Abstract
This article proposes a new control law for an embedded DC distributed network supplied by a supercapacitor module (as a supplementary source) and a battery module (as the main generator) for transportation applications. A novel control algorithm based on the nonlinear differential flatness approach is studied and implemented in the laboratory. Using the differential flatness theory, straightforward solutions to nonlinear system stability problems and energy management have been developed. To evaluate the performance of the studied control technique, a hardware power electronics system is designed and implemented with a fully digital calculation (real-time system) realized with a MicroLabBox dSPACE platform (dual-core processor and FPGA). Obtained test bench results with a small scale prototype platform (a supercapacitor module of 160 V, 6 F and a battery module of 120 V, 40 Ah) corroborate the excellent control structure during drive cycles: steady-state and dynamics.
Highlights
The crisis of continuously growing fossil fuel costs has provoked transportation industries to advance more efficient automobiles technology
In [14], Song et al have developed an energy management algorithm for an electric vehicle supplied by batteries and SCs
Two algorithms are used: one based on Lyapunov-function regulation to stabilize the DC bus, and another based on a sliding mode approach to regulate both classic 2-quadrant converters connected to power sources, making them less reliable in case of electrical failures
Summary
The crisis of continuously growing fossil fuel costs has provoked transportation industries to advance more efficient automobiles technology. In [14], Song et al have developed an energy management algorithm for an electric vehicle supplied by batteries and SCs. Two algorithms are used: one based on Lyapunov-function regulation to stabilize the DC bus, and another based on a sliding mode approach to regulate both classic 2-quadrant converters connected to power sources, making them less reliable in case of electrical failures. Compared to the nonlinear algorithm (i.e., sliding mode, Lyapunov, fuzzy logic) reported in [13,14,15], nonlinear algorithms based on differential flatness require the use of trajectory planning to implement the control laws This trajectory planning aims at controlling different variables (e.g., currents of converters, stored energy in the DC bus and SC) to manage the energy in an EV while optimizing the performance of the system for any operating point.
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