Electric motorcycles (EMs) are gaining popularity in densely populated Asian countries, offering environmentally friendly solutions to combat traffic-related pollution. Governments and authorities are eager to promote EMs to reduce reliance on traditional fuel-based motorcycles. While prior research has explored the potential impacts of EMs, limited attention has been given to the adoption intentions of the Pakistani public. This study investigates the factors influencing the behavioral intentions of adopting EMs in Pakistan, employing an extended technology acceptance model (TAM) framework. The extended model incorporates perceived values and environmental concerns, along with perceived usefulness and perceived ease of use, to assess their impact on EM adoption intentions. Based on data collected from 228 respondents in Karachi, Pakistan, structural equation models were estimated to identify significant factors affecting EM adoption. Findings highlight the substantial influence of perceived value and environmental concern on behavioral intentions, with perceived ease of use playing a mediated role through perceived usefulness. Results suggest that effective marketing and user-friendly EM designs, coupled with well-crafted policies and education, can substantially boost EM adoption by the public, facilitating a shift toward sustainable transportation alternatives.
{"title":"Adoption of Electric Motorcycles in Pakistan: A Technology Acceptance Model Perspective","authors":"Sajan Shaikh, Mir Aftab Hussain Talpur, Farrukh Baig, Fariha Tariq, Shabir Hussain Khahro","doi":"10.3390/wevj14100278","DOIUrl":"https://doi.org/10.3390/wevj14100278","url":null,"abstract":"Electric motorcycles (EMs) are gaining popularity in densely populated Asian countries, offering environmentally friendly solutions to combat traffic-related pollution. Governments and authorities are eager to promote EMs to reduce reliance on traditional fuel-based motorcycles. While prior research has explored the potential impacts of EMs, limited attention has been given to the adoption intentions of the Pakistani public. This study investigates the factors influencing the behavioral intentions of adopting EMs in Pakistan, employing an extended technology acceptance model (TAM) framework. The extended model incorporates perceived values and environmental concerns, along with perceived usefulness and perceived ease of use, to assess their impact on EM adoption intentions. Based on data collected from 228 respondents in Karachi, Pakistan, structural equation models were estimated to identify significant factors affecting EM adoption. Findings highlight the substantial influence of perceived value and environmental concern on behavioral intentions, with perceived ease of use playing a mediated role through perceived usefulness. Results suggest that effective marketing and user-friendly EM designs, coupled with well-crafted policies and education, can substantially boost EM adoption by the public, facilitating a shift toward sustainable transportation alternatives.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135739156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the increasingly serious problem of environmental pollution, new energy vehicles have become a hot spot in today’s research. The lithium-ion battery has become the mainstream power battery of new energy vehicles as it has the advantages of long service life, high-rated voltage, low self-discharge rate, etc. The battery management system is the key part that ensures the efficient and safe operation of the vehicle as well as the long life of the power battery. The accurate estimation of the power battery state directly affects the whole vehicle’s performance. As a result, this paper established a lithium-ion battery charge state estimation model based on BP, PSO-BP and LSTM neural networks, which tried to combine the PSO algorithm with the LSTM algorithm. The particle swarm algorithm was utilized to obtain the optimal parameters of the model in the process of repetitive iteration so as to establish the PSO-LSTM prediction model. The superiority of the LSTM neural network model in SOC estimation was demonstrated by comparing the estimation accuracies of BP, PSO-BP and LSTM neural networks. The comparative analysis under constant flow conditions in the laboratory showed that the PSO-LSTM neural network predicts SOC more accurately than BP, PSO-BP and LSTM neural networks. The comparative analysis under DST and US06 operating conditions showed that the PSO-LSTM neural network has a greater prediction accuracy for SOC than the LSTM neural network.
