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A method of intelligent shift schedule control based on q-learning reinforcement learning algorithm

A shift schedule and reinforcement learning technology, applied in the field of intelligent shift schedule control based on the Q-Learning reinforcement learning algorithm, can solve the problems of difficulty in adapting to the dynamic traffic environment, large impact on vehicle dynamics and economy, and vehicle dynamics. and economical difficulties to achieve optimal

Active Publication Date: 2022-05-10
LIAOCHENG UNIV
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Problems solved by technology

[0002] A large number of studies have shown that the shift schedule has a great influence on the power and economy of the vehicle. The traditional regular shift schedule is difficult to adapt to the dynamic and uncertain traffic environment due to parameter determination, and cannot adapt to different road traffic conditions. Dynamic adaptive shift control, therefore, it is difficult to achieve the optimal power and economy of the vehicle

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  • A method of intelligent shift schedule control based on q-learning reinforcement learning algorithm
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  • A method of intelligent shift schedule control based on q-learning reinforcement learning algorithm

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Embodiment Construction

[0024] Below in conjunction with embodiment, further illustrate the present invention.

[0025] see image 3 It can be seen that a kind of intelligent shift rule control method based on Q-Learning reinforcement learning algorithm of the present invention comprises the following steps:

[0026] P1: Set the action set A={a for reinforcement learning 1 ,a 2 …a t} and state set S={s 1 ,s 2 …s t}; for action set A={a 1 ,a 2 …a t}, where the action value a(t) at time t represents the target gear that should be selected at time t, that is, a(t)={1,2,3,4}, for the state set S={s 1 ,s 2 …s t}, wherein the state s(t) at time t includes vehicle speed, acceleration and gear at time t, that is, s(t)={v(t),acc(t),gear(t)};

[0027] P2: Discretize the v and acc in the state set into 100 parts respectively, and the gear is 4 gears, that is, the state variables in the state set are 100×100×4=40000, and then realize it through the optimal Latin hypercube design State space reductio...

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Abstract

The invention discloses that the invention relates to an intelligent shifting regularity control method based on the Q-Learning reinforcement learning algorithm. The reinforcement learning algorithm is used to control the up and down of the gear, and after repeated training, the AMT is always in the gear with the best energy consumption and economy. It can work under the position to solve the problem that adaptive shift control cannot be realized under unknown dynamic conditions, which further reduces energy consumption and further improves the power and economy of the vehicle.

Description

technical field [0001] The invention relates to the field of AMT intelligent control, in particular to an intelligent shift rule control method based on a Q-Learning reinforcement learning algorithm. Background technique [0002] A large number of studies have shown that the shift schedule has a great influence on the power and economy of the vehicle. The traditional regular shift schedule is difficult to adapt to the dynamic and uncertain traffic environment due to parameter determination, and cannot adapt to different road traffic conditions. Dynamic adaptive shift control, therefore, it is difficult to achieve optimal power and economy of the vehicle. Therefore, an intelligent shift schedule control method based on reinforcement learning is proposed. Contents of the invention [0003] The technical problem to be solved by the present invention is to provide an intelligent shift rule control method based on the Q-Learning reinforcement learning algorithm. The method ada...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): F16H61/02G06N20/00
CPCG06N20/00F16H61/0213F16H2061/0075F16H2061/0087F16H2061/009F16H2061/0223
Inventor 张坤郭洪强崔庆虎赵峰睿
Owner LIAOCHENG UNIV
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