ENHANCING BATTERY THERMAL MANAGEMENT IN ELECTRIC VEHICLES：A HYBRID DMCOA ALGORITHM AND DEEP NEURAL NETWORK APPROACH
This paper presents an innovative approach to Battery Thermal Management Systems (BTMS) utilizing a hybrid algorithm, the Dwarf Mongoose-based Coati Optimization Algorithm (DMCOA), in conjunction with a deep neural network (DNN). Our objective is to optimize the temperature of lithium-ion batteries...
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| Format: | Article |
| Language: | English |
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Regional Association for Security and crisis management, Belgrade, Serbia
2023-11-01
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| Series: | Operational Research in Engineering Sciences: Theory and Applications |
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| Online Access: | https://oresta.org/menu-script/index.php/oresta/article/view/599 |
| _version_ | 1797193579438800896 |
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| author | Gengqiang Huang Chonlatee Photong |
| author_facet | Gengqiang Huang Chonlatee Photong |
| author_sort | Gengqiang Huang |
| collection | DOAJ |
| description |
This paper presents an innovative approach to Battery Thermal Management Systems (BTMS) utilizing a hybrid algorithm, the Dwarf Mongoose-based Coati Optimization Algorithm (DMCOA), in conjunction with a deep neural network (DNN). Our objective is to optimize the temperature of lithium-ion batteries, particularly in Electric Vehicles (EVs). The DMCOA draws inspiration from cooperative behaviors seen in coatis and dwarf mongooses. It employs advanced strategies, such as cooperative attacks simulation and escape behavior imitation to ensure efficient minimization of cost function. Additionally, a DNN is employed to predict vehicle speed and battery heat production rate under various conditions, enhancing the control of the BTMS. Simulation outcomes demonstrate the effectiveness of the hybrid algorithm in maintaining battery temperatures, with minimal deviation from the target range. Simulation results show that the proposed hybrid algorithm efficiently maintains battery temperatures, with just a 0.3°C average difference from the target and a maximum 1.1°C difference among modules. Additionally, it extends battery lifespan by 0.02%, 0.015%, and 0.01% compared to Fuzzy Logic control (FLC), Artificial Neural Network (ANN) and intelligent model predictive control (IMPC), respectively. It also achieves energy savings of 23%, 25% and 15% compared to the FLC, ANN, and IMPC models. Hence, it is evident that the proposed model holds promise for enhancing battery life span with minimal cost in EVs with its simplicity, efficiency, and robustness.
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| first_indexed | 2024-03-07T14:12:40Z |
| format | Article |
| id | doaj.art-69a74fff972e449abc1afc7c58bc4f2d |
| institution | Directory Open Access Journal |
| issn | 2620-1607 2620-1747 |
| language | English |
| last_indexed | 2024-04-24T05:42:38Z |
| publishDate | 2023-11-01 |
| publisher | Regional Association for Security and crisis management, Belgrade, Serbia |
| record_format | Article |
| series | Operational Research in Engineering Sciences: Theory and Applications |
| spelling | doaj.art-69a74fff972e449abc1afc7c58bc4f2d2024-04-23T19:49:30ZengRegional Association for Security and crisis management, Belgrade, SerbiaOperational Research in Engineering Sciences: Theory and Applications2620-16072620-17472023-11-0162ENHANCING BATTERY THERMAL MANAGEMENT IN ELECTRIC VEHICLES：A HYBRID DMCOA ALGORITHM AND DEEP NEURAL NETWORK APPROACHGengqiang Huang0Chonlatee Photong1Faculty of Engineering, Mahasarakham University, Maha Sarakham 44150, ThailandFaculty of Engineering, Mahasarakham University, Maha Sarakham 44150, Thailand This paper presents an innovative approach to Battery Thermal Management Systems (BTMS) utilizing a hybrid algorithm, the Dwarf Mongoose-based Coati Optimization Algorithm (DMCOA), in conjunction with a deep neural network (DNN). Our objective is to optimize the temperature of lithium-ion batteries, particularly in Electric Vehicles (EVs). The DMCOA draws inspiration from cooperative behaviors seen in coatis and dwarf mongooses. It employs advanced strategies, such as cooperative attacks simulation and escape behavior imitation to ensure efficient minimization of cost function. Additionally, a DNN is employed to predict vehicle speed and battery heat production rate under various conditions, enhancing the control of the BTMS. Simulation outcomes demonstrate the effectiveness of the hybrid algorithm in maintaining battery temperatures, with minimal deviation from the target range. Simulation results show that the proposed hybrid algorithm efficiently maintains battery temperatures, with just a 0.3°C average difference from the target and a maximum 1.1°C difference among modules. Additionally, it extends battery lifespan by 0.02%, 0.015%, and 0.01% compared to Fuzzy Logic control (FLC), Artificial Neural Network (ANN) and intelligent model predictive control (IMPC), respectively. It also achieves energy savings of 23%, 25% and 15% compared to the FLC, ANN, and IMPC models. Hence, it is evident that the proposed model holds promise for enhancing battery life span with minimal cost in EVs with its simplicity, efficiency, and robustness. https://oresta.org/menu-script/index.php/oresta/article/view/599Electric Vehicles (EVs)Battery Thermal ManagementLithium-ion BatteryDeep Neural NetworkOptimization |
| spellingShingle | Gengqiang Huang Chonlatee Photong ENHANCING BATTERY THERMAL MANAGEMENT IN ELECTRIC VEHICLES：A HYBRID DMCOA ALGORITHM AND DEEP NEURAL NETWORK APPROACH Operational Research in Engineering Sciences: Theory and Applications Electric Vehicles (EVs) Battery Thermal Management Lithium-ion Battery Deep Neural Network Optimization |
| title | ENHANCING BATTERY THERMAL MANAGEMENT IN ELECTRIC VEHICLES：A HYBRID DMCOA ALGORITHM AND DEEP NEURAL NETWORK APPROACH |
| title_full | ENHANCING BATTERY THERMAL MANAGEMENT IN ELECTRIC VEHICLES：A HYBRID DMCOA ALGORITHM AND DEEP NEURAL NETWORK APPROACH |
| title_fullStr | ENHANCING BATTERY THERMAL MANAGEMENT IN ELECTRIC VEHICLES：A HYBRID DMCOA ALGORITHM AND DEEP NEURAL NETWORK APPROACH |
| title_full_unstemmed | ENHANCING BATTERY THERMAL MANAGEMENT IN ELECTRIC VEHICLES：A HYBRID DMCOA ALGORITHM AND DEEP NEURAL NETWORK APPROACH |
| title_short | ENHANCING BATTERY THERMAL MANAGEMENT IN ELECTRIC VEHICLES：A HYBRID DMCOA ALGORITHM AND DEEP NEURAL NETWORK APPROACH |
| title_sort | enhancing battery thermal management in electric vehicles a hybrid dmcoa algorithm and deep neural network approach |
| topic | Electric Vehicles (EVs) Battery Thermal Management Lithium-ion Battery Deep Neural Network Optimization |
| url | https://oresta.org/menu-script/index.php/oresta/article/view/599 |
| work_keys_str_mv | AT gengqianghuang enhancingbatterythermalmanagementinelectricvehiclesahybriddmcoaalgorithmanddeepneuralnetworkapproach AT chonlateephotong enhancingbatterythermalmanagementinelectricvehiclesahybriddmcoaalgorithmanddeepneuralnetworkapproach |