{"title":"Research on SOC Estimation Method for Lithium-Ion Batteries Based on Neural Network","authors":"Chuanwei Zhang, Xusheng Xu, Yikun Li, Jing Huang, Chenxi Li, Weixin Sun","doi":"10.3390/wevj14100275","DOIUrl":"https://doi.org/10.3390/wevj14100275","url":null,"abstract":"With the increasingly serious problem of environmental pollution, new energy vehicles have become a hot spot in today’s research. The lithium-ion battery has become the mainstream power battery of new energy vehicles as it has the advantages of long service life, high-rated voltage, low self-discharge rate, etc. The battery management system is the key part that ensures the efficient and safe operation of the vehicle as well as the long life of the power battery. The accurate estimation of the power battery state directly affects the whole vehicle’s performance. As a result, this paper established a lithium-ion battery charge state estimation model based on BP, PSO-BP and LSTM neural networks, which tried to combine the PSO algorithm with the LSTM algorithm. The particle swarm algorithm was utilized to obtain the optimal parameters of the model in the process of repetitive iteration so as to establish the PSO-LSTM prediction model. The superiority of the LSTM neural network model in SOC estimation was demonstrated by comparing the estimation accuracies of BP, PSO-BP and LSTM neural networks. The comparative analysis under constant flow conditions in the laboratory showed that the PSO-LSTM neural network predicts SOC more accurately than BP, PSO-BP and LSTM neural networks. The comparative analysis under DST and US06 operating conditions showed that the PSO-LSTM neural network has a greater prediction accuracy for SOC than the LSTM neural network.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135830504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Currently, in the process of autonomous parking, the algorithm detection accuracy and rate of parking spaces are low due to the diversity of parking scenes, changes in lighting conditions, and other unfavorable factors. An improved algorithm based on YOLOv5-OBB is proposed to reduce the computational effort of the model and increase the speed of model detection. Firstly, the backbone module is optimized, the Focus module and SSP (Selective Spatial Perception) module are replaced with the general convolution and SSPF (Selective Search Proposals Fusion) modules, and the GELU activation function is introduced to reduce the number of model parameters and enhance model learning. Secondly, the RFB (Receptive Field Block) module is added to fuse different feature modules and increase the perceptual field to optimize the small target detection. After that, the CA (coordinate attention) mechanism is introduced to enhance the feature representation capability. Finally, the post-processing is optimized using spatial location correlation to improve the accuracy of the vehicle position and bank angle detection. The implementation results show that by using the improved method proposed in this paper, the FPS of the model is improved by 2.87, algorithm size is reduced by 1 M, and the mAP is improved by 8.4% on the homemade dataset compared with the original algorithm. The improved model meets the requirements of perceived accuracy and speed of parking spaces in autonomous parking.
目前，在自主停车过程中，由于停车场景的多样性、光照条件的变化等不利因素，算法对停车位的检测准确率和检测率较低。为了减少模型的计算量，提高模型检测的速度，提出了一种基于YOLOv5-OBB的改进算法。首先，对骨干模块进行优化，将Focus模块和选择性空间感知(SSP)模块替换为通用卷积(general convolution)和选择性搜索建议融合(Selective Search Proposals Fusion)模块，并引入GELU激活函数，减少模型参数数量，增强模型学习能力;其次，加入RFB (Receptive Field Block)模块，融合不同特征模块，增加感知场，优化小目标检测;在此基础上，引入CA (coordinate attention)机制来增强特征表示能力。最后，利用空间位置相关优化后处理，提高车辆位置和倾斜角检测的精度。实现结果表明，采用本文提出的改进方法，在自制数据集上模型的FPS比原算法提高了2.87，算法大小减少了1 M, mAP提高了8.4%。改进后的模型满足自主停车对车位感知精度和速度的要求。
{"title":"Autonomous Parking Space Detection for Electric Vehicles Based on Improved YOLOV5-OBB Algorithm","authors":"Zhaoyan Chen, Xiaolan Wang, Weiwei Zhang, Guodong Yao, Dongdong Li, Li Zeng","doi":"10.3390/wevj14100276","DOIUrl":"https://doi.org/10.3390/wevj14100276","url":null,"abstract":"Currently, in the process of autonomous parking, the algorithm detection accuracy and rate of parking spaces are low due to the diversity of parking scenes, changes in lighting conditions, and other unfavorable factors. An improved algorithm based on YOLOv5-OBB is proposed to reduce the computational effort of the model and increase the speed of model detection. Firstly, the backbone module is optimized, the Focus module and SSP (Selective Spatial Perception) module are replaced with the general convolution and SSPF (Selective Search Proposals Fusion) modules, and the GELU activation function is introduced to reduce