Pub Date : 2024-04-18DOI: 10.1177/09544070241245477
Luo Jiang, Javad Kheyrollahi, Charles Robert Koch, Mahdi Shahbakhti
Cooperative truck platooning, a convoy of trucks driving together while communicating and coordinating with each other, represents a technology-driven approach to improve energy conversion efficiency, lower greenhouse gas emissions, and enhance road safety. Despite numerous studies have explored these potentials, there is a scarcity of empirical investigations into on-road cooperative truck platooning during commercial operations, particularly in winter driving conditions. This paper presents the findings of an experimental study on the first commercially focused truck platooning implementation on a Canadian public highway in the winter season, using two SAE level 2 class 8 trucks. The on-road trials took place on the Queen Elizabeth II Highway, between Calgary and Edmonton, with ambient temperatures ranging from −27°C to 12°C, and truck weights spanning 16–39 tons. Nine well-trained and experienced drivers conducted 41 incident-free (platooning and baseline) test trips, covering a distance of 22,855 km. The experimental results confirmed the feasibility of operating commercial truck platooning with 3–5 s time gaps on public roads during the Canadian winter season including various road surface conditions. The results also show that the platooning engagement ratio reached up to 88.9%, with an average of 61.6% across 25 platooning trips. Furthermore, the follower truck achieved a 1.6% fuel savings on flat road sections during platooning, but its freight transportation specific fuel consumption was higher than that of the lead truck on hilly terrain. Test results indicate the lighter truck exhibited higher specific nitrogen oxides (NOx) emissions. Moreover, the frequent engagement and disengagement of the cooperative truck platooning system had adverse effects on the powertrain system of the truck, leading to increased fuel consumption and engine-out NOx emissions. This study provides real-world data to identify limitations and needed areas for improvement in adapting cooperative truck platooning technology to commercial operations on public roads.
{"title":"Cooperative truck platooning trial on Canadian public highway under commercial operation in winter driving conditions","authors":"Luo Jiang, Javad Kheyrollahi, Charles Robert Koch, Mahdi Shahbakhti","doi":"10.1177/09544070241245477","DOIUrl":"https://doi.org/10.1177/09544070241245477","url":null,"abstract":"Cooperative truck platooning, a convoy of trucks driving together while communicating and coordinating with each other, represents a technology-driven approach to improve energy conversion efficiency, lower greenhouse gas emissions, and enhance road safety. Despite numerous studies have explored these potentials, there is a scarcity of empirical investigations into on-road cooperative truck platooning during commercial operations, particularly in winter driving conditions. This paper presents the findings of an experimental study on the first commercially focused truck platooning implementation on a Canadian public highway in the winter season, using two SAE level 2 class 8 trucks. The on-road trials took place on the Queen Elizabeth II Highway, between Calgary and Edmonton, with ambient temperatures ranging from −27°C to 12°C, and truck weights spanning 16–39 tons. Nine well-trained and experienced drivers conducted 41 incident-free (platooning and baseline) test trips, covering a distance of 22,855 km. The experimental results confirmed the feasibility of operating commercial truck platooning with 3–5 s time gaps on public roads during the Canadian winter season including various road surface conditions. The results also show that the platooning engagement ratio reached up to 88.9%, with an average of 61.6% across 25 platooning trips. Furthermore, the follower truck achieved a 1.6% fuel savings on flat road sections during platooning, but its freight transportation specific fuel consumption was higher than that of the lead truck on hilly terrain. Test results indicate the lighter truck exhibited higher specific nitrogen oxides (NOx) emissions. Moreover, the frequent engagement and disengagement of the cooperative truck platooning system had adverse effects on the powertrain system of the truck, leading to increased fuel consumption and engine-out NOx emissions. This study provides real-world data to identify limitations and needed areas for improvement in adapting cooperative truck platooning technology to commercial operations on public roads.","PeriodicalId":54568,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part D-Journal of Automobile Engineering","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140630714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-18DOI: 10.1177/09544070241242895
Changda Li, Chen Liang, Donghui Sun, Guolin Wang
Tire durability has been an essential part of passenger and vehicle safety, which usually requires years of road services to test relevant performance. The tire oven aging test has been widely accepted as an accelerated laboratory test to induce large thermal oxidation of rubber compounds with mechanical degradations under high temperatures. Then, the oven-aged tires are tested on rolling drums to compare the durability hours and failure locations. As an assembly of various components with different structures and formulations, tire aging and durability tests are complicated issues incorporating oxygen permeation, consumption, and mechanical degradation. Therefore, it calls for a predictive workflow and model to evaluate the oven aging extent and corresponding durability performance. Based on the computed local oxygen consumption of tires, this article assigns the aged property to each element in a tire FEA (Finite Element Analysis) model by building the quantitative correlations between compound oxygen consumption and degraded mechanical properties. Subsequently, tire mechanics simulations are conducted with SEDG (Strain Energy Density Gradient) to evaluate and compare the endurance of oven-aged tires with different inner liner structures and formulations.
