Research on Global Optimization Algorithm of Integrated Energy and Thermal Management for Plug-In Hybrid Electric Vehicles.

Power distribution and battery thermal management are important technologies for improving the energy efficiency of plug-in hybrid electric vehicles (PHEVs). In response to the global optimization of integrated energy thermal management strategy (IETMS) for PHEVs, a dynamic programming algorithm based on adaptive grid optimization (AGO-DP) is proposed in this paper to improve optimization performance by reducing the optimization range of SOC and battery temperature, and adaptively adjusting the grid distribution of state variables according to the actual feasible region. The simulation results indicate that through AGO-DP optimization, the reduction ratio of the state feasible region is more than 30% under different driving conditions. Meanwhile, the algorithm can obtain better global optimal driving costs more rapidly and accurately than traditional dynamic programming algorithms (DP). The computation time is reduced by 33.29-84.67%, and the accuracy of the global optimal solution is improved by 0.94-16.85% compared to DP. The optimal control of the engine and air conditioning system is also more efficient and reasonable. Furthermore, AGO-DP is applied to explore IETMS energy-saving potential for PHEVs. It is found that the IETMS energy-saving potential range is 3.68-23.74% under various driving conditions, which increases the energy-saving potential by 0.55-3.26% compared to just doing the energy management.

Multi Source Electric Vehicles: Smooth Transition Algorithm for Transient Ripple Minimization.

Any engineering system involves transitions that reduce the performance of the system and lower its comfort. In the field of automotive engineering, the combination of multiple motors and multiple power sources is a trend that is being used to enhance hybrid electric vehicle (HEV) propulsion and autonomy. However, HEV riding comfort is significantly reduced because of high peaks that occur during the transition from a single power source to a multisource powering mode or from a single motor to a multiple motor traction mode. In this study, a novel model-based soft transition algorithm (STA) is used for the suppression of large transient ripples that occur during HEV drivetrain commutations and power source switches. In contrast to classical abrupt switching, the STA detects transitions, measures their rates, generates corresponding transition periods, and uses adequate transition functions to join the actual and the targeted operating points of a given HEV system variable. As a case study, the STA was applied to minimize the transition ripples that occur in a fuel cell-supercapacitor HEV. The transitions that occurred within the HEV were handled using two proposed transition functions which were: a linear-based transition function and a stair-based transition function. The simulation results show that, in addition to its ability to improve driving comfort by minimizing transient torque ripples and DC bus voltage fluctuations, the STA helps to increase the lifetime of the motor and power sources by reducing the currents drawn during the transitions. It is worth noting that the considered HEV runs on four-wheel drive when the load torque applied on it exceeds a specified torque threshold; otherwise, it operates in rear-wheel drive.

An Equivalent Consumption Minimization Strategy for a Parallel Plug-In Hybrid Electric Vehicle Based on an Environmental Perceiver.

An energy management strategy is a key technology used to exploit the energy-saving potential of a plug-in hybrid electric vehicle. This paper proposes the environmental perceiver-based equivalent consumption minimization strategy (EP-ECMS) for parallel plug-in hybrid vehicles. In this method, the traffic characteristic information obtained from the intelligent traffic system is used to guide the adjustment of the equivalence factor, improving the environmental adaptiveness of the equivalent consumption minimization strategy (ECMS). Two main works have been completed. First, a high-accuracy environmental perceiver was developed based on a graph convolutional network (GCN) and attention mechanism to complete the traffic state recognition of all graph regions based on historical information. Moreover, it provides the grade of the corresponding region where the vehicle is located (for the ECMS). Secondly, in the offline process, the search for the optimal equivalent factor is completed by using the Harris hawk optimization algorithm based on the representative working conditions under various grades. Based on the identified traffic grades in the online process, the optimized equivalence factor tables are checked for energy management control. The simulation results show that the improved EP-ECMS can achieve 7.25% energy consumption optimization compared with the traditional ECMS.

Energy Management Strategy of a Hybrid Power System Based on V2X Vehicle Speed Prediction.