the number of model parameters and enhance model learning. Secondly, the RFB (Receptive Field Block) module is added to fuse different feature modules and increase the perceptual field to optimize the small target detection. After that, the CA (coordinate attention) mechanism is introduced to enhance the feature representation capability. Finally, the post-processing is optimized using spatial location correlation to improve the accuracy of the vehicle position and bank angle detection. The implementation results show that by using the improved method proposed in this paper, the FPS of the model is improved by 2.87, algorithm size is reduced by 1 M, and the mAP is improved by 8.4% on the homemade dataset compared with the original algorithm. The improved model meets the requirements of perceived accuracy and speed of parking spaces in autonomous parking.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135790321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The algorithm function designed in this paper can make a car maintain stability during automatic vehicle hold through the model input of multi-level target fluid pressure combined with slope judgment modules of different levels after the automatic vehicle hold software works. At the same time, a complete parking function module is designed, which can monitor the whole parking process in real time. Through the design of this function, the functional diversity of the electro-hydraulic braking system can be increased. When judging that the driver intends to start, the automatic vehicle hold system will automatically release the fluid pressure according to the opening of the accelerator pedal pressed by the driver so that the vehicle does not happen to brake when the vehicle starts in the slippery slope condition. Finally, real vehicle verification proves that the function can effectively meet the parking requirements and start on the flat and on a ramp. Also, it can effectively control the vehicle according to the driver’s driving intention.
{"title":"Functional Model of an Automatic Vehicle Hold Based on an Electro-Hydraulic Braking System","authors":"Yufeng Zhou, Bo Huang, Jiahao Liu, Tianjun Zhou","doi":"10.3390/wevj14100277","DOIUrl":"https://doi.org/10.3390/wevj14100277","url":null,"abstract":"The algorithm function designed in this paper can make a car maintain stability during automatic vehicle hold through the model input of multi-level target fluid pressure combined with slope judgment modules of different levels after the automatic vehicle hold software works. At the same time, a complete parking function module is designed, which can monitor the whole parking process in real time. Through the design of this function, the functional diversity of the electro-hydraulic braking system can be increased. When judging that the driver intends to start, the automatic vehicle hold system will automatically release the fluid pressure according to the opening of the accelerator pedal pressed by the driver so that the vehicle does not happen to brake when the vehicle starts in the slippery slope condition. Finally, real vehicle verification proves that the function can effectively meet the parking requirements and start on the flat and on a ramp. Also, it can effectively control the vehicle according to the driver’s driving intention.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135829380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aiming at the problem of insufficient tracking accuracy caused by object occlusion in the process of multi-object tracking, this paper proposes a multi-order semantic fusion pedestrian multi-object tracking network. Firstly, the feature pyramid attention module is used in the backbone network to enlarge the receptive field and obtain more abundant feature information to improve the detection accuracy of different scale objects. Secondly, a size-aware module is integrated into the pedestrian re-identification branch network to fuse semantic features from different resolutions and extract more basic pedestrian features, thereby improving the tracking accuracy. Finally, the detection head is reconstructed and the small object detection layer is fused to make the proposed network adapt to objects of different sizes. Experiments on the MOT16 and MOT17 datasets show that the multi-object tracking accuracy of the proposed network reaches 75.4% (MOT16) and 74.3% (MOT17), which effectively deals with the problem of low tracking accuracy caused by occlusion in the field of autonomous driving, and achieves good tracking results. The network proposed in this paper improves the tracking accuracy of pedestrians and provides a basis for further practical applications.