{"title":"Durability evaluation and simulation of oven aged tires","authors":"Changda Li, Chen Liang, Donghui Sun, Guolin Wang","doi":"10.1177/09544070241242895","DOIUrl":"https://doi.org/10.1177/09544070241242895","url":null,"abstract":"Tire durability has been an essential part of passenger and vehicle safety, which usually requires years of road services to test relevant performance. The tire oven aging test has been widely accepted as an accelerated laboratory test to induce large thermal oxidation of rubber compounds with mechanical degradations under high temperatures. Then, the oven-aged tires are tested on rolling drums to compare the durability hours and failure locations. As an assembly of various components with different structures and formulations, tire aging and durability tests are complicated issues incorporating oxygen permeation, consumption, and mechanical degradation. Therefore, it calls for a predictive workflow and model to evaluate the oven aging extent and corresponding durability performance. Based on the computed local oxygen consumption of tires, this article assigns the aged property to each element in a tire FEA (Finite Element Analysis) model by building the quantitative correlations between compound oxygen consumption and degraded mechanical properties. Subsequently, tire mechanics simulations are conducted with SEDG (Strain Energy Density Gradient) to evaluate and compare the endurance of oven-aged tires with different inner liner structures and formulations.","PeriodicalId":54568,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part D-Journal of Automobile Engineering","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140626986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, loss minimization control method of IPMSM based on approximate calculation is proposed. Firstly, according to the IPMSM mathematical model considering the equivalent iron loss, the loss minimization extreme value is solved by Lagrange equation, and the electromagnetic current variable is eliminated by the equivalent transformation method. Secondly, the relation of dq axis current is obtained by defining the virtual current. Considering the existence of the square term in the relation, a new approximate calculation method is proposed to eliminate the square term of the given current and obtain the dq axis current with the minimum loss. Finally, in order to further prove the performance of the proposed method, [Formula: see text] control, virtual current control method and torque approximation control method are analyzed and compared in MATLAB/SIMULINK. Simulation results show that the proposed control method is more perfect than the traditional virtual current method and has higher accuracy than the traditional approximate calculation method. Compared with the traditional three control methods, the total loss is reduced by 7.44% at least under rated working conditions.
