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The battery power state (SOP) is the basic indicator for the Battery management system (BMS) of the battery energy storage system (BESS) to formulate control strategies. Although there have been many studies on state estimation of lithium-ion batteries (LIBs), aging and temperature variation are seldom considered in peak power prediction during the whole life of the battery. To fill this gap, this paper aims to propose an adaptive peak power prediction method for power lithium-ion batteries considering temperature and aging is proposed. First, the Thevenin equivalent circuit model is used to jointly estimate the state of charge (SOC) and SOP of the lithium-ion power battery, and the variable forgetting factor recursive least squares (VFF-RLS) algorithm and extended Kalman filter (EKF) are utilized to identify the battery parameters online. Then, multiple constraint parameters including current, voltage, and SOC were derived, considering the dependence of the polarization resistance of the battery on the battery current. Finally, the verification experiment was carried out with LiFePO4 battery. The experimental results under FUDS operating conditions show that the maximum SOC estimation error is 1.94%. And the power prediction errors at 20%, 50%, and 70% SOC were 5.0%, 8.1% and 4.5%, respectively. Our further work will focus on the joint estimation of battery state to further improve the accuracy. [ABSTRACT FROM AUTHOR]

State of Charge (SOC) is a key parameter for battery management and vehicle energy management. Recently used SOC estimation methods for lithium-ion battery for vehicles have problems of too simple a base model for the battery and large sampling noise in both the voltage and current signals. To improve the accuracy of SOC estimation and consider that the extended Kalman filter algorithm needs linear approximation of the system equation, the unscented Kalman filter (UKF) algorithm was used to reduce the influence of sampling noise, and an improved algorithm with better filtering effect and SOC estimation accuracy was proposed. Based on the SOC estimation and battery model, the peak power prediction method for the battery is proposed and used in the power distribution strategy for Series HEV. Considering the frequent changes in load current and sampling noise, an experiment was designed to verify the effectiveness and robustness of the algorithm. The experimental results show that the UKF algorithm and the improved UKF algorithm can achieve 6% and 1.5% estimation error. The power distribution strategy based on battery SOC estimation and peak power prediction is tested and validated.

Accurate determination of the continuous and instantaneous load capability is important for safety, durability, and energy deployment of lithium-ion batteries. It is also a crucial challenge for the battery-management-system to determine the load capability of a pack due to inevitable differences among in-pack cells. To overcome these challenges, a flexible battery equivalent-circuit-model is first developed without prior knowledge of in-pack cells' capacity and internal resistance. Then, a method for determining the load capability of a pack is proposed based on a two time-scale filter using a combination of recursive-least-square-method and Kalman-filter. Distinguished from traditional model-based state-of-charge estimation strategies, only the terminal voltages and currents are used to estimate the open-circuit-voltages of each cell. The temperature and hysteresis effects are compensated to improve estimation accuracy. To reduce the computational complexity, a two time-scale scheme is designed to monitor the state-of-charge and model parameters of in-pack cells, which are further used to determine the peak power of the battery pack. Experiments and simulations conducted on LiFePO 4 battery pack are employed to verify the performance of the proposed approach under dynamic operating currents and temperatures. The results indicate that the proposed approach is suitable for determining the load capability of a battery pack. • A two time-scale co-estimator for determining battery load capability is proposed. • A peak power prediction framework for series-connected battery pack is proposed. • The analysis for influential mechanisms of peak power constraints are performed. • The advancements of the proposed method are verified under dynamic conditions. [ABSTRACT FROM AUTHOR]

The Zinc Nickel single flow batteries (ZNBs) have gained increasing attention recently. Due to the high variability of the intermittent renewable energy sources, load demands, and the operating conditions, the state of charge (SoC) is not an ideal indicator to gauge the potential cycling abilities. Alternatively, the peak power is more closely related to the instantaneous power acceptance and deliverance, and its real-time estimation plays a key role in grid-based energy storage systems. However, little has been done to comprehensively examine the peak power delivery capability of Zinc Nickel single flow batteries (ZNBs). To fill this gap, the recursive least square (RLS) method is first employed to achieve online battery model identification and represent the impact of varying working conditions. The state of charge (SoC) is then estimated by the extended Kalman filter (EKF). With these preliminaries, a novel peak power prediction method is developed based on the rolling prediction horizon. Four indices are proposed to capture the characteristics of the peak power capability over length-varying prediction windows. Finally, the consequent impacts of the electrode material and applied flow rate on peak power deliverability are analysed qualitatively. • Loads dynamics are considered based on online adaptive model identification. • Online peak power prediction is developed according to the rolling horizon scheme. • Mathematical fundamentals of proposed peak power estimator are elucidated. • Four indices are used to characterise the peak power deliverability. • Impacts of flow rate and electrode materials on power deliverance are analysed. [ABSTRACT FROM AUTHOR]