Hybrid electric vehicle energy optimization method based on battery life attenuation mode identification

Abstract

The invention discloses a hybrid electric vehicle energy optimization method based on battery life attenuation mode identification. The control method comprises the following steps that energy management optimization control strategies under a China passenger vehicle working condition, a new European driving cycle (NEDC) working condition and a highway fuel economy test (HWFET) working condition are generated respectively for a hybrid electric vehicle, and the sums of the fuel consumption costs and the battery life attenuation costs of stages are taken as an optimized objective function; and the battery life attenuation mode under each working condition is classified based on the result of solution based on a discrete dynamic programming algorithm, finally a battery life attenuation mode is identified based on the neural network, and a control strategy which can be applied to a real vehicle online in real time under the corresponding mode is established. According to the hybrid electric vehicle energy optimization method based on the battery life attenuation mode identification, based on the working condition dimension, the battery life attenuation rule is extracted, the battery life attenuation mode is identified, the battery life attenuation degree is effectively relieved, the fuel economy is guaranteed, and the accuracy of a system on the battery life prediction under an actual driving condition and the adaptability of the system to the actual driving condition are improved.

Description

technical field [0001] The invention belongs to the technical field of hybrid electric vehicle energy management, and more precisely, the invention relates to an energy optimization method of a hybrid electric vehicle based on identification of battery life attenuation patterns. Background technique [0002] There are multiple power sources in hybrid electric vehicles, and it is necessary to coordinate the working status of each power source to meet the power demand of the vehicle, and then give full play to its energy-saving advantages. The performance of the power battery directly affects the performance of the drive motor, thereby affecting the fuel economy and emission performance of the vehicle, which is the key to the performance of the vehicle. Studies have shown that fuel economy and battery life attenuation are mutually influential. The current research on energy optimization management strategies mainly achieves the best fuel economy under specific working conditio...

AI Technical Summary

Problems solved by technology

Among them, the model-based estimation method, due to the complex and changeable actual driving conditions of the car, it is difficult to design a model that is exactly the same as the actual online working conditions of the battery to accurately predict the battery life, such as patent CN107878445A, the publication date is 2018-4-6 , which discloses an energy optimization management method for hybrid electric vehicles considering battery performance attenuation. This method optimizes battery performance for typical urban working conditions in China to ensure fuel economy, without considering battery performance attenuation under other working conditions characteristics, and cannot achieve online real-time optimal control of battery performance and vehicle economy under actual driving conditions

However, the data-based estimation method uses a large amount of experimental data to study the attenuation law of battery life. The data processing volume is huge, and it is not universal in working conditions. It can only describe the battery life attenuation law in a single experiment or under a single working condition more accurately. , but cannot represent the law of battery life decay under actual driving conditions

[0004] In addition, hybrid vehicle energy management strategies are generally divided into rule-based energy management strategies and optimization-based energy management strategies. Rule-based energy management strategies are often used in real vehicle control, but the determination of its rules is very dependent on experience. , while the dynamic programming global optimization management strategy with excellent domestic research results is offline optimization, which cannot be directly applied to real-time control

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