Enhancing the estimation accuracy in low state-of-charge area: A novel onboard battery model through surface state of charge determination

A highly accurate predictive-adaptive method for lithium-ion battery remaining discharge energy prediction in electric vehicle applications

“…For the E RDE prediction issue, a high SOC estimation accuracy in the entire SOC range is required. Compared with the traditional ECM, the extended equivalent circuit model (EECM) simplified from the electrochemical single particle model could provide enhanced estimation accuracy especially in low-SOC area [45], and is hence adopted here for real-time SOC estimation through EKF algorithm.…”

“…The test profile was executed 21 times from 100% to 0% SOC with 5% SOC gap. In voltage prediction process, the ECM with one-state RC structure is chosen for low computational complexity, while in the online SOC estimation in Section 5.1, the extended equivalent circuit model (EECM) is applied for high accuracy [45]. As a global optimization method which could find the optimum for complex objective functions effectively, the genetic algorithm (GA) developed by Holland [46] is employed for the ECM model parameterization based on the voltage response at each test point.…”

An All-Region State-of-Charge Estimator Based on Global Particle Swarm Optimization and Improved Extended Kalman Filter for Lithium-Ion Batteries

“…If the fixed model parameters are used for SOC estimation, the model error and SOC error may be very large. Moreover, Reference [33] confirmed that the model error of ECMs in the low SOC range (<20%) is much greater than that in the high SOC range, resulting in a large SOC estimation error based on the traditional EKF in the low SOC range. Therefore, it is necessary to develop an algorithm that can accurately estimate SOC in the low SOC range.…”

A Remaining Discharge Energy Prediction Method for Lithium-Ion Battery Pack Considering SOC and Parameter Inconsistency

“…As can be seen from the above analysis, the key issue of RDE prediction is to establish a model which can predict the battery terminal voltage precisely, and estimate the model parameters and SOC accurately. Significant progress has been made in the study of single cell model parameter identification and the SOC estimation problem in recent years [12][13][14][15][16][17][18][19][20][21][22][23][24][25]. A commonly used method is to establish an ECM [13], which consists of a pure ohmic resistor and several resistor-capacitor (RC) parallel links, to describe the single cell.…”

“…A commonly used method is to establish an ECM [13], which consists of a pure ohmic resistor and several resistor-capacitor (RC) parallel links, to describe the single cell. The parameters of the RC components are identified by experimental data through offline algorithms, such as least square algorithm [14][15][16], genetic algorithm [17][18][19][20], particle swarm optimization algorithm [21], and online identified by the recursive least square algorithm [22,23]. An adaptive filtering algorithm, such as the extended Kalman filter (EKF) [24,25], is then used to estimate the SOC based on the battery cell model and parameter identification results.…”