Powertrain sizing of electrically supercharged internal combustion engine vehicles

We assess the concept of electrically supercharged internal combustion engines, where the supercharger, consisting of a compressor and an electric motor, draws electric power from a buffer (a battery or a supercapacitor). In particular, we investigate the scenario of downsizing the engine, while delivering high power demands by supercharging. Simultaneously, we seek the optimum buffer size that provides sufficient electric power and energy to run the supercharger, such that the vehicle is able to deliver the performance required by a driving cycle representing the typical daily usage of the vehicle. We provide convex modeling steps that formulate the problem as a second order cone program that not only delivers the optimal engine and buffer size, but also provides the optimal control and state trajectories for a given gear selection strategy. Finally, we provide a case study of sizing the engine and the electric buffer for different compressor power ratings

Four-quadrant speed control of 4/2 switched reluctance machines

The main challenge in designing a 4Q speed control algorithm for a 4/2 SRM lies in the existence of “dead” torque zones where the machine’s capability to produce torque is considerably reduced. This makes its startup challenging and has so far hindered its fourquadrant control. This paper provides a solution for the 4/2 SRM startup problem and uses it to enable its fourquadrant operation. The proposed control algorithm respects the varying average torque bounds and explicitly enforces the maximum phase current limit. A parametric, open-loop, average torque control scheme is derived and employed to ensure efficient production of torque required to track the desired speed reference. The effectiveness of the proposed solution is verified experimentally on a highspeed hardware setup

Extremum seeking control with adaptive disturbance feedforward

This paper presents an extension to the classical gradient-based extremum seeking control for the case when the disturbances responsible for changes in the extremum of a selected performance function are available for measurement. Based on these additional measurements, an adaptive extremum seeking disturbance feedforward is designed that approximates the unknown, static mapping between the disturbances and the optimal inputs. For this purpose, orthogonal, multivariate Tchebyshev polynomials are used. The feedforward enables the extremum seeking to be conducted in the proximity of the extremum thus yielding improvements both in terms of accuracy and increased convergence speed compared to the traditional scheme. Simulation results given for a turbine driven electrical generator system demonstrate the benefits of the presented design

Convex Modeling and Optimization of a Vehicle Powertrain Equipped with a Generator-Turbine Throttle Unit

This paper investigates an internal combustion (gasoline) engine throttled by a generator-turbine unit. Apart from throttling, the purpose of this device is to complement the operation of a conventional car alternator and support its downsizing by introducing an additional source of energy for the electric auxiliaries. Its energy recovery potential is examined by employing a novel, convex approach to modeling and optimization of the resulting vehicle powertrain. For a given gear-shifting strategy, the proposed method allows the computation of optimal control trajectories, e.g., the optimal engine fuel, alternator, and turbine power, as well as of optimal design parameters, i.e., the optimal battery, alternator and turbogenerator size. The conducted numerical case study shows that a generator-turbine throttle unit has a potential to reduce the total operational (fuel) and component (battery, alternator, and turbogenerator) costs by typically 2%-6%, depending on factors such as the engine size and the choice of a driving cycle

Extremum seeking control with data-based disturbance feedforward

This paper presents a practical extension to the classical gradient-based extremum seeking control for the case when the disturbances responsible for the changes in the extremum of a related performance function can be measured. The additional information is used to improve accuracy, convergence speed and robustness of the underlying ESC scheme. Based on the disturbance measurements a map between them and the optimal inputs is iteratively constructed and used as an extremum seeking feedforward. A supervising state-machine is designed to regulate feedforward and search processes ensuring the latter is conducted in the close vicinity of an extremum. The search is based on the sinusoidal input perturbation introduced each time the disturbance is detected and removed once the optimal set-point is identified. Simulation results for the cases of photovoltaic and turbine driven electrical generator systems demonstrate the benefits of the presented design

Convex modeling and sizing of electrically supercharged internal combustion engine powertrain

This paper investigates a concept of an electrically supercharged internal combustion engine powertrain. A supercharger consists of an electric motor and a compressor. It draws its power from an electric energy buffer (e.g., a battery) and helps the engine during short-duration high-power demands. Both the engine and the buffer are sized to reduce the sum of the vehicle operational (fuel) and component (engine and buffer) costs. For this purpose, a convex, driving cycle-based vehicle model is derived, enabling the formulation of an underlying optimization problem as a second order cone program. Such a program can be efficiently solved using dedicated numerical tools (for a given gear selection strategy), which provides not only the optimal engine/buffer sizes but also the optimal vehicle control and state trajectories (e.g., compressor power and buffer energy). Finally, the results obtained from a representative, numerical case study are discussed in detail

Model predictive control of a high speed switched reluctance generator system

This paper presents a novel voltage control strategy for the high-speed operation of a Switched Reluctance Generator. It uses a linear Model Predictive Control law based on the average system model. The controller computes the DC-link current needed to achieve the tracking of a desired voltage reference in the presence of an unknown electrical load current. Its output is converted into a pair of excitation angles (turn-on/turn-off) by means of optimized scaling and mapping, while minimizing the dominant high-speed power loss. The constraints on the average DC-link voltage and current signals are directly handled. A numerical example is provided to validate the effectiveness and the robustness of the proposed control scheme

Convex Modeling and Optimization of a Vehicle Powertrain Equipped with a Generator-Turbine Throttle Unit

This paper investigates an internal combustion (gasoline) engine throttled by a generator-turbine unit. Apart from throttling, the purpose of this device is to complement the operation of a conventional car alternator and support its downsizing by introducing an additional source of energy for the electric auxiliaries. Its energy recovery potential is examined by employing a novel, convex approach to modeling and optimization of the resulting vehicle powertrain. For a given gear-shifting strategy, the proposed method allows the computation of optimal control trajectories, e.g., the optimal engine fuel, alternator, and turbine power, as well as of optimal design parameters, i.e., the optimal battery, alternator and turbogenerator size. The conducted numerical case study shows that a generator-turbine throttle unit has a potential to reduce the total operational (fuel) and component (battery, alternator, and turbogenerator) costs by typically 2%-6%, depending on factors such as the engine size and the choice of a driving cycle

Model predictive control of a high speed switched reluctance generator system

\u3cp\u3eThis paper presents a novel voltage control strategy for the high-speed operation of a Switched Reluctance Generator. It uses a linear Model Predictive Control law based on the average system model. The controller computes the DC-link current needed to achieve the tracking of a desired voltage reference in the presence of an unknown electrical load current. Its output is converted into a pair of excitation angles (turn-on/turn-off) by means of optimized scaling and mapping, while minimizing the dominant high-speed power loss. The constraints on the average DC-link voltage and current signals are directly handled. A numerical example is provided to validate the effectiveness and the robustness of the proposed control scheme.\u3c/p\u3

Extremum seeking control with adaptive disturbance feedforward

\u3cp\u3eThis paper presents an extension to the classical gradient-based extremum seeking control for the case when the disturbances responsible for changes in the extremum of a selected performance function are available for measurement. Based on these additional measurements, an adaptive extremum seeking disturbance feedforward is designed that approximates the unknown, static mapping between the disturbances and the optimal inputs. For this purpose, orthogonal, multivariate Tchebyshev polynomials are used. The feedforward enables the extremum seeking to be conducted in the proximity of the extremum thus yielding improvements both in terms of accuracy and increased convergence speed compared to the traditional scheme. Simulation results given for a turbine driven electrical generator system demonstrate the benefits of the presented design.\u3c/p\u3