{"title":"Loss minimization control of interior permanent magnet synchronous motor (IPMSM) based on approximate calculation","authors":"Jingang Liu, Ruiqi Li, Xianghuan Liu, Jianwen Cheng, Jianyun Zheng, Bohuan Tan","doi":"10.1177/09544070241244498","DOIUrl":"https://doi.org/10.1177/09544070241244498","url":null,"abstract":"In this paper, loss minimization control method of IPMSM based on approximate calculation is proposed. Firstly, according to the IPMSM mathematical model considering the equivalent iron loss, the loss minimization extreme value is solved by Lagrange equation, and the electromagnetic current variable is eliminated by the equivalent transformation method. Secondly, the relation of dq axis current is obtained by defining the virtual current. Considering the existence of the square term in the relation, a new approximate calculation method is proposed to eliminate the square term of the given current and obtain the dq axis current with the minimum loss. Finally, in order to further prove the performance of the proposed method, [Formula: see text] control, virtual current control method and torque approximation control method are analyzed and compared in MATLAB/SIMULINK. Simulation results show that the proposed control method is more perfect than the traditional virtual current method and has higher accuracy than the traditional approximate calculation method. Compared with the traditional three control methods, the total loss is reduced by 7.44% at least under rated working conditions.","PeriodicalId":54568,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part D-Journal of Automobile Engineering","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140630801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-17DOI: 10.1177/09544070241244414
Rongkun Wei, Yunsong Wei, Yingxue Xiao, Rong Ma
Separating the drivable and non- drivable areas on semi-structured and unstructured roads is an important task for autonomous vehicles to safely and avoid obstacles. Semi structured and unstructured roads have different intensities, normal vector angles, and curvature information than the background, and this paves the way for the design and development of an efficient detection system for drivable areas on this roads. In this paper, an effective method for detecting drivable areas is proposed that is based on important indicators of an experimental vehicles. This method calculate the information gain of features is calculated firstly to determine the sequence of feature processing. On the basis of this sequence calculate the maximum inter-class variance of features, and combined with the specific indicators of the experimental vehicle to realize the detection of drivable areas. Finally, the performance of the method is evaluated in terms of average precision, recall, and detection accuracy, and compared with the performance of existing road detection methods, including the K-nearest-neighbors classifier and the random forest classifier methods. The experimental results show that the average precision, recall, and detection accuracy of the system are 96.19%, 96.89%, and 96.72%, respectively. The method proposed here can effectively identify and classify drivable areas on semi structured and unstructured roads.
{"title":"Lidar-based classification and detection system for drivable area on roads","authors":"Rongkun Wei, Yunsong Wei, Yingxue Xiao, Rong Ma","doi":"10.1177/09544070241244414","DOIUrl":"https://doi.org/10.1177/09544070241244414","url":null,"abstract":"Separating the drivable and non- drivable areas on semi-structured and unstructured roads is an important task for autonomous vehicles to safely and avoid obstacles. Semi structured and unstructured roads have different intensities, normal vector angles, and curvature information than the background, and this paves the way for the design and development of an efficient detection system for drivable areas on this roads. In this paper, an effective method for detecting drivable areas is proposed that is based on important indicators of an experimental vehicles. This method calculate the information gain of features is calculated firstly to determine the sequence of feature processing. On the basis of this sequence calculate the maximum inter-class variance of features, and combined with the specific indicators of the experimental vehicle to realize the detection of drivable areas. Finally, the performance of the method is evaluated in terms of average precision, recall, and detection accuracy, and compared with the performance of existing road detection methods, including the K-nearest-neighbors classifier and the random forest classifier methods. The experimental results show that the average precision, recall, and detection accuracy of the system are 96.19%, 96.89%, and 96.72%, respectively. The method proposed here can effectively identify and classify drivable areas on semi structured and unstructured roads.","PeriodicalId":54568,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part D-Journal of Automobile Engineering","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140610810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aiming at keeping safe in time and addressing disturbance of uncertainty, an closed loop forward simulation filtering double-layer artificial potential field (CL-FDAPF) trajectory planner and first order linear active disturbance rejective control (FO-LADRC) motion controller are proposed for autonomous sweeper vehicle. Firstly, the double-layer artificial potential field, which consists of traditional potential cost layer and safe level layer, is adopted here to keep planning realtime, meet safe limitations and satisfy operational requirements, and the postprocessing of mean filtering and closed loop forward simulation is for vehicle dynamic constraints. Secondly, it is worth developing active disturbance rejection control strategy, which has the ability to accommodate uncertainty, since an accurate mathematical model of autonomous sweeper vehicle is unavailable as there being inevitable uncertainties in the system state observation and unavoidable environmental disturbances. Thirdly, several typical scenarios are designed in order to verify the real-time and reliability of the proposed algorithm. The results illustrate that the CL-FDAPF planner has highly real-time and stability as the peak time less than 0.045 s and mean time being about 0.02 s in 1000 cycles, and FO-LADRC controller has robust both at uncertainty of wheelbase and steering ratio, since the FO-LADRC have smaller lateral errors compared with two existing methods.
