The selection and matching of electric vehicle power system is a key issue in the development process of the whole vehicle. According to the vehicle parameters and performance indicators, the key parameters of the motor and the power battery pack are determined by matching and calculating the power system. Based on the cruise simulation software, build the electric vehicle simulation analysis model, and take the power and economic simulation analysis. According to the comparison between the simulation results and the experimental data, the rationality of the simulation model is further determined, which lays a foundation for the subsequent simulation optimization.
To achieve collision free path planning and tracking control of unmanned launch platforms in complex environments, this paper proposes a path obstacle avoidance control method based on deep deterministic strategy gradient reinforcement learning (RL) algorithm. This method models the collision avoidance problem as a Markov decision process, and uses a deep neural network to establish a nonlinear mapping from the laser radar perception state to the optimal control variables of velocity and angular velocity. Compared with traditional path obstacle avoidance algorithms, RL based obstacle avoidance algorithms do not rely on experience to set corner control rules. They can achieve end-to-end mapping from environmental state to optimal control by setting the return function, overcoming the shortcomings of traditional obstacle avoidance algorithms such as weak adaptability to the environment and low generalization ability. The effectiveness and feasibility of the proposed path collision avoidance algorithm were verified through simulation experiments. The results show that compared with traditional path planning obstacle avoidance algorithms, the RL based path avoidance method can achieve obstacle avoidance control in complex environments.
The combustion phase of a homogeneous charge compression ignition (HCCI) engine fueled with n-butanol/diesel blends was investigated on a 2-cylinder direct injection naturally aspirated and four-stroke engine at speed of 1000 r/min under different blending ratio and excess air ratio. The results show that with the increase of n-butanol/diesel volume content in the blends, the ignition timing of HCCI combustion retards under constant excess air ratio and the combustion duration shorten. When the blending ratio keeps constant, the the ignition timing brings forward with decrease in excess air ratio while the combustion duration has no significant difference, except for the blending ratio of 95%.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.