Energy consumption in vehicle driving is greatly influenced by traffic scenarios, and the intelligent traffic system (ITS) has a key role in solving the real-time optimal control of hybrid vehicles. To this end, a new energy management control strategy based on vehicle-to-everything (V2X) communication for vehicle speed prediction was proposed to dynamically adjust the engine and motor power output according to the traffic conditions. This study is based on intelligent network connectivity technology to obtain forward traffic state data and use a deep learning algorithm to model vehicle speed prediction using the traffic state data. The energy economy function was modeled using the MATLAB/Sinumlink platform and validated with a plug-in hybrid vehicle model simulation. The results indicate that the proposed strategy improves the vehicle energy economy by 13.02% and reduces CO2 emissions by 16.04% under real vehicle driving conditions, compared with the conventional logic threshold-based control strategy.

Characterizing ammonia emission from light-duty gasoline vehicles under the influence of multiple factors and its correlation with conventional pollutants.

Ammonia (NH3), which is a precursor of secondary particulate matter (PM), can be produced through three-way catalyst (TWC) side reactions in light-duty gasoline vehicles (LDGVs), posing a threat to human health and air quality. To explore ammonia emission characteristics, 8 LDGVs and 1 hybrid electric light-duty vehicle (HEV) with various mileages traveled were analyzed with a chassis dynamometer system during regulation driving cycles. The emission factors of the adopted China VI in-use LDGVs were 7.04 ± 2.61 mg/km under cold-start conditions and 4.94 ± 1.69 mg/km under hot-start conditions. With increasing mileage traveled, the total ammonia emissions increased, and the difference between the cold/hot-start results decreased. The emissions of in-use LDGVs with bi-fuel engines were analyzed, and more ammonia was generated in the compressed natural gas (CNG) mode through the hydrocarbon (HC) reforming reaction. The relationship between the emissions of ammonia and conventional pollutants was established. During the initial cold-start phase, a delay in ammonia formation was observed, and the ammonia emissions conformed with the CO and HC emissions after exhaust heating. Vehicle specific power (VSP) analysis revealed that the interval of highest ammonia emissions corresponded to acceleration events at high speeds. For the HEV, the transition from motor to engine drive conditions contributed to ammonia emission occurrence because of the more pronounced cold-start events. The use of HEV technology could introduce additional uncertainties in controlling urban ammonia emissions. Detailed analysis of emission characteristics could provide data support for future research on ammonia emission standards and control strategies for LDGVs.

Dynamic coordinated control strategy of a dual-motor hybrid electric vehicle based on clutch friction torque observer.

The hybrid power system with dual motors and multiple clutches experiences significant torque fluctuation during mode switching process due to the different torque response characteristics of the motor and engine. To address this issue, this paper focuses on the estimation of clutch friction torque and the development of dynamic coordinated control strategies for the components. Firstly, based on the dynamic model of the novel dual-motor hybrid electric vehicle, a torque observer based on the Kalman filter algorithm is developed to predict the friction torque generated in the clutch sliding friction stage. Secondly, the control strategies are developed for the mode switching process from single-motor to dual-motor and from dual-motor to parallel drive on a co-simulation platform. Thirdly, a power level Hardware-In-the-Loop test platform is built, and the performance of the designed control strategies is verified by the HIL platform. The results show that for the mode switching process from dual-motor to parallel drive, compared with the control strategy using the engine target speed, the control strategy based on engine idle speed proposed in this paper reduces the clutch sliding friction work and the maximum longitudinal jerk of the vehicle by 42.5% and 25.4%, respectively.

New coordinated drive mode switching strategy for distributed drive electric vehicles with energy storage system.

High performance and comfort are key features recommended in hybrid electric vehicle (HEV) design. In this paper, a new coordination strategy is proposed to solve the issue of undesired torque jerks and large power ripples noticed respectively during drive mode commutations and power sources switching. The proposed coordinated switching strategy uses stair-based transition function to perform drive mode commutations and power source switching's within defined transition periods fitting the transient dynamics of power sources and traction machines. The proposed technique is applied on a battery/ supercapacitor electric vehicle whose traction is ensured by two permanent magnet synchronous machines controlled using direct torque control and linked to HEV front and rear wheels. Simulation results highlight that the proposed coordinated switching strategy has a noteworthy positive impact on enhancing HEV transient performance as DC bus fluctuations were reduced to a narrow band of 6 V and transient torque ripples were almost suppressed.

Sliding mode coordinated control of hybrid electric vehicle via finite-time control technique.

