State-of-charge estimators considering temperature effect, hysteresis potential, and thermal evolution for LiFePO₄ batteries

Abstract

Summary To achieve accurate state‐of‐charge (SoC) estimation for LiFePO4 batteries, the effects of temperature, hysteresis, and thermal evolution are elaborately modeled. Open‐circuit voltage is regarded as the sum of electromotive force and hysteresis potential (Vh), where electromotive force is constructed as the function of SoC and temperature and Vh is reproduced with a geometrical model. By simulating battery heat generation and dissipation, a thermal evolution model is established and exploited for open‐circuit voltage and parameter identification. Then, on the basis of a second‐order equivalent circuit model, 2 SoC estimation schemes are proposed: One scheme uses the recursive least square with forgetting factor algorithm and off‐line equivalent circuit model parameters derived by the differential evolution algorithm; the other scheme resorts to the adaptive extended Kalman filter (EKF) and online tuned parameters. Experiments validate the effectiveness of the hysteresis model and the thermal evolution model. In contrast to a joint EKF estimator, experimental results under different temperatures and initial states suggest that both the proposed estimators are superior to the joint EKF estimator. Benefiting from the online updated parameters, the adaptive EKF estimator behaves best for giving consistent SoC‐tracking performance under different conditions.

Modeling and comparative analysis of a lithium‐ion hybrid capacitor under different temperature conditions

“…In addition, there is an obvious voltage gap between the two voltage curves. The average voltage difference between the two curves is about 53 mV, indicating an obvious hysteretic characteristic similar to that of a LIB . Because the lithium iron phosphate material is used in the LIHC, a Faraday electrochemical reaction occurs at the positive electrode.…”

Data‐driven lithium‐ion battery states estimation using neural networks and particle filtering

“…4,5 The accurate SOC and SOH estimation for lithium-ion batteries-based ESSs not only keeps the battery from overcharging and overdischarging but also provides the cell property alteration over its whole service life. Model-based method is another widely utilized approach, in which the Kalman filtering family, including Kalman filter (KF), 12 the extended Kalman filter (EKF), 13 and unscented Kalman filter (UKF), 14,15 has drawn much more attention to be applied to batteries SOC estimation. Simultaneously, irreversible physical and chemical alterations occur during its whole lifespan, which will lead to capacity decrease and resistance increase.…”

Optimal charging strategy design for lithium‐ion batteries considering minimization of temperature rise and energy loss

“…Intelligent charging strategies are favorable as they are capable of increasing charging speed while controlling the temperature rise. Under such strategies, batteries are charged in a short period until the terminal voltage attains a preset threshold, and then the charging current is adapted dynamically according to the variation of battery SOC and SOH . These kinds of strategies only aim at increasing charging speed and do not consider the charging energy loss.…”