A MPSC strategy with adjustable C-rate of lithium-ion cell for PMSM driving EV

Abstract

Battery is the core equipment of the electrical vehicle (EV), battery life, and performance are critical to EV. Only motor variables are considered in most model predictive control (MPC) strategy in which battery indexes are neglected. A model predictive speed control (MPSC) strategy with adjustable C-rate of battery is proposed in this paper on EV with permanent magnet synchronous machine (PMSM). The rest capacity of battery is involved in the cost function, and the operating speed is related to the rest capacity of battery which adjusts the C-rate of the discharging currents further to protect battery. As the EV runs, the C-rate is decreased step by step according to the improved cost function, and the energy is saved to prolong mileage. The benefits of the proposed method including discharging capacities and operating mileage are corroborated by the simulation and repetitive experimental results under same conditions.

A multivariable sliding mode predictive control method for the air management system of automotive fuel cells

“…The red dot lines represent the control results by the proposed method. And the values are obtained from the ESO estimation results by equation (10). The blue lines give the control target expectations.…”

“…where x6 and x7 are estimate values of x 6 and x 7 , b 1 and b 2 are observer gains that need to be determined. Define the observer errors x6 = x 6 À x6 , x7 = x 7 À x7 , then subtract equation (10) from equation (11), then error function can be written as:…”

“…In order to ensure the convergence of the ESO, the polynomial s 2 + (b 1 + c 16 )s + c 16 b 2 corresponding to the observer must meet the Hurwitz condition to ensure the convergence of the ESO, that is, the poles of the polynomial are in the left half plane of the coordinate axis. Based on equation (10), the estimated values of controller variables can be calculated by: pca = x7 and lO2 = x O2, in k sm (x 4 À pca )= nM O2 I st 4F .…”

“…The model predictive control is another useful method for OER control, which performs high accuracy and fast response. 10 Abdullah and Idres 11 proposed a constrained model predictive control (MPC) method to regulate the air flow rate and avoid the oxygen starvation. Nonlinear MPC, 12,13 generalized predictive control, 14 explicit MPC, 15 robust MPC, 16 and other improved MPC algorithms 17,18 were also widely applied in the OER regulation.…”