During the transient mode switching process of the hybrid electric vehicle (HEV) from motor driving mode to hybrid driving mode, dynamic coordinated control of different components is essential to improve the vehicle comfort and dynamic performance. The key to highly quality mode switching control includes fast and stable speed and/or displacement tracking of the engine and motor. The transient mode switching stages of the HEV is divided in this paper. On this basis, by combing the nonlinear sliding mode control and the finite-time stability theory, the global fast integral terminal sliding mode controller (GFITSMC) is designed for the transition stages involving clutch slip. The GFITSMC consists of the global fast integral terminal sliding mode surface (GFITSMS) and the non-smooth reaching law (NSRL). In order to improve the controller convergence and anti-disturbance performance, the proposed controller is synthesized from the perspective of finite-time stability. It is proved that, with proper NSRL and GFITSMS parameters, the speed and displacement tracking error of the motor and engine can reach the sliding mode surface and further converge to zero in a finite time. Simulation and hardware-in-the-loop (HIL) tests are performed to validate the effectiveness of the proposed control method. Research results demonstrate that the proposed strategy not only achieves faster transient mode switching by improving the state trajectory tracking performance, but also reduces the longitudinal jerk caused by the transient mode switching significantly.

A comprehensive review on isolated and non-isolated converter configuration and fast charging technology: For battery and plug in hybrid electric vehicle.

Electric vehicle systems are a promising future transportation system because they play an important role in reducing atmospheric carbon emission and have become a focal point of research and development in the present era. The emerging fast charging technology has the ability to have refueling experiences comparable to gasoline cars. This article discusses existing electric vehicle charging infrastructure with a particular emphasis on rapid charging technologies, which would be needed to meet current and potential EV refueling requirements. Various dc-dc converter topologies for battery electric and plug-in hybrid vehicles are compared and contrasted in this article in terms of performance, output power, current ripples, voltage ripples, conduction loss, recovery loss, switching frequency loss, reliability, durability, and cost. The architecture, benefits, and drawbacks of AC-DC and DC-DC converter topologies for rapid charging stations are also discussed in this article. Furthermore, this study addresses the crucial problems and difficulties associated with electric vehicle converters for direct current rapid charging. Eventually, technical and relevant contributions are provided for an electric vehicle system development.

The efficient operating parameter estimation for a simulated plug-in hybrid electric vehicle.

Hybrid electric vehicles (HEVs) and plug-in HEVs (PHEVs) are indispensable tools in reducing greenhouse gas emissions to fight the twin evils of pollution and climate change. In these vehicles, battery replacement and fuel costs are the major recurring costs over a lifetime. Hence, there is a growing attempt to develop strategies that reduce the long-run expenditure in these vehicles without compromising on performance levels. Further, an increase in the fuel economy is also required for the effective penetration of these vehicles in society. Here, the authors attempt to identify the optimal operating values for battery state of charge (SoC), power ratings of motor, and fuel converter to increase the battery life and fuel economy without degrading the vehicle performance. The simulations have been carried out on Ford C-Max Energi (2016) as a representative for PHEVs based on the Urban Dynamometer Driving Schedule (UDDS) and Highway (HWY) driving cycles. The software used for these simulations is the future automotive systems technology simulator (FASTSim), developed by the National Renewable Energy Laboratory (NREL). In this paper, firstly, the effect of important parameters like battery SoC, fuel converter power, and motor power on HEVs' driving range, battery life, fuel economy, cost, and charge-depleting range has been analyzed. Based on this analysis, the optimal values of the parameters have been estimated. These parameters have resulted in improvements of driving range by 4.3% and battery life by 18% at a minute cost of a 1% decrease in the charge-sustaining battery life and a 0.4-s increase in the time the car takes to hit 60 mph from the rest. This paper presents a simple, effective, and new approach that explores the effect of altering the existing design parameters on vehicle performance, without manipulating, adding, or deleting any component or controller. This can further be extended to study the impact of various other parameters in the proposed work and opens a way to explore other parameters that exist in various other components of XEVs (where X can be H/PH//F). This study will help in achieving optimal cost reduction in these vehicles.

The dataset of Japanese patents and patents' holding firms in green vehicle powertrains field.

