Transportation electrification is essential for decarbonizing transport. Currently, lithium-ion batteries are the primary power source for electric vehicles (EVs). However, there is still a significant journey ahead b...Transportation electrification is essential for decarbonizing transport. Currently, lithium-ion batteries are the primary power source for electric vehicles (EVs). However, there is still a significant journey ahead before EVs can establish themselves as the dominant force in the global automotive market. Concerns such as range anxiety, battery aging, and safety issues remain significant challenges.展开更多
Lithium-ion batteries are key drivers of the renewable energy revolution,bolstered by progress in battery design,modelling,and management.Yet,achieving high-performance battery health prognostics is a significant chal...Lithium-ion batteries are key drivers of the renewable energy revolution,bolstered by progress in battery design,modelling,and management.Yet,achieving high-performance battery health prognostics is a significant challenge.With the availability of open data and software,coupled with automated simulations,deep learning has become an integral component of battery health prognostics.We offer a comprehensive overview of potential deep learning techniques specifically designed for modeling and forecasting the dynamics of multiphysics and multiscale battery systems.Following this,we provide a concise summary of publicly available lithium-ion battery test and cycle datasets.By providing illustrative examples,we emphasize the efficacy of five techniques capable of enhancing deep learning for accurate battery state prediction and health-focused management.Each of these techniques offers unique benefits.(1)Transformer models address challenges using self-attention mechanisms and positional encoding methods.(2) Transfer learning improves learning tasks within a target domain by leveraging knowledge from a source domain.(3) Physics-informed learning uses prior knowledge to enhance learning algorithms.(4)Generative adversarial networks(GANs) earn praise for their ability to generate diverse and high-quality outputs,exhibiting outstanding performance with complex datasets.(5) Deep reinforcement learning enables an agent to make optimal decisions through continuous interactions with its environment,thus maximizing cumulative rewards.In this Review,we highlight examples that employ these techniques for battery health prognostics,summarizing both their challenges and opportunities.These methodologies offer promising prospects for researchers and industry professionals,enabling the creation of specialized network architectures that autonomously extract features,especially for long-range spatial-temporal connections across extended timescales.The outcomes could include improved accuracy,faster training,and enhanced generalization.展开更多
The development of electrochemical capacitors(i.e.supercapacitors)have attracted a lot of attention in recent years because of the increasing demand for efficient,high-power energy storage.Electrochemical capacitors(E...The development of electrochemical capacitors(i.e.supercapacitors)have attracted a lot of attention in recent years because of the increasing demand for efficient,high-power energy storage.Electrochemical capacitors(ECs)are particularly attractive for transportation and renewable energy generation applications,taking advantage of their superior power capability and outstanding cycle life.Over the past decade,various advanced electrode materials and cell design are being studied to improve the energy density of ECs.Hybrid Li-ion capacitors and pseudo-capacitors that utilize fast surface redox reactions of metal oxide and doped polymers are the prime candidates being considered.This paper is concerned with the metrics being used to describe the performance of ECs and how the metrics are evaluated by testing devices and how the data from the testing are best interpreted.Emphasize is on relating testing of advanced ECs using materials more complex than activated carbons to testing electric double-layer capacitors(EDLCs)using carbon in both electrodes.A second focus of the paper is projecting the potential of the advanced materials and ionic liquid electrolytes for the development of complete EC cells having an energy density more than a factor of ten greater the energy density of the EDLC devices currently on the market.This potential was evaluated by calculating the performance(energy and power)of a series of ECs that utilize the advanced materials that have been studied by electrochemists over the past 10-15 years.The capacitance and resistance of the advanced ECs were calculated utilizing specific capacitance(F/g or F/cm^(3))and porosity data for the electrode materials and ionic conductivity of the electrolytes.It was concluded that hybrid ECs can be developed with energy densities of at least 50 Wh/kg,70 Wh/L with efficient power greater than 3 k W/kg.Continued research on micro-porous carbons with specific capacitance of 200 F/g and greater is needed.to achieve these EC performance goals.展开更多
