Study on closed loop charging strategy of battery based on Maas's law
In the process of battery charging, the influence of different charging methods on battery life is different. Traditional charging methods such as constant voltage charging, constant current charge, two phase charge, or three phase charge, these methods to a certain extent accelerate the aging of the battery. In this paper, based on the theory of Maas best charge curve and closed-loop control, a charging strategy is designed. The experimental results show that the designed charging strategy has a substantial improvement in the aspect of extending battery life and safety protection.
Study of Parameters Identification Method of Li-Ion Battery Model for EV Power Profile Based on Transient Characteristics Data
Power simulation of lithium ion battery through battery model is of great significance for dynamic response simulation, heat generation calculation and charge-discharge strategy development. The accuracy and applicability of the model become crucial. In order to demonstrate the battery transient characteristics more effectively, a novel identification method for parameters of the 2nd order RC equivalent circuit model was proposed. Based on the derived evolution law of battery transient characteristics under the continuous pulse excitation, four feature points are extracted for parameter identification in each cycle. The proposed method reduced the time cost of identification from 11796.88s to 0.06s while ensuring that the error of voltage doesn’t exceed 2.2mV. In order to verify the power profiles applicability of the proposed method, applicability analysis of power profile for different identification methods was carried out including the methods using different amount of data (4N points, 200 points, 6000 points) under unidirectional current pulse excitation (UCPE), bidirectional current pulse excitation (BCPE) and unidirectional voltage pulse excitation (UVPE). It was illustrated that the identification process using data of multiple cycles could significantly reduce errors, including maximum error and average error. What’s more, the proposed method under UCPE had the lowest maximum error of 0.420% in voltage simulation and −0.421% in the current simulation of power profiles. Compared with the conventional method (using 200 points of single pulse data for parameter identification), the proposed method can reduce the average voltage error and the maximum error by 62.5% and 11.8% respectively under the DST power profile.
Ignition and burning rates of segregated waste combustion in packed beds.
Recent developments in national recycling and re-use programmes for municipal waste have led to segregation of an increasing proportion of waste to enhance material recovery. Several of the segregated streams contain materials that can not viably be re-used or recycled but can be used for energy recovery. In this study, the combustion of cardboard and waste wood was investigated in a small-scale packed bed reactor in order to provide fundamental data for the design/operation of moving bed furnaces. Key parameters of combustion including the ignition and burning rates were evaluated for various air flowrates and compared to the modelling results. Two successive stages of combustion were identified for both samples: the propagation of ignition front into the bed and combustion of the fuel above the ignition front. The burning rate of cardboard reached a peak of about 300 kg/m(2)h at the air flowrate of 936 kg/m(2)h and decreased at higher air flowrates. For waste wood, both the ignition and burning rates increased in the tested range of the air flowrate up to 702 kg/m(2)h, of which the values were very close to those for the cardboard. The model prediction was in good agreement with the test results for waste wood. However, the burning rate for cardboard was under-predicted due to strongly irregular shapes of the fuel.