In 2020, the Government of Japan declared "2050 carbon neutral" and launched a long-term strategy to create a "virtuous cycle of economy and environment". Japanese firms possess many technologies that contribute to decarbonization, which is important to expand investment for Green Technology (environmental technology) development. As automobiles are major contributors to greenhouse gas emissions [1], the technological shift towards vehicle powertrain systems is an attempt to lower problems like emissions of carbon dioxide, nitrogen oxides [2]. On the other hand, patent data are the most reliable business performance for applied research and development activities when investigating the knowledge domains or the technology evolution (Wand, 1997). Our paper describes a Japanese patents dataset of the vehicle powertrain systems for hybrid electric vehicle (HEV), battery electric vehicle (BEV) and fuel cell electric vehicles (FCEV). In this paper we create a method of bombinating international patent classification (IPC) and keywords to define "green" patents in vehicle powertrains field, using patent data which were applied to Japan Patent Office recorded on EPO's PATSTAT database during 2010∼2019 year. When analyze patents, it is necessary to consider the social situation of each country including language background, we collect patents description documents (abstracts and titles) not only written in English but also in Japanese. Finally, we build a database includes 6025 green patents' description documents and 266 patents' holding firms. With which we then identify 3756 HEV patents, 1716 BEV patents, and 553 FCEV patents. Data about patent holding firms is also appended. The full dataset may be useful to researchers who would like to do further search like natural language processing and machine learning on patent description documents, statistical data analysis for empirical economics.

Real-world emissions and fuel consumption of gasoline and hybrid light duty vehicles under local and regulatory drive cycles.

In this study, driving trajectory data from private vehicles were collected in Toronto, Canada to construct representative local drive cycles. In addition, real-driving emission testing for four conventional gasoline vehicles (ICEV) and one hybrid electric vehicle (HEV) was conducted in the same region using a Portable Emissions Measurement System. Instantaneous fuel consumption and emissions of Carbon Monoxide (CO), Nitrogen Oxides (NOx), and Particle Number (PN) were measured. The results for all vehicles indicate that the acceleration state tends to generate the highest emissions and fuel consumption with the largest variation due to higher power demand. When accelerating, the HEV was observed to generate four times more CO emissions than some ICEVs. Instantaneous fuel consumption and emissions were analyzed as a function of operating modes to estimate the fuel efficiency (FE) and emission factors (EF) associated with six representative local drive cycles and four regulatory drive cycles. With most regulatory drive cycles, vehicles can reach the labeled FE and EPA emission limits, except under the New York City Cycle with frequent stop-and-go conditions. In contrast, except for highway cycles, the FE of Toronto-specific drive cycles can hardly meet the labeled values. CO EFs of the HEV can be higher than ICEVs, while it is lower than the emission limit by 42% on average. ICEVs may exceed the CO limit by 131% under local highway cycles, while they can violate NOx and PN limits under local arterial cycles. The result of this study emphasizes the importance of local drive cycles and real driving emission tests.

Multi-objective energy management strategy for fuel cell hybrid electric vehicle based on stochastic model predictive control.

The fuel cell hybrid electric vehicle (FCHEV) has earned great interest among the automotive industry in recent years. However, the power allocation strategy between proton exchange membrane fuel cell (PEMFC) and lithium-ion battery remains a technological challenge. To conquer this problem, a multi-objective predictive energy management strategy (EMS) based on model predictive control (MPC) is proposed in this paper, combined with velocity forecast and driving pattern recognition. The comparative study is conducted to reveal the interaction between each optimization objectives. Simulation results illustrate that the proposed EMS could maintain SOC around reference, reduce fuel consumption by 6.67%, and avoid PEMFC degradation which caused by frequent start-off and rapid load change.

The impacts from cold start and road grade on real-world emissions and fuel consumption of gasoline, diesel and hybrid-electric light-duty passenger vehicles.

Progressively stringent regulations regarding vehicle emissions and fuel economy have spurred technology diversification in light-duty passenger vehicles (LDPVs). To assess the real-world emissions and fuel economy performances of hybrid electric vehicles (HEVs) compared to conventional internal combustion engine (ICE) vehicles, on-road measurements of ten gasoline, four diesel and six full hybrid LDPVs were performed using portable emissions measurement systems (PEMS) in Macao, China. The hot-running emission results indicate that the high emission risks of gasoline vehicles are associated with high mileage and old model years. Diesel vehicles are found to be the highest pollutant emitters in this study due to the intentional removal of aftertreatment systems. Under hot-running conditions, HEVs, as expected, could achieve carbon-reduction benefits of approximately 30 % (i.e., lower CO2 emissions and fuel consumption) compared to their conventional gasoline counterparts, while no measurable reduction in pollutant emissions was observed except in NOX (~70 % reduction). In contrast, the cold-start extra emissions (CSEEs) of CO2 reached 120-364 g/start for these HEVs, even exceeding the maximum values of conventional gasoline vehicles. However, the higher CO2 CSEEs of HEVs can be far offset by their hot-running emission reduction benefits. For tailpipe pollutants, the CSEEs of the HEVs were reduced by 21 %-68 % on average in comparison to those of conventional gasoline vehicles. Furthermore, strong correlations (R2 values of 0.69-0.89) between the road grades and relative emissions were observed. These results can provide necessary information regarding the improvement of future LDPV emission models and inventories.

