Energy management of P2 hybrid electric vehicle based on event triggered nonlinear model predictive control and deep Q network

Abstract

The energy management problem of a P2 hybrid electric vehicle (HEV) involves determining how to allocate the available energy between the internal combustion engine and the electric motor, as well as how to use the energy stored in the battery. The goal of the energy management system is to minimize the fuel consumption of the vehicle while maintaining its performance and drivability. The energy management problem of a P2 HEV is challenging because it involves multiple objectives and constraints, as well as uncertain and varying driving conditions. The system must balance the power demand of the vehicle with the available power from the engine and battery, while also considering factors such as the state of charge of the battery, the efficiency of the components, and the driving cycle of the vehicle. In this study, the P2 Hybrid Electric Vehicle (HEV) model in Simscape includes components such as the internal combustion engine, electric motor, battery, transmission, and vehicle dynamics. The Kia Niro 2018 vehicle specification provides information about the characteristics of these components, such as their power ratings, efficiencies, and physical dimensions. By combining this information with the Simscape model, the behavior of the P2 HEV is simulated under different driving conditions. The Simscape model is based on physical equations and principles, which means that it provides accurate and reliable predictions of the behavior of the P2 HEV. The model is used to analyze the performance of the vehicle under different scenarios, such as different driving cycles or changes in environmental conditions. By using the Kia Niro 2018 vehicle specification as a reference, the P2 HEV model is validated and adjusted to improve its accuracy. After that, to track the desired velocity profile for a P2 hybrid electric vehicle (HEV) based on the World Harmonized Light Vehicle Test Procedure (WLTP), first model predictive controller (MPC) is implemented. The MPC uses a mathematical model of the vehicle dynamics and powertrain components to predict future behavior over a certain time horizon, taking into account acceleration limits according to ISO 2631-5. The reference signal is determined based on the WLTP standard velocity profile, and an objective function is defined to minimize deviation from the reference signal. In addition to tracking the desired velocity profile, the torque distribution between the engine and motor in a P2 HEV is controlled using a second MPC. The MPC uses a mathematical model of the vehicle's powertrain components to predict future behavior over a certain time horizon, taking into account physical limits of the battery, engine, and motor. The objective of this MPC is to distribute the torque between the engine and motor in an optimal way to achieve the desired performance metrics, such as minimizing power losses. Constraints are established on the system, such as maximum and minimum torque of the engine and electric motor, state of charge of the battery, and current limits of the battery and total torque equality. To decrease the computational cost, an event-triggered mechanism is constructed in a P2 HEV energy management system using a Deep Q Network (DQN) algorithm. This mechanism triggers the model predictive controllers only when needed, reducing the computational burden and improving the energy efficiency of the system. The DQN algorithm is used to learn a policy that determines when to trigger the torque distribution MPC based on the current state of the system. The algorithm uses a neural network to estimate the value function and select actions that minimize the expected long-term cost. The event-triggered mechanism provides a flexible and adaptive approach to energy management in the P2 HEV, allowing for real-time adjustments based on changing driving conditions. The use of DQN allows for efficient and effective decision-making, improving the overall performance and efficiency of the P2 HEV. As a last, in a P2 hybrid electric vehicle (HEV) energy management system with two model predictive controllers (MPCs), the weights of the second MPC's cost function are trained using a deep Q-network (DQN) algorithm. This approach allows for the optimal distribution of torque between the engine and motor, taking into account physical limits of the battery, engine, and motor, as well as other desired performance metrics. By adjusting the weight of the cost function based on the current state of the system, the P2 HEV achieves optimal energy management and improved performance and efficiency. The use of DQN allows for efficient and effective decision-making, reducing the computational burden and improving the overall performance of the system.