Electric automobile compound energy management method based on rule and Q-learning reinforcement learning

A technology of enhanced learning and composite energy, applied in electric vehicles, vehicle energy storage, vehicle components, etc., to reduce energy loss and improve efficiency

Active Publication Date: 2019-04-02
NINGBO INST OF TECH ZHEJIANG UNIV ZHEJIANG
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to solve the energy distribution problem of the electric vehicle hybrid power system. This paper proposes a compound energy management meth

Method used

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  • Electric automobile compound energy management method based on rule and Q-learning reinforcement learning
  • Electric automobile compound energy management method based on rule and Q-learning reinforcement learning
  • Electric automobile compound energy management method based on rule and Q-learning reinforcement learning

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Embodiment

[0082] This method is used on the research experimental platform of electric vehicles and two-way power conversion using ECE (Economic Commission of Europe) driving conditions for experiments. The structure diagram of the experimental platform is as image 3 As shown, the entire research experiment platform is managed by industrial computer 1. Industrial computer 1 controls the charger, inverter, battery management system and two-way DC / DC converter through the CAN network, and uses Ethernet and power dynamometer system industrial computer 2 Communication, thus motor and inverter. ECE driving conditions such as Figure 4 Shown.

[0083] In the compound energy management strategy, the energy management strategy based on Q-learning enhanced learning is combined with the rule-based energy management strategy to complete the energy distribution of the hybrid power system: when the car is braking or the lithium battery and super capacitor When the energy is too low, the output power ...

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Abstract

The invention discloses an electric automobile compound energy management method based on a rule and Q-learning reinforcement learning. By the adoption of the method, energy management is conducted according to the power requirement of a vehicle at every moment and the SOCs of a lithium battery and a super capacitor. In an energy management strategy based on Q-learning reinforcement learning, an energy management controller takes actions through observation of the system state, calculates the corresponding award value of each action and makes updates in real time, an energy management strategywith the minimum system loss power is obtained through utilization of the award values through Q-learning reinforcement learning algorithm simulative training, finally, real-time power distribution is conducted through the energy management strategy obtained through learning, and meanwhile, the award values are continuously updated to adapt to the current driving condition. By the adoption of themethod, on the basis that the required power is met, the electric quantity of the lithium battery can be kept, the service life of the lithium battery is prolonged, meanwhile, system energy loss is reduced, and the efficiency of a hybrid power system is improved.

Description

Technical field [0001] The invention relates to an electric vehicle energy management method based on rules and Q-learning enhanced learning. Background technique [0002] The current high dependence of automobiles on non-renewable fuels has aroused people's attention to the sustainable development of the global environment. The problems of air pollution and resource consumption caused by traditional cars have greatly promoted the development of electric cars. For the energy storage system of electric vehicles, not only enough energy is needed to travel long distances, but also enough power is needed to accelerate, brake, and climb hills. Lithium batteries can meet the requirements of high power density and high energy density due to their light weight, high energy storage, high power, and no pollution. However, the use of lithium batteries alone may cause the battery pack to overheat and shorten its life. The super capacitor has the advantages of long life and high instantaneo...

Claims

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Application Information

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IPC IPC(8): B60L50/40B60L50/60B60L58/12
CPCB60L2240/54Y02T10/70
Inventor 陶吉利韩凯胡远敏马龙华张智焕
Owner NINGBO INST OF TECH ZHEJIANG UNIV ZHEJIANG
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