Hall-effect based semi-fast AC on-board charging equipment for electric vehicles.

The expected increase in the penetration of electric vehicles (EV) and plug-in hybrid electric vehicles (PHEV) will produce unbalanced conditions, reactive power consumption and current harmonics drawn by the battery charging equipment, causing a great impact on the power quality of the future smart grid. A single-phase semi-fast electric vehicle battery charger is proposed in this paper. This ac on-board charging equipment can operate in grid-to-vehicle (G2V) mode, and also in vehicle-to-grid (V2G) mode, transferring the battery energy to the grid when the vehicle is parked. The charger is controlled with a Perfect Harmonic Cancellation (PHC) strategy, contributing to improve the grid power quality, since the current demanded or injected has no harmonic content and a high power factor. Hall-effect current and voltage transducers have been used in the sensor stage to carry out this control strategy. Experimental results with a laboratory prototype are presented.

Research on innovative plug-in hybrid electric vehicle comprehensive energy consumption evaluation method based on statistic energy consumption.

The plug-in hybrid electric vehicle not only has the advantages of low emissions from electric vehicles, but also takes advantage of the high specific energy and high specific power of petroleum fuels, which can significantly improve the emissions and fuel economy of traditional vehicles. Studying its comprehensive energy consumption evaluation method is an important part of analyzing the economics of plug-in hybrid electric vehicles. This paper first puts forward the concept of statistical energy consumption and then proposes an innovative calculation method of plug-in hybrid electric vehicle energy consumption based on statistical energy consumption by referring to and analyzing the energy consumption test regulations of the United States, the European Union, and China. Given the two use case assumptions of charge depleting mode priority and charge sustaining mode only, considering the fuel consumption and the energy consumption that converts electrical energy consumption to fuel consumption, the probability density function of travel mileage distribution and energy consumption is derived. Finally, the interpretation and analysis of statistical energy consumption evaluation results are carried out.

A-EMCS for PHEV based on real-time driving cycle prediction and personalized travel characteristics.

Energy management plays an important role in improving the fuel economy of plug-in hybrid electric vehicles (PHEV). Therefore, this paper proposes an improved adaptive equivalent consumption minimization strategy (A-ECMS) based on long-term target driving cycle recognition and short-term vehicle speed prediction, and adapt it to personalized travel characteristics. Two main contributions have been made to distinguish our work from exiting research. Firstly, online long-term driving cycle recognition and short-term speed prediction are considered simultaneously to adjust the equivalent factor (EF). Secondly, the dynamic programming (DP) algorithm is applied to the offline energy optimization process of A-ECMS based on typical driving cycles constructed according to personalized travel characteristics. The improved A-ECMS can optimize EF based on mileage, SOC, long-term driving cycle and real-time vehicle speed. In the offline part, typical driving cycles of a specific driver is constructed by analyzing personalized travel characteristics in the historical driving data, and optimal SOC consumption under each typical driving cycle is optimized by DP. In the online part, the SOC reference trajectory is obtained by recognizing the target driving cycle from Intelligent Traffic System, and short-term vehicle speed is predicted by Nonlinear Auto-Regressive (NAR) neural network which both adjust EF together. Simulation results show that compared with CD-CS, the fuel consumption of A-ECMS proposed in the paper is reduced by 8.7%.

Impact of PHEV in active distribution network under gas station network attack.

