An adaptive working state iterative calculation method of the power battery by using the improved Kalman filtering algorithm and considering the relaxation effect.

The battery modeling and iterative state calculation in the battery management system is very important for the high-power lithium-ion battery packs, the accuracy of which affects its working performance and safety. An adaptive improved unscented Kalman filtering algorithm is developed to realize the iterative calculation process, aiming to overcome the rounding error in the numerical calculation treatment when it is used to estimate the nonlinear state value of the battery pack. As the sigma point is sampled in the unscented transform round from the unscented Kalman filter algorithm, an imaginary number appears that results in the working state estimation failure. In order to solve this problem, the decomposition is combined with the calculation process. Meanwhile, an adaptive noise covariance matching method is implied. Experiments show that the proposed method can guarantee the semi-positive and numerical stability of the state covariance, and the estimation accuracy can reach the third-order precision. The estimation error remains 1.60% under the drastic voltage and current change conditions, which can reduce the estimation error by 1.00% compared with the traditional method. It can provide a theoretical safety protection basis of the energy management for the lithium-ion battery pack

Estimation of state of charge of lead-acid battery used in solar photovoltaic system

7-19An accurate estimation of State of charge (SOC) of the lead-acid battery is of paramount importance for the efficient and reliable operation of solar photovoltaic (SPV) sytem. There are mainly four methods used for estimating SOC of the battery, viz. chemical, voltage, current integration and kalman filtering. In this present study, the SOC as indicated by the solar power conditioning unit (SPCU) was taken as reference and at every 5% SOC reduction, the other parameters such as- i) specific gravity of electrolyte, ii) battery terminal voltage, iii) (Ampere Hour) Ah and iv) energy deliverd to the resistive load were recorded. Based on this recorded values the SOC was predicted. The standard deviation (S.D.) of the difference of predicted SOC to the reference SOC was calculated based on specific gravity, Voltage, Ah and energy. The SD obtained was 6.17, 5.67, 0.33, 0.75 respectively. The specific gravity value for the battery electrolyte decreases with the decrease in the battery SOC%, the maximum value of SG at 100% SOC was 1.23 and the minimum at 20% SOC was 1.14. The terminal voltage was also got reduced with the reduction in SOC, from 24.85V at 100% SOC to 22.4V at 20% SOC. The energy stored by the battery during charging was 3.65 units and the energy delivered from the battery to the load was 3.245 units.  The efficiency of solar panel, lead-acid battery and the combined SPV system was 12.79%, 88.9% and 9.68% respectively. It was found that the SOC of the lead-acid battery would be more accurate when it is estimated based on current integration i.e., Ah, the SOC estimation based on energy is also acceptable since the SD for both is less than 1. Hence, through this investigation we can say that SOC prediction based on Ah or kWh measurement is more appropriate than specific gravity and voltage methods

Estimation of State of Charge for Lithium-Ion Battery Based on Finite Difference Extended Kalman Filter

An accurate estimation of the state of charge (SOC) of the battery is of great significance for safe and efficient energy utilization of electric vehicles. Given the nonlinear dynamic system of the lithium-ion battery, the parameters of the second-order RC equivalent circuit model were calibrated and optimized using a nonlinear least squares algorithm in the Simulink parameter estimation toolbox. A comparison was made between this finite difference extended Kalman filter (FDEKF) and the standard extended Kalman filter in the SOC estimation. The results show that the model can essentially predict the dynamic voltage behavior of the lithium-ion battery, and the FDEKF algorithm can maintain good accuracy in the estimation process and has strong robustness against modeling error