The inherent high nonlinearity of Switched Reluctance Machines (SRMs) poses significant challenges to modeling. Conventionally, large-data-size lookup tables (LUTs) are used, which consume extensive storage space in digital signal processors (DSPs) and complicate the application of well-established PI control techniques. This paper presents a linear flux SRM model, leveraging the space mapping method to map the SRM's nonlinear flux profile into a linear space in relation to both current and position. The proposed linear model offers two key advantages. First, it reduces data storage needs due to smaller LUTs. Second, it enhances the feasibility of applying PI control. Simulation results of an 8/6 SRM are presented to illustrate the proposed approach.

This paper introduces an improved real-time Model Predictive Climate Control (MPCC) technique to reduce power consumption in Heating, Ventilation and Air Conditioning (HVAC) system of the Battery Electric Vehicles (BEVs). In the presented control technique, the fan, heating and cooling power usage is optimized considering the effect of the ambient temperature. Moreover, the need of a physical temperature measurement sensor is eliminated using an ambient temperature observer. In a typical BEV, the HVAC load is significant and it influences the overall vehicle performance and driving range. Therefore, a real-time control system capable of maintaining desired cabin temperature while achieving maximum HVAC efficiency is highly desirable. In the proposed MPCC, the optimum split of the battery power between the heating\cooling and the fan power is identified as a function of ambient temperature through an offline optimization process and used in the form of a lookup-table for real-time implementation. It is demonstrated that the proposed MPCC improves energy consumption efficiency of the entire HVAC system up to 5% by dynamically adapting to the variations in the ambient temperature. Furthermore, owing to the online temperature observation process, the proposed HVAC control system allows elimination of ambient temperature sensor and corresponding maintenance efforts. In this paper, the performance of the proposed MPCC is evaluated over a single-zone HVAC model of a BEV in MATLAB\Simulink®environment.

This paper presents a novel self-commissioning procedure for the identification of inverter nonlinearity constant comprised of the average voltage drops on switches and diodes in conduction state and switching delays. Simultaneous estimation of phase resistance, d-axis synchronous inductance, and inverter nonlinearity constant is achieved at standstill condition by injecting sinusoidal d-axis current. The advantages of the proposed self-commissioning method are twofold: 1) The co-estimation capability provides insensitivity towards errors in resistance and d-axis inductance. 2) While sinusoidal d-axis current is injected, the q-axis current is actively maintained at 0A. Thus, no torque is generated during the self-commissioning period. The effect of discontinuous distortions due to non-ideal switching as well as current sensor noise is rejected by limiting the estimation period within a feasible estimation window. Thereby, a necessary minimum phase current magnitude is established for achieving accurate estimation. This paper also provides parameter convergence analysis and the existence of unique solutions during proposed self-commissioning process, further justifying the choice of proposed feasible estimation region.