Online Internal Temperature Estimation for Lithium-Ion Batteries Based on Kalman Filter

Abstract

Abstract:The battery internal temperature estimation is important for the thermal safety in applications, because the internal temperature is hard to measure directly. In this work, an online internal temperature estimation method based on a simplified thermal model using a Kalman filter is proposed. As an improvement, the influences of entropy change and overpotential on heat generation are analyzed quantitatively. The model parameters are identified through a current pulse test. The charge/discharge experiments under different current rates are carried out on the same battery to verify the estimation results. The internal and surface temperatures are measured with thermocouples for result validation and model construction. The accuracy of the estimated result is validated with a maximum estimation error of around 1 K.

Development of an Electro-Thermal Model for Electric Vehicles Using a Design of Experiments Approach

“…First, examining the temperature errors from the proposed approach (CCD), the highest error is observed for the US-06 drive cycle at a temperature of 10 • C. This error value is consistent with electro-thermal models proposed by Damay et al [19], Lin et al [17] and Sun et al [30], where maximum RMSE values around 1 K were observed. Therefore, the results from this table further confirm the fact that the electro-thermal model can adequately predict the temperature rise in the battery.…”

Determination of the Optimum Heat Transfer Coefficient and Temperature Rise Analysis for a Lithium-Ion Battery under the Conditions of Harbin City Bus Driving Cycles

“…The heat transfer coefficient of the test battery was obtained by fitting the curve of h versus temperature after the discharge and was found to be 20 W/m 2 K. Figure 8 shows the comparison between the experimental and simulation results of constant current discharge with different rates at ambient temperature (25 • C). Figure 8 shows that the experiment Energies 2017, 10, 1723 9 of 17 and the simulation were in good agreement, and the maximum error between the simulation and the experimental results was 0.88 • C. In addition, the temperature error was less than 1 • C, which indicates that the model can accurately predict temperature [21]. In order to verify the accuracy of the simulation model in the actual working conditions, the driving cycle experiment was tested at the same ambient temperature, and the output current of the cell used in regular lines was inputted in the model to simulate a battery that operates under the conditions of the regular lines cycles.…”

“…Figure 8 shows that the experiment and the simulation were in good agreement, and the maximum error between the simulation and the experimental results was 0.88 °C. In addition, the temperature error was less than 1 °C, which indicates that the model can accurately predict temperature [21]. In order to verify the accuracy of the simulation model in the actual working conditions, the driving cycle experiment was tested at the same ambient temperature, and the output current of the cell used in regular lines was inputted in the model to simulate a battery that operates under the conditions of the regular lines cycles.…”

“…The simulated battery can be regarded as a homogeneous heating object, because it is small in size [19]. The total heat generation rate of the battery (q) can be estimated by Equation (12) [21].…”

Dual EKF-Based State and Parameter Estimator for a LiFePO4 Battery Cell

“…5 can be adapted with respect to relatively slow temperature variations (which are emphasized for resistance-related parameters R b and R p [13], [44]), and even more gradual variations related to battery cycle and calendar life (inevitably affecting all of the parameters of the battery electric circuit [34], [46], [47]). Hence, utilizing battery temperature measurement or estimation [45] and collecting the test results of battery aging [34], [46] may be avoided for parameter map correction. Naturally, precise information about the discharged charge Q b should also be available for online updates of the parameters of the internal model map, e.g., by using a precise current sensor and charge counting approach (Equ.…”