{"title":"Research on Pedestrian Multi-Object Tracking Network Based on Multi-Order Semantic Fusion","authors":"Cong Liu, Chao Han","doi":"10.3390/wevj14100272","DOIUrl":"https://doi.org/10.3390/wevj14100272","url":null,"abstract":"Aiming at the problem of insufficient tracking accuracy caused by object occlusion in the process of multi-object tracking, this paper proposes a multi-order semantic fusion pedestrian multi-object tracking network. Firstly, the feature pyramid attention module is used in the backbone network to enlarge the receptive field and obtain more abundant feature information to improve the detection accuracy of different scale objects. Secondly, a size-aware module is integrated into the pedestrian re-identification branch network to fuse semantic features from different resolutions and extract more basic pedestrian features, thereby improving the tracking accuracy. Finally, the detection head is reconstructed and the small object detection layer is fused to make the proposed network adapt to objects of different sizes. Experiments on the MOT16 and MOT17 datasets show that the multi-object tracking accuracy of the proposed network reaches 75.4% (MOT16) and 74.3% (MOT17), which effectively deals with the problem of low tracking accuracy caused by occlusion in the field of autonomous driving, and achieves good tracking results. The network proposed in this paper improves the tracking accuracy of pedestrians and provides a basis for further practical applications.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135458732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Johannes Human Giliomee, Marthinus Johannes Booysen
The inevitable electrification of the sub-Saharan African paratransit system poses substantial threats to an already crippled electricity supply network. The integration of any electric vehicle fleet in this region will require in-depth analyses and understanding of the grid impact due to charging. This allows informative decisions for sufficient planning to be made for the required network infrastructure or the implementation of applicable ‘load-shifting’ techniques. This paper presents Grid-Sim, a software tool that enables comprehensive analysis of the grid impact implications of electrifying vehicle fleets. Grid-Sim is applied to assess the load profiles, energy demand, load-shifting techniques, and associated emissions for two charging stations serving an electrified minibus taxi fleet of 202 vehicles in Johannesburg, South Africa. It is found that the current operation patterns result in a peak grid power draw of 12 kW/taxi, grid-drawn energy of 87.4 kWh/taxi/day, and, subsequently, 93 kg CO2/taxi/day of emissions. However, when using the built-in option of including external batteries and a solar charging station, the average peak power draw reduces by 66%, and both grid-drawn energy and emissions reduce by 58%.
{"title":"Grid-Sim: Simulating Electric Fleet Charging with Renewable Generation and Battery Storage","authors":"Johannes Human Giliomee, Marthinus Johannes Booysen","doi":"10.3390/wevj14100274","DOIUrl":"https://doi.org/10.3390/wevj14100274","url":null,"abstract":"The inevitable electrification of the sub-Saharan African paratransit system poses substantial threats to an already crippled electricity supply network. The integration of any electric vehicle fleet in this region will require in-depth analyses and understanding of the grid impact due to charging. This allows informative decisions for sufficient planning to be made for the required network infrastructure or the implementation of applicable ‘load-shifting’ techniques. This paper presents Grid-Sim, a software tool that enables comprehensive analysis of the grid impact implications of electrifying vehicle fleets. Grid-Sim is applied to assess the load profiles, energy demand, load-shifting techniques, and associated emissions for two charging stations serving an electrified minibus taxi fleet of 202 vehicles in Johannesburg, South Africa. It is found that the current operation patterns result in a peak grid power draw of 12 kW/taxi, grid-drawn energy of 87.4 kWh/taxi/day, and, subsequently, 93 kg CO2/taxi/day of emissions. However, when using the built-in option of including external batteries and a solar charging station, the average peak power draw reduces by 66%, and both grid-drawn energy and emissions reduce by 58%.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135459016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the development of autonomous driving technology, truck platooning control has become a reality. Truck platooning can improve road capacity by maintaining a minor headway. Platooning systems can significantly reduce fuel consumption and emissions, especially for trucks. In this study, we designed a Platoon-MAPPO algorithm to implement truck platooning control based on multi-agent reinforcement learning for a platooning facing an on-ramp scenario on highway. A centralized training, decentralized execution algorithm was used in this paper. Each truck only computes its actions, avoiding the data computation delay problem caused by centralized computation. Each truck considers the truck status in front of and behind itself, maximizing the overall gain of the platooning and improving the global operational efficiency. In terms of performance evaluation, we used the traditional rule-based platooning following model as a benchmark. To ensure fairness, the model used the same network structure and traffic scenario as our proposed model. The simulation results show that the algorithm proposed in this paper has good performance and improves the overall efficiency of the platoon while guaranteeing traffic safety. The average energy consumption decreased by 14.8%, and the road occupancy rate decreased by 43.3%.
