A comprehensive working state monitoring method for power battery packs considering state of balance and aging correction.

A comprehensive working state monitoring method is proposed to protect the power lithium-ion battery packs, implying accurate estimation effect but using minimal time demand of self-learning treatment. A novel state of charge estimation model is conducted by using the improved unscented Kalman filtering method, in which the state of balance and aging process correction is considered, guaranteeing the powered battery supply reliability effectively. In order to realize the equilibrium state evaluation among the internal battery cells, the numerical description and evaluation is putting forward, in which the improved variation coefficient is introduced into the iterative calculation process. The intermittent measurement and real-time calibration calculation process is applied to characterize the capacity change of the battery pack towards the cycling maintenance number, according to which the aging process impact correction can be investigated. This approach is different to the traditional methods by considering the multi-input parameters with real-time correction, in which every calculation step is investigated to realize the working state estimation by using the synthesis algorithm. The state of charge estimation error is 1.83%, providing the technical support for the reliable power supply application of the lithium-ion battery packs

A novel endurance prediction method of series connected lithium-ion batteries based on the voltage change rate and iterative calculation.

High-power lithium-ion battery packs are widely used in large and medium-sized unmanned aerial vehicles and other fields, but there is a safety hazard problem with the application that needs to be solved. The generation mechanism and prevention measurement research is carried out on the battery management system for the unmanned aerial vehicles and the lithium-ion battery state monitoring. According to the group equivalent modeling demand of the battery packs, a new idea of compound equivalent circuit modeling is proposed and the model constructed to realize the accurate description of the working characteristics. In order to realize the high-precision state prediction, the improved unscented Kalman feedback correction mechanism is introduced, in which the simplified particle transforming is introduced and the voltage change rate is calculated to construct a new endurance prediction model. Considering the influence of the consistency difference between battery cells, a novel equilibrium state evaluation idea is applied, the calculation results of which are embedded in the equivalent modeling and iterative calculation to improve the prediction accuracy. The model parameters are identified by the Hybrid Pulse Power Characteristic test, in which the conclusion is that the mean value of the ohm internal resistance is 20.68mΩ. The average internal resistance is 1.36mΩ, and the mean capacitance value is 47747.9F. The state of charge prediction error is less than 2%, which provides a feasible way for the equivalent modeling, battery management system design and practical application of pack working lithium-ion batteries

An adaptive working state iterative calculation method of the power battery by using the improved Kalman filtering algorithm and considering the relaxation effect.

The battery modeling and iterative state calculation in the battery management system is very important for the high-power lithium-ion battery packs, the accuracy of which affects its working performance and safety. An adaptive improved unscented Kalman filtering algorithm is developed to realize the iterative calculation process, aiming to overcome the rounding error in the numerical calculation treatment when it is used to estimate the nonlinear state value of the battery pack. As the sigma point is sampled in the unscented transform round from the unscented Kalman filter algorithm, an imaginary number appears that results in the working state estimation failure. In order to solve this problem, the decomposition is combined with the calculation process. Meanwhile, an adaptive noise covariance matching method is implied. Experiments show that the proposed method can guarantee the semi-positive and numerical stability of the state covariance, and the estimation accuracy can reach the third-order precision. The estimation error remains 1.60% under the drastic voltage and current change conditions, which can reduce the estimation error by 1.00% compared with the traditional method. It can provide a theoretical safety protection basis of the energy management for the lithium-ion battery pack

Open circuit voltage and state of charge relationship functional optimization for the working state monitoring of the aerial lithium-ion battery pack.

The aerial lithium-ion battery pack works differently from the usual battery packs, the working characteristic of which is intermittent supplement charge and instantaneous large current discharge. An adaptive state of charge estimation method combined with the output voltage tracking strategy is proposed by using the reduced particle - unscented Kalman filter, which is based on the reaction mechanism and experimental characteristic analysis. The improved splice equivalent circuit model is constructed together with its state-space description, in which the operating characteristics can be obtained. The relationship function between the open circuit voltage and the state of charge is analyzed and especially optimized. The feasibility and accuracy characteristics are tested by using the aerial lithium-ion battery pack experimental samples with seven series-connected battery cells. Experimental results show that the state of charge estimation error is less than 2.00%. The proposed method achieves the state of charge estimation accurately for the aerial lithium-ion battery pack, which provides a core avenue for its high-power supply security

A novel adaptive state of charge estimation method of full life cycling lithium-ion batteries based on the multiple parameter optimization.

The state of charge (SoC) estimation is the safety management basis of the packing lithium-ion batteries (LIB), and there is no effective solution yet. An improved splice equivalent modeling method is proposed to describe its working characteristics by using the state-space description, in which the optimization strategy of the circuit structure is studied by using the aspects of equivalent mode, analog calculation, and component distribution adjustment, revealing the mathematical expression mechanism of different structural characteristics. A novel particle adaptive unscented Kalman filtering algorithm is introduced for the iterative calculation to explore the working state characterization mechanism of the packing LIB, in which the incorporate multiple information is considered and applied. The adaptive regulation is obtained by exploring the feature extraction and optimal representation, according to which the accurate SoC estimation model is constructed. The state of balance evaluation theory is explored, and the multiparameter correction strategy is carried out along with the experimental working characteristic analysis under complex conditions, according to which the optimization method is obtained for the SoC estimation model structure. When the remaining energy varies from 10% to 100%, the tracking voltage error is less than 0.035 V and the SoC estimation accuracy is 98.56%. The adaptive working state estimation is realized accurately, which lays a key breakthrough foundation for the safety management of the LIB packs