No abstract

Accurate prediction of the state of health (SOH) of Li-ion battery has an important role in the estimation of battery state of charge (SOC), which can not only improve the efficiency of battery usage but also ensure its safety performance.The battery capacity will decrease with the increase of charge and discharge times, while the internal resistance will become larger, which will affect battery management. The capacity attenuation characteristics of Li-ion batteries are analyzed by aging experiment. Based on the equivalent circuit model and online parameter identification, a novel adaptive dual extended Kalman filter algorithm is proposed to consider the influence of the battery SOH on the estimation of the battery SOC, and the SOC and SOH of the Li-ion battery are estimated collaboratively. The feasibility and accuracy of the model and algorithm

The research of the real-time state of charge (SOC) estimation method for lithium-ion battery is developing towards the trend of model diversification and algorithm complexity. However, due to the limitation of computing ability in the actual battery management system, the traditional ampere-hour (Ah) method is still widely used. First, temperature, charge-discharge current, and battery aging are considered as the main factors, which affect the estimation accuracy of the Ah method under the condition that detection accuracy of the current sensor is determined. Second, the relationship between the SOC and battery open-circuit voltage at different temperatures is analyzed, which is used to modify the initial SOC. Third, the influence mechanism of main factors on the effect of the Ah method is analyzed, and proposes a capacity composite correction factor to reflect the influence of charge-discharge efficiency, coulomb efficiency, and battery aging comprehensively, and then update its value in real-time. Lastly, the adaptive improved Ah formula and the complete SOC estimation model is designed, and the estimation effect of this model is verified by comparing with other SOC estimation methods in the experiment of dynamic cycle test. The results show that the estimation error of the adaptive improved method is less than 2% under two comprehensive working conditions, while the error of the traditional method is 5% to 10%, and compared with an extended kalman filter algorithm, it also gets a better SOC estimation performance, which proves that this method is scientific and effective.

Summary Coulomb Counting (CC) method plays an important role in the state of charge (SOC) estimation theory of lithium‐ion batteries, and a lot of improvement and optimization strategies are based on it. With the increasing demand for precise management of lithium‐ion battery systems, the performance of the traditional CC method is no longer suitable for more complex working conditions. First, the battery aging, extreme temperature, and high‐rate discharging were considered as the main influencing factors which limit the SOC estimation accuracy of the CC method, and the performance degradation mechanism of the traditional CC method under the influence of the above factors are experimentally analyzed, especially the change of battery total dischargeable capacity after aging, and the change rules of key parameters in the CC equation are analyzed. Then the initial SOC and the total dischargeable capacity of the CC method are modified and estimated respectively to realize the accurate estimation of SOC under complex working conditions, especially the accurate SOC estimation during the whole life cycle of lithium‐ion batteries. The experimental results show that the improved CC method can effectively deal with complex working conditions, and the comprehensive estimation accuracy of SOC is within 3.6%.

The state of charge (SOC) estimation of lithium-ion battery is a crucial portion of the battery management system (BMS). The high-precision estimation is the foundation of BMS safety and efficiency. To that extent, a fractional-order algorithm with time-varying parameters model is proposed to ensure the accuracy of the SOC. Since the battery state changes slowly and is related to the state in the past, this study proposes a memory factor M containing the battery state in the past to estimate the SOC. Moreover, by comparing the experimental results of different orders, the most appropriate fractional order is determined. In order to eliminate the influence of noises introduced into historical data processing, an adaptive noise factor is added to the algorithm. The experimental results confirm that the maximum error of the adaptive fractional-order extended Kalman (AFEKF) estimation is less than 2%, which indicates that the estimation method provides a higher accuracy than the extended Kalman filter.