Power Battery State Estimation Research And Management System Design

In the battery management system,battery state estimation is one of its core functions,which mainly include two aspects: state of charge estimation and power state estimation.The battery state estimation can not only provide an important clue for the safety of the battery system,but also can utilize the battery to the most.Therefore,it is extremely valuable to conduct research on battery state estimation.When it comes to the study,this article mainly does the following work:First of all,aim at the problem of excessive battery model error,a second-order Thevenin circuit model considering hysteresis characteristics is designed.This model can feedback the phenomenon of voltage hysteresis when the battery is in use;and adopts the optimal bounding ellipsoid algorithm for parameters Identification,this identification method can have a better identification effect when the noise is uncertain.Comparing design experiments,t the results show that the optimal delimited ellipsoid algorithm has higher accuracy than the least squares algorithm when faced with sudden changes in current;the error between the output voltage of the hysteretic model and the output voltage of the non-hysteresis model is smaller than 0.07 V.Secondly,to solve the problem of inaccurate measurement of the state of charge caused by measurement error in the battery use,a state of charge estimation based on the chaotic sunflower algorithm is proposed.The algorithm brings the chaotic factor of the chemotactic step in the sunflower algorithm to change the length of the chemotactic step,and applies a shrinkage strategy and Gaussian mutation in the process of searching for the best state,so as to significantly improve the efficiency of finding the best state.Finally,an experiment is designed under impulse conditions.When the initial state of charge is 1 and 0.68,the effects are different,then compare them with each other.The experimental results show that the chaotic sunflower algorithm can promise faster convergence and higher accuracy in the state of charge estimation.Thirdly,an improved particle filter algorithm is proposed for the phenomenon that particles are easily degraded in the traditional particle filter algorithm.The importance sampling technology in the traditional particle filter algorithm is also introduced into the chaotic sunflower algorithm,and the resampling technology in the traditional particle filter algorithm has been upgraded to improve the accuracy of the particle filter algorithm.Then combine the upgraded particle filter algorithm and ampere-hour integration to solve the problem of excessive error caused by the inaccurate initial value of the ampere-hour integration algorithm when estimating the battery state of charge.The experimental results demonstrate that compared with the traditional ampere-hour integration algorithm,the accuracy of the fusion algorithm in estimating the state of charge of the battery has been improved by 1.21%.In view of the difficulty in estimating the power state of the battery in the operation process,the improved particle filter algorithm is first used to estimate the battery state of charge,and the charge and discharge power state is estimated under three constraints---state of charge,voltage and current.Experiments have verified that the improved particle filter algorithm can estimate the power state well within 10 s and 30 s.Finally,according to the actual application,a battery management system,with MC9S12GRMV1 as the processor,is designed.And the state of charge estimation and power state estimation functions of the battery management system have already been tested through the platform.The results indicate that the designed battery management system can estimate the state of charge and power state well.