{"title":"Research on Reinforcement-Learning-Based Truck Platooning Control Strategies in Highway On-Ramp Regions","authors":"Jiajia Chen, Zheng Zhou, Yue Duan, Biao Yu","doi":"10.3390/wevj14100273","DOIUrl":"https://doi.org/10.3390/wevj14100273","url":null,"abstract":"With the development of autonomous driving technology, truck platooning control has become a reality. Truck platooning can improve road capacity by maintaining a minor headway. Platooning systems can significantly reduce fuel consumption and emissions, especially for trucks. In this study, we designed a Platoon-MAPPO algorithm to implement truck platooning control based on multi-agent reinforcement learning for a platooning facing an on-ramp scenario on highway. A centralized training, decentralized execution algorithm was used in this paper. Each truck only computes its actions, avoiding the data computation delay problem caused by centralized computation. Each truck considers the truck status in front of and behind itself, maximizing the overall gain of the platooning and improving the global operational efficiency. In terms of performance evaluation, we used the traditional rule-based platooning following model as a benchmark. To ensure fairness, the model used the same network structure and traffic scenario as our proposed model. The simulation results show that the algorithm proposed in this paper has good performance and improves the overall efficiency of the platoon while guaranteeing traffic safety. The average energy consumption decreased by 14.8%, and the road occupancy rate decreased by 43.3%.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135459450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Large-format lithium-ion (Li-ion) batteries are increasingly applied in energy storage systems for electric vehicles, owing to their flexible shape design, lighter weight, higher specific energy, and compact layouts. Nevertheless, the large thermal gradient of Li-ion batteries leads to performance degradation and irreversible safety issues. The difference in the highest temperature position at various operational modes makes accurate temperature monitoring complicated. Accordingly, a full understanding of the temperature inconsistency of large-format Li-ion batteries is crucial. In this study, these inconsistent characteristics are analyzed by establishing an electrothermal model and conducting experiments based on an 8-Ah pouch-type ternary Li-ion battery with contraposition tabs. Regarding the characteristic of inhomogeneous temperature distribution, the analysis results demonstrate that it is primarily attributable to the uneven heat generation within the battery system and the effects of the two tabs. For the evolution of the highest temperature position, this study compares the maximum temperature rise of the positive tab and main battery body. The results illustrate that the operating temperature has a greater impact on the maximum temperature rise of the main battery body since its resistance strongly depends on the operating temperature compared to the positive and negative tabs. In addition, the electrothermal model is expected to be employed for the battery thermal management system (BTMS) to mitigate the battery temperature inconsistency.
{"title":"Research on Temperature Inconsistency of Large-Format Lithium-Ion Batteries Based on the Electrothermal Model","authors":"Chao Yu, Jiangong Zhu, Xuezhe Wei, Haifeng Dai","doi":"10.3390/wevj14100271","DOIUrl":"https://doi.org/10.3390/wevj14100271","url":null,"abstract":"Large-format lithium-ion (Li-ion) batteries are increasingly applied in energy storage systems for electric vehicles, owing to their flexible shape design, lighter weight, higher specific energy, and compact layouts. Nevertheless, the large thermal gradient of Li-ion batteries leads to performance degradation and irreversible safety issues. The difference in the highest temperature position at various operational modes makes accurate temperature monitoring complicated. Accordingly, a full understanding of the temperature inconsistency of large-format Li-ion batteries is crucial. In this study, these inconsistent characteristics are analyzed by establishing an electrothermal model and conducting experiments based on an 8-Ah pouch-type ternary Li-ion battery with contraposition tabs. Regarding the characteristic of inhomogeneous temperature distribution, the analysis results demonstrate that it is primarily attributable to the uneven heat generation within the battery system and the effects of the two tabs. For the evolution of the highest temperature position, this study compares the maximum temperature rise of the positive tab and main battery body. The results illustrate that the operating temperature has a greater impact on the maximum temperature rise of the main battery body since its resistance strongly depends on the operating temperature compared to the positive and negative tabs. In addition, the electrothermal model is expected to be employed for the battery thermal management system (BTMS) to mitigate the battery temperature inconsistency.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"2014 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135457897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As an important component of new energy vehicles, the safety of lithium-ion batteries has attracted extensive attention. To reveal the mechanism and characteristics of ternary lithium-ion batteries under different trigger modes, an experimental system was established. The effects of different trigger modes on battery surface temperature, battery internal temperature, injection time, and battery voltage were analyzed. Among them, acupuncture, overheating, and overcharging are used as trigger conditions for mechanical, thermal, and electrical abuse. The results show that the injection time and surface peak temperature are positively correlated with the energy input before thermal runaway. Before the cell triggers abuse, the more input energy, the higher the cell surface temperature, the more serious the thermal runaway, and the higher the damage to the surrounding battery system. Under the same conditions, the intensity and damage degree of overcharge thermal runaway are greater than those of internal short circuit and overtemperature. The abnormal change of voltage suddenly rising and rapidly falling can be used as a condition to judge whether overcharge thermal runaway occurs. Finally, according to the temperature curves at different positions, the thermal diffusion law under different abuse conditions is summarized, which provides a basis for the safety design of the battery module.
