1,646 research outputs found

    Progress and summary of reinforcement learning on energy management of MPS-EV

    Full text link
    The high emission and low energy efficiency caused by internal combustion engines (ICE) have become unacceptable under environmental regulations and the energy crisis. As a promising alternative solution, multi-power source electric vehicles (MPS-EVs) introduce different clean energy systems to improve powertrain efficiency. The energy management strategy (EMS) is a critical technology for MPS-EVs to maximize efficiency, fuel economy, and range. Reinforcement learning (RL) has become an effective methodology for the development of EMS. RL has received continuous attention and research, but there is still a lack of systematic analysis of the design elements of RL-based EMS. To this end, this paper presents an in-depth analysis of the current research on RL-based EMS (RL-EMS) and summarizes the design elements of RL-based EMS. This paper first summarizes the previous applications of RL in EMS from five aspects: algorithm, perception scheme, decision scheme, reward function, and innovative training method. The contribution of advanced algorithms to the training effect is shown, the perception and control schemes in the literature are analyzed in detail, different reward function settings are classified, and innovative training methods with their roles are elaborated. Finally, by comparing the development routes of RL and RL-EMS, this paper identifies the gap between advanced RL solutions and existing RL-EMS. Finally, this paper suggests potential development directions for implementing advanced artificial intelligence (AI) solutions in EMS

    Indoor wireless communications and applications

    Get PDF
    Chapter 3 addresses challenges in radio link and system design in indoor scenarios. Given the fact that most human activities take place in indoor environments, the need for supporting ubiquitous indoor data connectivity and location/tracking service becomes even more important than in the previous decades. Specific technical challenges addressed in this section are(i), modelling complex indoor radio channels for effective antenna deployment, (ii), potential of millimeter-wave (mm-wave) radios for supporting higher data rates, and (iii), feasible indoor localisation and tracking techniques, which are summarised in three dedicated sections of this chapter

    Stochastic model predictive control for energy management of power-split plug-in hybrid electric vehicles based on reinforcement learning

    Get PDF
    In this paper, a stochastic model predictive control (MPC) method based on reinforcement learning is proposed for energy management of plug-in hybrid electric vehicles (PHEVs). Firstly, the power transfer of each component in a power-split PHEV is described in detail. Then an effective and convergent reinforcement learning controller is trained by the Q-learning algorithm according to the driving power distribution under multiple driving cycles. By constructing a multi-step Markov velocity prediction model, the reinforcement learning controller is embedded into the stochastic MPC controller to determine the optimal battery power in predicted time domain. Numerical simulation results verify that the proposed method achieves superior fuel economy that is close to that by stochastic dynamic programming method. In addition, the effective state of charge tracking in terms of different reference trajectories highlight that the proposed method is effective for online application requiring a fast calculation speed

    Design, Modeling and Control of a Thermal Management System for Hybrid Electric Vehicles

    Get PDF
    Hybrid electric vehicle (HEV) technology has evolved in the last two decades to become economically feasible for mass produced automobiles. With the integration of a lithium battery pack and electric motors, HEVs offer a significantly higher fuel efficiency than traditional vehicles that are driven solely by an internal combustion engine. However, the additional HEV components also introduce new challenges for the powertrain thermal management system design. In addition to the common internal combustion engine, the battery pack, the generator(s), as well as the electric motor(s) are now widely applied in the HEVs and have become new heat sources and they also require proper thermal management. Conventional cooling systems have been typically equipped with a belt driven water pump and radiator fan, as well as other mechanical actuators such as the thermostat valve. The operation of these components is generally determined by the engine speed. This open-loop cooling strategy has a low efficiency and suffers the risk of over-cooling the coolant and components within the system. In advanced thermal management systems, the mechanical elements are upgraded by computer controlled actuators including a servo-motor driven pump, variable speed fans, a smart thermostat, and an electric motor driven compressor. These electrified actuators offer the opportunity to improve temperature tracking and reduce parasitic losses. This dissertation investigates a HEV powertrain thermal management system featuring computer controlled cooling system actuators. A suite of mathematical models have been created to describe the thermal behaviour of the HEV powertrain components. Model based controllers were developed for the vehicle\u27s cooling systems including the battery pack, electric motors, and internal combustion engine. Optimal control theory has been applied to determine the ideal battery cooling air temperature and the desired heat removal rate on e-motor cooling surface. A model predictive controller(MPC) was developed to regulate the refrigerant compressor and track the battery cooling air temperature. A series of Lyapunov-based nonlinear controllers have been implemented to regulate the coolant pumps and radiator fans in the cooling systems for the engine and e-motors. Representative numerical results are presented and discussed. Overall, the proposed control strategies have demonstrated the effectiveness in improving both the temperature tracking performance and the cooling system power consumption reduction. The peak temperature error in the selected A123 battery core can be tracked within 0.25 C of the target; a 50% reduction of the vapor compression system energy consumption can be obtained by properly designing the cooling air flow structure. Similarly, the cooling system of HEV electric motors shows that the machine internal peak temperature can be tracked to the target value with a maximum error of 3.9 C and an average error of 0.13 C. A 70% to 81% cooling system energy consumption reduction can be achieved under different driving cycle comparing to classical controller applied to maintain a similar level of hotspot temperature stabilization. The proposed optimal nonlinear controller tracks the engine coolant temperature with an average error of 0.35 C and at least 13% reduction in engine cooling power. Further, a close analysis on the cooling system energy consumption reduction has been conducted with a heat exchanger simulation tool established for cooling system design optimization. This research has developed the basis for the holistic control of HEV powertrain thermal management systems by including a suite of model based nonlinear controllers to simultaneously regulate the cooling actuators for the battery pack, e-motors, and conventional internal combustion engine. Numerical studies has been conducted with a high fidelity HEV model under real driving cycles to demonstrate the advantages of introducing advanced control theory into multi-mode vehicle drive systems
    corecore