{"title":"CL-FDAPF trajectory planner and FO-LADRC motion controller for autonomous sweeper vehicle","authors":"Dequan Zeng, Yiming Hu, Tianfu Ai, Chengcheng Liang, Yiquan Yu, Zhiqiang Jiang","doi":"10.1177/09544070241239992","DOIUrl":"https://doi.org/10.1177/09544070241239992","url":null,"abstract":"Aiming at keeping safe in time and addressing disturbance of uncertainty, an closed loop forward simulation filtering double-layer artificial potential field (CL-FDAPF) trajectory planner and first order linear active disturbance rejective control (FO-LADRC) motion controller are proposed for autonomous sweeper vehicle. Firstly, the double-layer artificial potential field, which consists of traditional potential cost layer and safe level layer, is adopted here to keep planning realtime, meet safe limitations and satisfy operational requirements, and the postprocessing of mean filtering and closed loop forward simulation is for vehicle dynamic constraints. Secondly, it is worth developing active disturbance rejection control strategy, which has the ability to accommodate uncertainty, since an accurate mathematical model of autonomous sweeper vehicle is unavailable as there being inevitable uncertainties in the system state observation and unavoidable environmental disturbances. Thirdly, several typical scenarios are designed in order to verify the real-time and reliability of the proposed algorithm. The results illustrate that the CL-FDAPF planner has highly real-time and stability as the peak time less than 0.045 s and mean time being about 0.02 s in 1000 cycles, and FO-LADRC controller has robust both at uncertainty of wheelbase and steering ratio, since the FO-LADRC have smaller lateral errors compared with two existing methods.","PeriodicalId":54568,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part D-Journal of Automobile Engineering","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140610759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-15DOI: 10.1177/09544070231206878
Wenbin Hou, Xinyu Wang, Jingfei Han, Zaiqi Yao, Changsheng Wang
The conceptual design stage is an indispensable and important stage in the development of the modern body. However, due to the lack of sufficient body structure parameter support, the design defects generated at this time are difficult to make up for in the subsequent design stage. Therefore, it is of great significance to develop a conceptual design software for body structure that integrates design, analysis, and optimization. This paper proposes an intelligent body frame structure modeling and optimization system based on conceptual design, S-iVCD (Intelligent System for Conceptual Design of Vehicle Body Structure). Based on deep learning methods and body design sketches, a conceptual model of body structure is quickly established. Based on the data parameters stored in the Excel table, the system is seamlessly connected to the domestic independent finite element software SIPESC to calculate the simulated working conditions. The body structure optimization module is driven by the MMA (method of moving asymptotes), and takes the body structure performance and total mass as the optimization goals or constraints of the mathematical model to achieve body structure performance improvement and lightweight design.
{"title":"An intelligent body frame structure modeling and optimization system based on conceptual design","authors":"Wenbin Hou, Xinyu Wang, Jingfei Han, Zaiqi Yao, Changsheng Wang","doi":"10.1177/09544070231206878","DOIUrl":"https://doi.org/10.1177/09544070231206878","url":null,"abstract":"The conceptual design stage is an indispensable and important stage in the development of the modern body. However, due to the lack of sufficient body structure parameter support, the design defects generated at this time are difficult to make up for in the subsequent design stage. Therefore, it is of great significance to develop a conceptual design software for body structure that integrates design, analysis, and optimization. This paper proposes an intelligent body frame structure modeling and optimization system based on conceptual design, S-iVCD (Intelligent System for Conceptual Design of Vehicle Body Structure). Based on deep learning methods and body design sketches, a conceptual model of body structure is quickly established. Based on the data parameters stored in the Excel table, the system is seamlessly connected to the domestic independent finite element software SIPESC to calculate the simulated working conditions. The body structure optimization module is driven by the MMA (method of moving asymptotes), and takes the body structure performance and total mass as the optimization goals or constraints of the mathematical model to achieve body structure performance improvement and lightweight design.","PeriodicalId":54568,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part D-Journal of Automobile Engineering","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140560204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-13DOI: 10.1177/09544070241241872
Baolong Hou, Qinyu Sun, Yingshi Guo
The existing obstacle-avoidance trajectory planning and trajectory tracking control algorithms have limitations such as long-time consumption, high failure rate in dynamic traffic environments, and insufficient trajectory tracking accuracy in curved roads. Based on the above problems, this paper designs a two stage obstacle-avoidance trajectory planner based on nonlinear optimization theory. In first stage Part-NLP, only considering the safety obstacle avoidance, a point mass model and linearization constraints are established to quickly solve the initial trajectory. In the second stage Full-NLP, considering smooth soft constraints comprehensively, the initial trajectory is optimized by establishing driving corridors and a lightweight iterative framework. In control module, this paper selects a linear quadratic form lateral trajectory tracking controller, and the parameters were optimized through the carnivorous plant algorithm. The joint simulation results show that in dynamic traffic environment of curved roads, the two stage planner proposed can accurately plan safe and smooth obstacle avoidance trajectories, and there is a significant reduction in time consumption compared to traditional NLP algorithms. The control strategy can accurately track the planned trajectories, with lateral error controlled within plus or minus 0.1 m, heading error controlled within plus or minus 0.15 rad, speed tracking error controlled within plus or minus 0.15 m/s, and vehicle yaw angle error controlled within plus or minus 0.04 rad; the hardware-in-loop test results indicate that the controller can achieve real-time and accurate trajectory tracking.
