs recursive least squares for model parameter identification, and implements SOC estimation based on the extended Kalman filter. The proposed EKF-SOC estimation algorithm is deployed on the STM32F446 embedded platform. By leveraging the CMSIS-DSP math library for optimized matrix operations, the measured single estimation time is 34 μs with a CPU utilization of only 0.034%, demonstrating the real-time feasibility of the algorithm on resource-constrained platforms. To address potential numerical divergence issues in single-precision floating-point operations on embedded systems, the Joseph form is adopted for covariance update, together with forced symmetrization and a minimum threshold protection for diagonal entries, effectively ensuring numerical stability. Experiments are conducted under FUDS and DST operating conditions to validate the estimation performance under initial SOC errors and random current biases, respectively. The results show that the EKF-SOC algorithm can effectively correct initial SOC deviations and current accumulation errors, with MAE and RMSE both controlled within 2%. The error covariance updates normally throughout the process without anomalies or divergence, confirming the satisfactory stability of the algorithm.

Accurate state of charge (SOC) estimation is crucial for the safety, reliability, and energy efficiency of lithium-ion battery systems. However, variations in battery parameters and the loss of historical information during the update steps of traditional unscented Kalman filters (UKFs) often lead to decreased estimation accuracy under dynamic operating conditions. To address these issues, this paper proposes a variable forgetting factor recursive least squares (VFFRLS) algorithm combined with a multi-innovation unscented Kalman filter (MIUKF) algorithm. First, a second-order RC equivalent circuit model is established, and the battery parameters are identified online using the VFFRLS method, enabling the model to dynamically adapt to changing operating conditions. Then, multi-innovation theory is incorporated into the standard UKF, extending the single-innovation matrix to a multi-innovation matrix, effectively enhancing the utilization of historical residuals and improving robustness to measurement noise and model uncertainty. Experimental validation under four typical dynamic operating conditions (FUDS, DST, BJDST, and US06) demonstrates that the proposed method significantly improves SOC estimation accuracy. Compared to the traditional UKF, MIUKF reduces MAE and RMSE by 25–30% while maintaining real-time performance, with single-step computation time reaching the microsecond level. Robustness tests under different initial SOC errors further validate MIUKF’s strong robustness to initial biases. In summary, the proposed method provides an effective solution for high-precision SOC estimation of batteries and has the potential for application in battery management systems.

The accuracy of Peukert’s battery capacity equation may decrease under the conditions of variable current and variable temperatures. Some researchers have previously tried to overcome the lack of C-rate change. However, the dependence of battery capacity on temperature is still not included. In this paper, we mainly studied the capacity reduction effect of batteries under variable temperatures. The proposed method can calculate the battery’s available capacity according to the specific discharge conditions. The experimental method proposed in this paper provides a reasonable test method to generate the required coefficients in order to establish a state of charge prediction model with high accuracy. After establishing the method, we can make a real-time prediction of the available energy of battery including the remaining energy of battery. From the result, we can see that the result is of great precision and the method is valuable.

Storage and discharge conditions of primary lithium batteries are studied and the capacity estimation models during the whole life cycle of underwater vehicles is developed based on temperature. The storage experiments for 90 days at different temperatures and discharge experiments at different temperatures and current rates are conducted. At low temperatures, experimental results reveal that there is no significant capacity decay during storage. At high temperatures, the charge storage capacity attenuates with the increase of storage, and the attenuation rate is directly related to the temperature. In discharge experiments, maximum available capacity increases with temperature during 0 °C to 25 °C, whereas the given phenomenon is not significant at other temperatures. Moreover, the current rate is less important for the maximum available capacity. To establish capacity estimation models during the storage stage and working stage of underwater vehicles, we have developed a capacity decay model and a temperature calibration model. Moreover, the model accuracy is evaluated, and the errors in capacity decay model and temperature calibration model are less than 2% and 0.7%, respectively. Capacity estimation models provided in this research are not only accurate, but also relatively simple, which have high value for underwater vehicles in engineering applications.