Haiou Liu

TL;DR: A genetic algorithm based co-optimization method, along with an adaptive real-time optimization strategy, is proposed to solve the proposed VMMP problem, able to provide the fuel economic route and reference speed for drivers or automated vehicles to improve the vehicle fuel economy.

TL;DR: This paper presents a novel trajectory tracking methodology, called kinematics-aware model predictive control (KAMPC), by combining the slip kinematic model with a trajectory tracking control strategy for skid-steered tracked vehicles.

TL;DR: The co-simulation results indicate that the proposed EACC is able to decrease the fuel consumption by more than 30% comparing with the power follower strategy adopting the fastest route, and even with the same powertrain controller, the economic route and speed stratgey is ability to improve the fuel economy by 14.21%, compared with the fastest routes without optimization.

TL;DR: This paper proposes to add a third parameter, called the terrain coefficient, to form a three-parameter gear-shifting schedule for improving the fuel economy of a vehicle by adding a compound parameter consisting of the road grade and the rolling resistance coefficient.

TL;DR: In this paper, a kernel density estimation-based load spectrum compilation method for a transmission shaft of a tracked vehicle is proposed, which comprises the following steps of S1, collecting and preprocessing torque load sample data of the tracked vehicle; S2, generating a two-dimensional load spectrum through first two-time rain flow counting, mean amplitude extremum inference, second rain flow count, two dimensional kernel density estimations, multi-condition synthesis and extrapolation in sequence.