现有的避障轨迹规划和轨迹跟踪控制算法存在耗时长、动态交通环境下故障率高、弯道轨迹跟踪精度不足等局限性。基于上述问题，本文设计了一种基于非线性优化理论的两阶段避障轨迹规划算法。在第一阶段 Part-NLP，只考虑安全避障，建立点质量模型和线性化约束，快速求解初始轨迹。在第二阶段 Full-NLP 中，综合考虑平滑软约束，通过建立行驶走廊和轻量级迭代框架来优化初始轨迹。在控制模块，本文选用线性二次方形式横向轨迹跟踪控制器，通过多肉植物算法对参数进行优化。联合仿真结果表明，在弯曲道路的动态交通环境中，所提出的两阶段规划器能准确规划安全平稳的避障轨迹，与传统的 NLP 算法相比，耗时明显减少。控制策略可以精确跟踪规划轨迹，横向误差控制在正负 0.1 m 以内，航向误差控制在正负 0.15 rad 以内，速度跟踪误差控制在正负 0.15 m/s 以内，车辆偏航角误差控制在正负 0.04 rad 以内；硬件在环测试结果表明，控制器可以实现实时、精确的轨迹跟踪。
{"title":"Research on two stage obstacle-avoidance trajectory planning and trajectory tracking control in curves","authors":"Baolong Hou, Qinyu Sun, Yingshi Guo","doi":"10.1177/09544070241241872","DOIUrl":"https://doi.org/10.1177/09544070241241872","url":null,"abstract":"The existing obstacle-avoidance trajectory planning and trajectory tracking control algorithms have limitations such as long-time consumption, high failure rate in dynamic traffic environments, and insufficient trajectory tracking accuracy in curved roads. Based on the above problems, this paper designs a two stage obstacle-avoidance trajectory planner based on nonlinear optimization theory. In first stage Part-NLP, only considering the safety obstacle avoidance, a point mass model and linearization constraints are established to quickly solve the initial trajectory. In the second stage Full-NLP, considering smooth soft constraints comprehensively, the initial trajectory is optimized by establishing driving corridors and a lightweight iterative framework. In control module, this paper selects a linear quadratic form lateral trajectory tracking controller, and the parameters were optimized through the carnivorous plant algorithm. The joint simulation results show that in dynamic traffic environment of curved roads, the two stage planner proposed can accurately plan safe and smooth obstacle avoidance trajectories, and there is a significant reduction in time consumption compared to traditional NLP algorithms. The control strategy can accurately track the planned trajectories, with lateral error controlled within plus or minus 0.1 m, heading error controlled within plus or minus 0.15 rad, speed tracking error controlled within plus or minus 0.15 m/s, and vehicle yaw angle error controlled within plus or minus 0.04 rad; the hardware-in-loop test results indicate that the controller can achieve real-time and accurate trajectory tracking.","PeriodicalId":54568,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part D-Journal of Automobile Engineering","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140560632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-13DOI: 10.1177/09544070241242825
Aykut Bacak
Alternative fuels are becoming more popular as awareness of fossil fuel depletion, pollution, and climate change grows. Numerous industrial companies are producing electric automobiles for use worldwide. Electric vehicles’ battery packs’ cooling causes firing due to high temperatures. In this study, the surface temperatures of a single electric battery with dimensions of 160 mm × 210 mm within a battery pack were investigated using computational fluid dynamics and, subsequently, Levenberg-Marquardt machine learning as a function of ambient temperature, convective heat transfer coefficient, nominal capacity of the electric battery, and discharge rate. The transport coefficient has been calculated for a rechargeable electric battery with a nominal capacity ranging from 14.6 to 20 Ah and a discharge rate varying between 1 and 15, taking into account conditions of stagnant air at temperatures ranging from 20°C to 35°C and values between 5 and 20 W/m2.K. Insufficient or absent cooling of battery temperatures can lead to them reaching combustion temperatures of electric vehicle batteries, typically from 50°C to 80°C, depending on the operational circumstances. An artificial neural network was utilized in machine learning to forecast maximum temperatures based on operating conditions without requiring simulation. The neural network achieved an estimated mean squared error of 0.00552 and a calculated coefficient of determination of 0.99. The neural network model can predict outputs with mean and standard deviation rates below 0.237. The anticipated artificial neural network model can accurately forecast the maximum surface temperature of an electric vehicle battery.