This paper proposes a novel method to analyze the impacts of plug-in hybrid electric vehicle (PHEV) charging on branch power flows and voltages of an active distribution network under gas station network attack. Specifically, when the gas station network is attacked and cannot provide refueling service, PHEVs running out of gasoline will be only driven in the electric vehicle (EV) mode, which will significantly increase PHEV charging load and lead to branch power flow increment and voltage drop or even voltage collapse in distribution network. To overcome the problem, the switch of PHEV operating mode (i.e., the EV mode and the combustion engine (CE) mode) is first analyzed by considering whether the remaining gasoline can satisfy daily gasoline consumption, and based on that, a novel model of the PHEV charging load is constructed. Then, an integrated approach including Nataf/normalization transformation and elementary transformation (ET) is employed to deal with the general correlation of spatially close distributed generations in the active distribution network. Furthermore, point estimate method (PEM) based probabilistic load flow (PLF) is used to analyze the impacts of PHEV charging on branch power flows and voltages of the active distribution network under gas station network attack. Finally, the proposed method is tested on a real coastal active distribution network, and simulation results verify that PHEV charging could result in continuous branch power flow increase and voltage decrease over a prolonged attack time. Moreover, the higher PHEV operating status (OS) leads to slower branch power flow growth and voltage drop, and a higher PHEV penetration level will exacerbate branch power flow increment and voltage limit violation over with the extension of the attack.

The global patents dataset on the vehicle powertrains of ICEV, HEV, and BEV.

Patent bibliometrics data are the most reliable business performance metric for applied research and development activities when investigating the knowledge domains or the technological evolution of vehicle powertrain technologies in the automotive industry. Our paper describes a global patents dataset for the internal combustion engine vehicles (ICEV), hybrid electric vehicles (HEV) and battery electric vehicles (BEV) over 1985-2016. We extracted the patents granted in each powertrain field from Thomson Reuters' Derwent Innovations Index (DII). We applied a combined search strategy of international patent classifications (IPCs) and keywords as well as 'patent families' and 'priority dates' to construct our global patents dataset. This strategy returned a total of 78,732 patents, within which we identified 49,154 ICEV patents; 10,888 HEV patents; and 18,690 BEV patents. Our database includes numerous descriptive features of each patent such as title, abstract, claim, priority, application and publication dates, IPCs, assignees/applicants, inventors, and cited references. These data are associated with the research article 'The evolution of dynamic interactions between the knowledge development of powertrain systems' [1]. The full dataset, which is attached to this article, may be useful to both researchers and practitioners interested in investigating, modelling or forecasting the complexity and evolution of the technical and knowledge domains of the vehicle powertrains, across a variety of instruments such as social network analysis and regression models.

Dataset on the global patent networks within and between vehicle powertrain technologies - Cases of ICEV, HEV, and BEV.

The emergence of networks is a crucial channel for automotive organisations to build and diffuse the required environmental innovations in the transportation sector and accelerate the transition to the green mobility economy. This article contains the dataset regarding the global patents networks shaped both within and between the three vehicle powertrains of internal combustion engine vehicle (ICEV), hybrid electric vehicle (HEV) and battery electric vehicle (BEV) for the period of 1985-2016. The data was acquired from Thomson Reuters' Derwent Innovations Index (DII) platform using the elements of 'patent families' and 'priority dates'. We describe the dataset for the three major automotive periods of 'towards sustainable mobility' (1985-1996), 'towards hybridisation' (1997-2007), and 'towards mass commercialisation' (2008-2016). The dataset bears on two levels, individual and mutual, and we used a separate combined search strategy of keywords and IPCs codes (international patent classification) for each level. At individual level, we explored the internal network features of each powertrain individually (i.e. ICEV, HEV, and BEV). Monitoring a total of 78,732 patents in the three individual powertrain networks, we discovered a total of 1856 unique parent organisations connecting vis-à-vis 5849 bilateral relationships and operating around 4450 joint patents. At mutual level, we explored the mutually common network features of the powertrains (i.e. ICEV-HEV, HEV-BEV, and BEV-ICEV). Monitoring a total of 4702 patents in the three mutual powertrain networks, we discovered a total of 102 unique parent organisations connecting vis-à-vis 384 bilateral relationships and operating around 303 joint patents. These organisations were found specialised around 435 unique subgroup-level IPC codes, of which 134 codes were related to environmentally friendly innovations. The dataset presented in this article is used in [1] and allows researchers not only to map and model the network dynamics and structures within and between the powertrains at global level, but also to analyse and forecast their knowledge flows, technical domains and environmental innovations aspect, using a wide range of models such as social network analysis or regression.