Hybrid electric vehicle battery life optimization method considering battery health state

Abstract

The invention aims to disclose a hybrid electric vehicle battery life optimization method considering a battery health state. The control method comprises two parts: a driving mode recognition moduleand a battery life optimization method. For the plug-in hybrid electric vehicle, the sum of the fuel consumption cost of each stage and the battery life attenuation cost considering the battery healthstate is taken as an optimization objective function, and solving is conducted based on a dynamic programming algorithm; the driving characteristic parameters are trained by adopting a random forestmodel to realize the identification of driving modes, a neural network controller isrespectively trained by utilizing corresponding optimization results under each driving mode, and a corresponding neural network-based energy management control strategy isestablished to realize the optimization of the service life of the battery. According to the method, real-time optimization of the service lifeof the battery is achieved, the driving mileage of the whole vehicle is increased, the fuel economy of the whole vehicle is guaranteed, meanwhile, the service life of the battery is prolonged, and theuse cost of the vehicle is reduced.

Description

technical field [0001] The invention belongs to the field of hybrid electric vehicle battery life, more precisely, the invention relates to a hybrid electric vehicle battery life optimization method considering battery health state. Background technique [0002] Hybrid electric vehicles have multiple power sources, and it is necessary to reasonably coordinate the working status of each power source to meet the dynamic performance of the vehicle, and then give full play to its energy-saving advantages. As an important type of hybrid vehicles, plug-in hybrid vehicles have a larger battery capacity than non-plug-in hybrid vehicles and can support longer mileage. During short-distance driving, the vehicle power system provides energy to drive the vehicle, which makes it have the advantages of a pure electric vehicle. The performance of its power battery directly affects the performance of the drive motor, thereby affecting the fuel economy and emission performance of the vehicle...

AI Technical Summary

Problems solved by technology

The current battery life research mainly focuses on optimizing the battery life under specific working conditions, and can only obtain the theoretical battery life attenuation under specific working conditions, without considering the impact of driving mileage on battery life attenuation, so from the perspective of driving mileage To divide the driving mode of plug-in hybrid electric vehicles to optimize battery life

[0004] At present, most of the estimation methods of the battery health state only consider the operating state of the battery. For example, the Chinese patent publication number is CN107831444A, and the publication date is 2018-03-23. It discloses a method for estimating the health state of lithium-ion batteries. Based on the measured apparent data, a method for estimating the state of health of the lithium-ion battery is obtained, which has the characteristics of easy acquisition of parameters and real-time application, but does not take into account the impact of vehicle economics on the degree of battery life attenuation

The optimization method of battery life is mainly a model-based estimation method. For example, the patent number is CN107878445B, and the publication date is 2019-01-18. It discloses an energy optimization management method for hybrid electric vehicles considering battery performance attenuation. This method is aimed at Chinese typical Optimizing battery performance in urban working conditions ensures fuel economy, but does not consider the impact of changes in battery health and mileage on battery life attenuation

The existing published patents do not fully consider the relationship between the attenuation degree of the battery health state and the driving mode of the vehicle based on the mileage, making it difficult to fully optimize the battery life under actual driving conditions, which in turn leads to deterioration of the battery use conditions, which is not conducive to Improve vehicle fuel economy

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