Reinforcement learning-based control for the thermal management of the battery and occupant compartments of electric vehicles

Zhang, Yan; Huang, Jianglu; He, Liange; Zhao, Donggang; Zhao, Yu

doi:10.1039/d3se01403g

Sustainable Energy Fuels

2024

DOI: 10.1039/d3se01403g

|View full text |Cite

Reinforcement learning-based control for the thermal management of the battery and occupant compartments of electric vehicles

Yan Zhang,

Jianglu Huang,

Liange He

et al.

Abstract: The coupling between the direct-cooled battery thermal management system and the occupant compartment is difficult to control. Therefore, a control strategy based on reinforcement learning is designed to achieve good control results.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Utilizing multilayer perceptron for machine learning diagnosis in phase change material‐based thermal management systems

Arif,

Reddy,

Srividya

et al. 2024

Heat Trans

View full text Add to dashboard Cite

Electric vehicles encounter significant challenges in colder climates due to reduced battery efficiency at low temperatures and increased electricity demand for cabin heating, which impacts vehicle propulsion. This study aims to address these challenges by implementing a thermal management system utilizing Phase Change Materials (PCMs) and validating the performance of a Multilayer Perceptron (MLP) model in predicting PCMs behavior and battery temperature distributions. The study employs an MLP model trained with 160 samples of diverse heat inputs, including pulsating, constant, wiener, discharging, and random temperatures. The model uses these temperatures as inputs and liquid fractions as target values. Performance evaluation is conducted using the MATLAB platform and is benchmarked against existing approaches, such as Long Short‐term Memory (LSTM), spatiotemporal convolutional neural network (CNN), and pooled CNN‐LSTM. The MLP model's accuracy in predicting PCMs phase transitions is validated by comparing predicted liquid fractions with numerically obtained values. Additionally, this study forecasts temperature distributions within a standard battery pack under various discharge scenarios, considering the performance of commercial lithium‐ion batteries. The proposed MLP model demonstrates high efficacy, achieving a correlation of up to 0.999 and root mean squared error below 0.013 compared with numerical results.

show abstract

Utilizing multilayer perceptron for machine learning diagnosis in phase change material‐based thermal management systems

Arif,

Reddy,

Srividya

et al. 2024

Heat Trans

View full text Add to dashboard Cite

show abstract

Synergizing Transfer Learning and Multi-Agent Systems for Thermal Parametrization in Induction Traction Motors

Mehboob,

Fattouh,

Sahoo

2024

Applied Sciences

View full text Add to dashboard Cite

Maintaining optimal temperatures in the critical parts of an induction traction motor is crucial for railway propulsion systems. A reduced-order lumped-parameter thermal network (LPTN) model enables computably inexpensive, accurate temperature estimation; however, it requires empirically based parameter estimation exercises. The calibration process is typically performed in labs in a controlled experimental setting, which is associated with a lot of supervised human efforts. However, the exploration of machine learning (ML) techniques in varied domains has enabled the model parameterization in the drive system outside the laboratory settings. This paper presents an innovative use of a multi-agent reinforcement learning (MARL) approach for the parametrization of an LPTN model. First, a set of reinforcement learning agents are trained to estimate the optimized thermal parameters using the simulated data in several driving cycles (DCs). The selection of a reinforcement learning agent and the level of neurons in the RL model is made based on variability of the driving cycle data. Furthermore, transfer learning is performed on a new driving cycle data collected on the measurement setup. Statistical analysis and clustering techniques are proposed for the selection of an RL agent that has been pre-trained on the historical data. It is established that by synergizing within reinforcement learning techniques, it is possible to refine and adjust the RL learning models to effectively capture the complexities of thermal dynamics. The proposed MARL framework shows its capability to accurately reflect the motor’s thermal behavior under various driving conditions. The transfer learning usage in the proposed approach could yield significant improvement in the accuracy of temperature prediction in the new driving cycles data. This approach is proposed with the aim of developing more adaptive and efficient thermal management strategies for railway propulsion systems.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Reinforcement learning-based control for the thermal management of the battery and occupant compartments of electric vehicles

Abstract: The coupling between the direct-cooled battery thermal management system and the occupant compartment is difficult to control. Therefore, a control strategy based on reinforcement learning is designed to achieve good control results.

Cited by 2 publications

References 41 publications

Utilizing multilayer perceptron for machine learning diagnosis in phase change material‐based thermal management systems

Utilizing multilayer perceptron for machine learning diagnosis in phase change material‐based thermal management systems

Synergizing Transfer Learning and Multi-Agent Systems for Thermal Parametrization in Induction Traction Motors

Contact Info

Product

Resources

About