This paper proposes and illustrates a methodology to predict the fraction of time motor vehicles spend in different operating conditions from readily observable variables called emission specific characteristics(ESC)....This paper proposes and illustrates a methodology to predict the fraction of time motor vehicles spend in different operating conditions from readily observable variables called emission specific characteristics(ESC). ESC describe salient characteristics of vehicles,roadway geometry, the roadside environment, traffic, and driving style(aggressive,normal, and calm). The information generated by our methodology can then be entered in vehicular emission models that rely on vehicle specific power, i.e., comprehensive modal emissions model(CMEM), international vehicle emissions(IVE), or motor vehicle emission simulator(MOVES) to compute energy consumption and vehicular emissions for various air pollutants. After generating second-by-second vehicle trajectories from a calibrated micro-simulation model, the authors estimated structural equation models to examine the influence of link ESC on vehicle operation. Authors' results show that 67% of the link speed variance is explained by ESC. Overall, the roadway geometry exerts a greater influence on link speed than traffic characteristics, the roadside environment, and driving style.Moreover, the speed limit has the strongest influence on vehicle operation, followed by facility type and driving style. Better understanding the impact on vehicle operation of ESC could help metropolitan planning organizations(MPOs) and regional transportation authorities predict vehicle operations and reduce the environmental footprint of motor vehicles.展开更多
A general review of the socio-economic impact of the intelligent transport system (ITS) is pre-sented with a case study to demonstrate the data envelopment analysis method. Cost-benefit analyses are still the dominant...A general review of the socio-economic impact of the intelligent transport system (ITS) is pre-sented with a case study to demonstrate the data envelopment analysis method. Cost-benefit analyses are still the dominant method for evaluating ITS and other transport engineering projects, while cost effective analyses and multi-criteria appraisals are widely used to define and prioritize objectives by providing useful information for the most promising policy decisions. Both cost-benefit analyses and a data envelopment analysis method are applied to analyze the socio-economic impact of convoy driving systems. The main findings are that a convoy provides a worthwhile benefit-cost ratio when more than 30% of the traffics in the convoys and the traffic load exceeds 5500 vehicles/h for a three-lane motorway. The results also show that for a fixed percentage of convoys, increased demand will increase the data envelopment analysis method relative efficiency and that the neglect of certain output indicators of an ITS may result in underestimation of the system effects.展开更多
Car-following models describe how one vehicle follows the preceding vehicles. In order to better módel and explain car-following dynamics, this paper categorizes the state of a traveling vehicle into three sub-pr...Car-following models describe how one vehicle follows the preceding vehicles. In order to better módel and explain car-following dynamics, this paper categorizes the state of a traveling vehicle into three sub-processes: the starting (acceleration) process, the car-following process, and the stopping (deceleration) process. The starting process primarily involves vehicle acceleration behavior. The stopping process involves not only car-following behavior but also deceleration behavior. This paper regards both the stopping process and the starting process as spring systems. The car-following dynamics during the starting process and the stopping process is modeled in this paper. The parameters of the proposed models, which are represented in the form of trigonometric functions, possess explicit physical meaning and definitive ranges. We have calibrated the model of the starting process using data from the Traffic Engineering Handbook, and obtained reasonable results. Compared with traditional stimulus-response car-following models, this model can better explain traffic flow phenomena and driver behavior theory.展开更多
文摘Transportation electrification is essential for decarbonizing transport. Currently, lithium-ion batteries are the primary power source for electric vehicles (EVs). However, there is still a significant journey ahead before EVs can establish themselves as the dominant force in the global automotive market. Concerns such as range anxiety, battery aging, and safety issues remain significant challenges.
文摘Lithium-ion batteries are key drivers of the renewable energy revolution,bolstered by progress in battery design,modelling,and management.Yet,achieving high-performance battery health prognostics is a significant challenge.With the availability of open data and software,coupled with automated simulations,deep learning has become an integral component of battery health prognostics.We offer a comprehensive overview of potential deep learning techniques specifically designed for modeling and forecasting the dynamics of multiphysics and multiscale battery systems.Following this,we provide a concise summary of publicly available lithium-ion battery test and cycle datasets.By providing illustrative examples,we emphasize the efficacy of five techniques capable of enhancing deep learning for accurate battery state prediction and health-focused management.Each of these techniques offers unique benefits.(1)Transformer models address challenges using self-attention mechanisms and positional encoding methods.(2) Transfer learning improves learning tasks within a target domain by leveraging knowledge from a source domain.(3) Physics-informed learning uses prior knowledge to enhance learning algorithms.(4)Generative adversarial networks(GANs) earn praise for their ability to generate diverse and high-quality outputs,exhibiting outstanding performance with complex datasets.(5) Deep reinforcement learning enables an agent to make optimal decisions through continuous interactions with its environment,thus maximizing cumulative rewards.In this Review,we highlight examples that employ these techniques for battery health prognostics,summarizing both their challenges and opportunities.These methodologies offer promising prospects for researchers and industry professionals,enabling the creation of specialized network architectures that autonomously extract features,especially for long-range spatial-temporal connections across extended timescales.The outcomes could include improved accuracy,faster training,and enhanced generalization.