{"title":"Experimental Study on Effects of Triggering Modes on Thermal Runaway Characteristics of Lithium-Ion Battery","authors":"Yuanjin Dong, Jian Meng, Xiaomei Sun, Peidong Zhao, Peng Sun, Bin Zheng","doi":"10.3390/wevj14100270","DOIUrl":"https://doi.org/10.3390/wevj14100270","url":null,"abstract":"As an important component of new energy vehicles, the safety of lithium-ion batteries has attracted extensive attention. To reveal the mechanism and characteristics of ternary lithium-ion batteries under different trigger modes, an experimental system was established. The effects of different trigger modes on battery surface temperature, battery internal temperature, injection time, and battery voltage were analyzed. Among them, acupuncture, overheating, and overcharging are used as trigger conditions for mechanical, thermal, and electrical abuse. The results show that the injection time and surface peak temperature are positively correlated with the energy input before thermal runaway. Before the cell triggers abuse, the more input energy, the higher the cell surface temperature, the more serious the thermal runaway, and the higher the damage to the surrounding battery system. Under the same conditions, the intensity and damage degree of overcharge thermal runaway are greater than those of internal short circuit and overtemperature. The abnormal change of voltage suddenly rising and rapidly falling can be used as a condition to judge whether overcharge thermal runaway occurs. Finally, according to the temperature curves at different positions, the thermal diffusion law under different abuse conditions is summarized, which provides a basis for the safety design of the battery module.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135472447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to address the problems associated with low detection accuracy, weak detection ability of small targets, insufficiently obvious differentiation of colors, and inability to accurately locate the actual position of the target object in the Formula Student Autonomous China, the YOLOv5s algorithm is improved by adding coordinate attention, modifying the color space transformation module, and adding a normalized Gaussian Wasserstein distance module and a monocular camera distance measurement module. Finally, it is experimentally verified that by adding and modifying the above modules, the YOLOv5s algorithm’s precision is improved by 6.9%, recall by 4.4%, and mean average precision by 4.9%; although the detection frame rate decreases, it still meets the requirement. Monocular camera distance measurement has a maximum error of 5.64% within 20 m in the Z-direction and 5.33% in the X-direction.
{"title":"Research on Cone Bucket Detection Algorithm Based on Improved YOLOv5s","authors":"Jiyue Zhuo, Gang Li, Yang He","doi":"10.3390/wevj14100269","DOIUrl":"https://doi.org/10.3390/wevj14100269","url":null,"abstract":"In order to address the problems associated with low detection accuracy, weak detection ability of small targets, insufficiently obvious differentiation of colors, and inability to accurately locate the actual position of the target object in the Formula Student Autonomous China, the YOLOv5s algorithm is improved by adding coordinate attention, modifying the color space transformation module, and adding a normalized Gaussian Wasserstein distance module and a monocular camera distance measurement module. Finally, it is experimentally verified that by adding and modifying the above modules, the YOLOv5s algorithm’s precision is improved by 6.9%, recall by 4.4%, and mean average precision by 4.9%; although the detection frame rate decreases, it still meets the requirement. Monocular camera distance measurement has a maximum error of 5.64% within 20 m in the Z-direction and 5.33% in the X-direction.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135580033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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