Abstract
This study applied a model predictive control (MPC) framework to solve the cruising control problem of a series hydraulic hybrid vehicle (SHHV). The controller not only regulates vehicle velocity, but also engine torque, engine speed, and accumulator pressure to their corresponding reference values. At each time step, a quadratic programming problem is solved within a predictive horizon to obtain the optimal control inputs. The objective is to minimize the output error. This approach ensures that the components operate at high efficiency thereby improving the total efficiency of the system. The proposed SHHV control system was evaluated under urban and highway driving conditions. By handling constraints and input-output interactions, the MPC-based control system ensures that the system operates safely and efficiently. The fuel economy of the proposed control scheme shows a noticeable improvement in comparison with the PID-based system, in which three Proportional-Integral-Derivative (PID) controllers are used for cruising control.
Highlights
Forecasted fossil fuel depletion and growing environmental concern have stimulated the development of vehicles with ever-higher efficiency
Design parameters of the standard model predictive control (MPC) include penalty weights, prediction horizon, and control horizon. These parameters can be tuned using simulations and observations for the series hydraulic hybrid vehicle (SHHV) working under different driving conditions
The results indicate that selecting suitable weight factors enables the MPC-based control system to designate various outputs in accordance with their reference values
Summary
Forecasted fossil fuel depletion and growing environmental concern have stimulated the development of vehicles with ever-higher efficiency. By decoupling the engine from the wheels, the series hydraulic hybrid architecture adds more degree-of-freedom to engine control In this scenario, engine torque, engine speed, and accumulator pressure are control variables in addition to vehicle velocity. In [7], engine speed is controlled using a PI controller, which uses vehicle velocity error as the input. This study presents a systematic illustration of a controller for the tracking problem in a hydraulic hybrid powertrain system using a linear MPC methodology. The supervisory controller determines optimal reference values for engine speed, engine torque, and accumulator pressure to satisfy the demand for a given vehicle velocity. The MPC regulates the engine, the variable displacement pump, and the pump/motor to ensure that vehicle velocity, engine speed, engine torque, and accumulator pressure adhere to their corresponding reference values.
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