基金the China Scholarship Council(CSC)for the financial support for the study and research project as an international Ph.D.student at ITS-UC Davis。
文摘The development of electrochemical capacitors(i.e.supercapacitors)have attracted a lot of attention in recent years because of the increasing demand for efficient,high-power energy storage.Electrochemical capacitors(ECs)are particularly attractive for transportation and renewable energy generation applications,taking advantage of their superior power capability and outstanding cycle life.Over the past decade,various advanced electrode materials and cell design are being studied to improve the energy density of ECs.Hybrid Li-ion capacitors and pseudo-capacitors that utilize fast surface redox reactions of metal oxide and doped polymers are the prime candidates being considered.This paper is concerned with the metrics being used to describe the performance of ECs and how the metrics are evaluated by testing devices and how the data from the testing are best interpreted.Emphasize is on relating testing of advanced ECs using materials more complex than activated carbons to testing electric double-layer capacitors(EDLCs)using carbon in both electrodes.A second focus of the paper is projecting the potential of the advanced materials and ionic liquid electrolytes for the development of complete EC cells having an energy density more than a factor of ten greater the energy density of the EDLC devices currently on the market.This potential was evaluated by calculating the performance(energy and power)of a series of ECs that utilize the advanced materials that have been studied by electrochemists over the past 10-15 years.The capacitance and resistance of the advanced ECs were calculated utilizing specific capacitance(F/g or F/cm^(3))and porosity data for the electrode materials and ionic conductivity of the electrolytes.It was concluded that hybrid ECs can be developed with energy densities of at least 50 Wh/kg,70 Wh/L with efficient power greater than 3 k W/kg.Continued research on micro-porous carbons with specific capacitance of 200 F/g and greater is needed.to achieve these EC performance goals.
基金the Ford Foundation International Fellowship Program that provided support
文摘This paper proposes and illustrates a methodology to predict the fraction of time motor vehicles spend in different operating conditions from readily observable variables called emission specific characteristics(ESC). ESC describe salient characteristics of vehicles,roadway geometry, the roadside environment, traffic, and driving style(aggressive,normal, and calm). The information generated by our methodology can then be entered in vehicular emission models that rely on vehicle specific power, i.e., comprehensive modal emissions model(CMEM), international vehicle emissions(IVE), or motor vehicle emission simulator(MOVES) to compute energy consumption and vehicular emissions for various air pollutants. After generating second-by-second vehicle trajectories from a calibrated micro-simulation model, the authors estimated structural equation models to examine the influence of link ESC on vehicle operation. Authors' results show that 67% of the link speed variance is explained by ESC. Overall, the roadway geometry exerts a greater influence on link speed than traffic characteristics, the roadside environment, and driving style.Moreover, the speed limit has the strongest influence on vehicle operation, followed by facility type and driving style. Better understanding the impact on vehicle operation of ESC could help metropolitan planning organizations(MPOs) and regional transportation authorities predict vehicle operations and reduce the environmental footprint of motor vehicles.
文摘A general review of the socio-economic impact of the intelligent transport system (ITS) is pre-sented with a case study to demonstrate the data envelopment analysis method. Cost-benefit analyses are still the dominant method for evaluating ITS and other transport engineering projects, while cost effective analyses and multi-criteria appraisals are widely used to define and prioritize objectives by providing useful information for the most promising policy decisions. Both cost-benefit analyses and a data envelopment analysis method are applied to analyze the socio-economic impact of convoy driving systems. The main findings are that a convoy provides a worthwhile benefit-cost ratio when more than 30% of the traffics in the convoys and the traffic load exceeds 5500 vehicles/h for a three-lane motorway. The results also show that for a fixed percentage of convoys, increased demand will increase the data envelopment analysis method relative efficiency and that the neglect of certain output indicators of an ITS may result in underestimation of the system effects.
文摘Car-following models describe how one vehicle follows the preceding vehicles. In order to better módel and explain car-following dynamics, this paper categorizes the state of a traveling vehicle into three sub-processes: the starting (acceleration) process, the car-following process, and the stopping (deceleration) process. The starting process primarily involves vehicle acceleration behavior. The stopping process involves not only car-following behavior but also deceleration behavior. This paper regards both the stopping process and the starting process as spring systems. The car-following dynamics during the starting process and the stopping process is modeled in this paper. The parameters of the proposed models, which are represented in the form of trigonometric functions, possess explicit physical meaning and definitive ranges. We have calibrated the model of the starting process using data from the Traffic Engineering Handbook, and obtained reasonable results. Compared with traditional stimulus-response car-following models, this model can better explain traffic flow phenomena and driver behavior theory.