{"title":"Investigation of maximum temperatures in lithium-ion batteries by CFD and machine learning","authors":"Aykut Bacak","doi":"10.1177/09544070241242825","DOIUrl":"https://doi.org/10.1177/09544070241242825","url":null,"abstract":"Alternative fuels are becoming more popular as awareness of fossil fuel depletion, pollution, and climate change grows. Numerous industrial companies are producing electric automobiles for use worldwide. Electric vehicles’ battery packs’ cooling causes firing due to high temperatures. In this study, the surface temperatures of a single electric battery with dimensions of 160 mm × 210 mm within a battery pack were investigated using computational fluid dynamics and, subsequently, Levenberg-Marquardt machine learning as a function of ambient temperature, convective heat transfer coefficient, nominal capacity of the electric battery, and discharge rate. The transport coefficient has been calculated for a rechargeable electric battery with a nominal capacity ranging from 14.6 to 20 Ah and a discharge rate varying between 1 and 15, taking into account conditions of stagnant air at temperatures ranging from 20°C to 35°C and values between 5 and 20 W/m<jats:sup>2</jats:sup>.K. Insufficient or absent cooling of battery temperatures can lead to them reaching combustion temperatures of electric vehicle batteries, typically from 50°C to 80°C, depending on the operational circumstances. An artificial neural network was utilized in machine learning to forecast maximum temperatures based on operating conditions without requiring simulation. The neural network achieved an estimated mean squared error of 0.00552 and a calculated coefficient of determination of 0.99. The neural network model can predict outputs with mean and standard deviation rates below 0.237. The anticipated artificial neural network model can accurately forecast the maximum surface temperature of an electric vehicle battery.","PeriodicalId":54568,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part D-Journal of Automobile Engineering","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140560647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-13DOI: 10.1177/09544070241238297
Yan Zhang, Jianglu Huang, Liange He, Donggang Zhao, Yu Zhao
The performance and life of electric vehicle power batteries will be reduced at low temperatures, and the lower temperature in the electric vehicle will also affect the comfort of drivers and passengers. Taking into account the winter temperatures and the unique drive structure of the plug-in hybrid electric vehicle, a specially designed driving mode for low-temperature environment is implemented. Based on this drive mode, a plug-in hybrid electric vehicle (PHEV) integrated thermal management structure is proposed to heat the battery and the passenger compartment, thereby improving energy efficiency. A mathematical model is used to establish the entire vehicle thermal management system, which is then experimentally validated. Under the NEDC (New European Driving Cycle) at ambient temperatures of −5°C, −10°C, −15°C, and −20°C, the calculation results of engine waste heat utilization and PTC (Positive Temperature Coefficient) heating are compared and analyzed. The results show that the average heating rate of the thermal management system proposed in this study is 23% faster than that of PTC heating at low temperature. The SOC decreases to 63.43% when engine waste heat utilization is adopted. When PTC heating is used, the SOC decreases to 49.18%. However, the advantage of the faster rate of engine waste heat compared to PTC heating becomes less pronounced as the ambient temperature decreases.
