System and method for calibration optimizing experiment of hybrid power assembly based on working condition self-learning

Abstract

The invention discloses system and method for calibration optimizing experiment of a hybrid power assembly based on working condition self-learning. The system comprises a hybrid power assembly system, a hybrid powder assembly stand system, an in-use new energy vehicle acquiring module, a new energy remote monitoring database, an experiment management control panel, a self-learning optimizing processing module and a hybrid power energy managing unit. According to the system, the working condition parameter self-learning under special geographical conditions and different driving modes of drivers can be achieved through the historical data of a new energy remote monitoring database and a new energy power assembly stand experiment system, the optimal hybrid power energy distribution and experiment verification can be performed, and therefore, the problem of the hybrid power assembly can be found out, and the energy distribution parameters can be optimized, and as a result, the optimizing cost of the hybrid power assembly can be reduced.

Description

technical field [0001] The invention relates to the field of fuel-saving optimization of new energy hybrid powertrains, in particular to an experimental device and an experimental method for experimental optimization calibration of hybrid powertrains under road working conditions. Background technique [0002] With the environmental pressure and the fossil fuel crisis, the energy saving and emission reduction of vehicles has become the main direction of road vehicle improvement. Hybrid power has dual power sources, which can optimize the energy distribution of two or more power sources according to the driving conditions of the vehicle, thereby improving energy utilization. In addition, hybrid power also has a certain amount of braking energy recovery, further reducing energy waste. In recent years, the use of hybrid technology is the main way to improve vehicle fuel consumption and reduce pollutant emissions. [0003] Hybrid energy optimization management strategy is one ...

AI Technical Summary

Problems solved by technology

In addition, for different driving conditions, the difference in energy consumption of the same hybrid power during actual operation may reach 30%; the difference in energy consumption of hybrid power with different driving styles under the same road conditions may also reach 40%.

Traditional control strategy calibration based on typical cycle conditions is difficult to meet the promotion and reference of hybrid vehicles

[0004] In order to improve the working condition adaptability of hybrid energy management, starting from the optimization of control strategy, hybrid energy optimization strategy based on driving condition identification, hybrid energy optimization strategy based on driver's driving style identification, and multi-level energy optimization strategy, etc. Although it can theoretically provide a hybrid energy optimization strategy, there are hardware resource limitations, high cost, and difficulty in practical application

[0005] Starting from the off-line calibration of the vehicle, the direct calibration of the control strategy is carried out using typical cycle conditions or actual operating data in various regions, which has the problems of heavy manual workload, cumbersome calibration process and high cost.

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