Patent ID: 11958365
Assignee: JIANGXI UNIVERSITY OF SCIENCE AND TECHNOLOGY
Field: Transport (Mechanical engineering)
Classification: CPC B  G  Y | IPC B  G

Claim 5:
6. The method for dual-motor control on an electric vehicle based on adaptive dynamic programming according to claim 5, wherein S32 specifically comprises:
S321) inputting the real-time data information of the electric vehicle into the execution network to obtain optimal torque distribution of the two motors, and calculating differences ΔTe1 and ΔTe2 between optimal output torque of the two motors and actual output torque of the two motors at the current moment, wherein the real-time data information comprises torque Te, motor efficiency map, rotational speed n, vehicle-mounted battery information soc, difference ΔTe between current torque and target torque, difference Δn between a current rotational speed and a target rotational speed, difference Δsoc between current vehicle-mounted battery information and target vehicle-mounted battery information, and ΔTe(t−1), ΔTe(t−2), map(t−1), map(t−2), Δn(t−1), Δn(t−2), Δsoc(t−1), and Δsoc(t−2) that are obtained through delay;
S322) obtaining differences ΔTe1(t−1), ΔTe1(t−2), ΔTe2(t−1), and ΔTe2(t−2) between optimal output torque and actual output torque of the two motors at moment t−1 and moment t−2 through delay based on differences ΔTe1 and ΔTe2 between optimal output torque of the two motors and actual output torque of the two motors at the current moment that are obtained in S321;
S323) inputting the real-time data information ΔTe1, ΔTe2, ΔTe1(t−1), ΔTe1(t−2), ΔTe2(t−1), ΔTe2(t−2), map, map(t−1), map(t−2), Δsoc, Δsoc(t−1), and Δsoc(t−2) obtained in S321 and S322 into the evaluation network to obtain a value of cost function Ĵ(t) of the evaluation network at moment t;
S324) obtaining real-time data information ΔTe1(t−3), map(t−3), and Δsoc(t−3) at moment t−3 through delay, and inputting the obtained real-time data information ΔTe1(t−1) ΔTe1(t−2), ΔTe1(t−3), ΔTe2(t−1), ΔTe2(t−2), map(t−1), map(t−2), map(t−3), Δsoc(t−1), Δsoc(t−2), and Δsoc(t−3) into evaluation network to obtain a value of cost function Ĵ(t−1) of the evaluation network at moment t−1;
S325) updating weights of the evaluation network and the execution network based on the results obtained in the foregoing steps; and
S326) repeating S321 to S325 until the optimal cost function and the optimal control law are found.