{"title":"Recovery of engine waste heat in low temperature environment of plug-in hybrid electric vehicle","authors":"Yan Zhang, Jianglu Huang, Liange He, Donggang Zhao, Yu Zhao","doi":"10.1177/09544070241238297","DOIUrl":"https://doi.org/10.1177/09544070241238297","url":null,"abstract":"The performance and life of electric vehicle power batteries will be reduced at low temperatures, and the lower temperature in the electric vehicle will also affect the comfort of drivers and passengers. Taking into account the winter temperatures and the unique drive structure of the plug-in hybrid electric vehicle, a specially designed driving mode for low-temperature environment is implemented. Based on this drive mode, a plug-in hybrid electric vehicle (PHEV) integrated thermal management structure is proposed to heat the battery and the passenger compartment, thereby improving energy efficiency. A mathematical model is used to establish the entire vehicle thermal management system, which is then experimentally validated. Under the NEDC (New European Driving Cycle) at ambient temperatures of −5°C, −10°C, −15°C, and −20°C, the calculation results of engine waste heat utilization and PTC (Positive Temperature Coefficient) heating are compared and analyzed. The results show that the average heating rate of the thermal management system proposed in this study is 23% faster than that of PTC heating at low temperature. The SOC decreases to 63.43% when engine waste heat utilization is adopted. When PTC heating is used, the SOC decreases to 49.18%. However, the advantage of the faster rate of engine waste heat compared to PTC heating becomes less pronounced as the ambient temperature decreases.","PeriodicalId":54568,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part D-Journal of Automobile Engineering","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140560629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the foreseeable future, the anticipation is that intelligent vehicles will transition to a mode where the intelligent driving system collaborates seamlessly with the human driver. This harmonious integration between the driver and the intelligent control system holds paramount significance for the successful execution of driving tasks, ultimately contributing to the development of more advanced and user-friendly automobiles. A pivotal element in advancing from assisted to autonomous driving lies in the establishment of a human-machine co-driving mode. This research delineates a longitudinal control strategy tailored for intelligent vehicles featuring human-machine interaction. The approach involves the creation of a personalized safe distance model for car-following by collecting driver characteristic parameters. Focused on the car-following methodology, this study formulates the kinematics state space equation, performance index function, and constraint conditions governing car-following dynamics. Subsequently, a car-following control strategy is devised based on model predictive control (MPC), which is addressed through rolling optimization techniques. Building upon this foundation, a human-machine driving control strategy is proposed to dynamically allocate driving authorities in real-time. This strategy takes into account speed and vehicle distance risk as two-dimensional inputs, employing a cooperative driving control strategy within the dual-drive dual-control system. The proposed method was validated in a simulated environment.
{"title":"An intelligent human-machine interaction-based longitudinal control strategy for autonomous vehicles","authors":"Ping Liu, Hang Shu, Yunpeng Tian, Yikang Zhang, Weiping Ding, Haibo Huang","doi":"10.1177/09544070241242831","DOIUrl":"https://doi.org/10.1177/09544070241242831","url":null,"abstract":"In the foreseeable future, the anticipation is that intelligent vehicles will transition to a mode where the intelligent driving system collaborates seamlessly with the human driver. This harmonious integration between the driver and the intelligent control system holds paramount significance for the successful execution of driving tasks, ultimately contributing to the development of more advanced and user-friendly automobiles. A pivotal element in advancing from assisted to autonomous driving lies in the establishment of a human-machine co-driving mode. This research delineates a longitudinal control strategy tailored for intelligent vehicles featuring human-machine interaction. The approach involves the creation of a personalized safe distance model for car-following by collecting driver characteristic parameters. Focused on the car-following methodology, this study formulates the kinematics state space equation, performance index function, and constraint conditions governing car-following dynamics. Subsequently, a car-following control strategy is devised based on model predictive control (MPC), which is addressed through rolling optimization techniques. Building upon this foundation, a human-machine driving control strategy is proposed to dynamically allocate driving authorities in real-time. This strategy takes into account speed and vehicle distance risk as two-dimensional inputs, employing a cooperative driving control strategy within the dual-drive dual-control system. The proposed method was validated in a simulated environment.","PeriodicalId":54568,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part D-Journal of Automobile Engineering","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140560529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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