An improved coulomb counting method based on dual open‐circuit voltage and real‐time evaluation of battery dischargeable capacity considering temperature and battery aging

Abstract

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%.

Operando odd random phase electrochemical impedance spectroscopy as a promising tool for monitoring lithium-ion batteries during fast charging

“…Developing a healthaware fast-charging protocol without compromising the health of batteries is a crucial step to further accelerate the transition of EVs. The state-ofthe-art monitoring approaches are mostly based on conventional parameters i.e., voltage, current and temperature, and assisted by the Coulomb counting method, however it becomes insufficient in more complex usage conditions [53]. Applying the fast-charging in a mismatched SoC region (i.e., low or high SoC region) leads to accelerated aging effects on batteries such as the destabilization of the lattice structure, the release of lattice oxygen, side (electro)chemical reactions and excessive heat generation etc, leading to a rapid degradation of the batteries and safety hazards [54][55][56][57].…”

Considering the temperature influence state‐of‐charge estimation for lithium‐ion batteries based on a back propagation neural network and improved unscented Kalman filtering

“…With this being said, this method has great application prospects 24,25 . However, it needs to meet the premise of ensuring the performance of the state estimation algorithm and the battery model accuracy 26 . Therefore, improving the performance of the algorithm and the model accuracy is the research core of this kind of method 27,28 …”

Estimation of State of Charge and State of Health of Lithium‐Ion Batteries Based on a New Adaptive Nonlinear Observer

“…In the presence of noise, the EKF method outperforms the KF algorithm in terms of precision. On the other hand, due to the Jacobian matrixes, the EKF technique Coulomb counting method [7] Simple, easy implementation, and fast computation Applicable only for open-loop systems, and this method is very sensitive 2 Open-circuit voltage method [7,8] Simple, easy implementation Applicable only for open-loop systems and unsuitable for flat OCV-SOC curves 3 Kalman-filter-based method [9][10][11] An accurate and commonly used method in literature, closed-loop system Improved methods are only suitable for linear systems, and complexity is more 4 Extended Kalman filter and unscented Kalman filter methods [12][13][14][15][16][17][18] More accurate, robust, and applicable to the nonlinear systems Complex algorithm and use of the mass of matrix 5 Support vector machine method [19][20][21] Accurate and robust Sensitive to the training data and the complex controller is required 6…”