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In the field of intelligent transportation, the planning of traffic flows that meet energy-efficient driving requirements necessitates the acquisition of energy consumption data for each vehicle within the traffic flow. The current methods for calculating vehicle energy consumption generally rely on longitudinal dynamics models, which require comprehensive knowledge of all vehicle power system parameters. While this approach is feasible for individual vehicle models, it becomes impractical for a large number of vehicle types. This paper proposes a digital model for vehicle driving energy consumption using vehicle speed, acceleration, and battery state of charge (SOC) as inputs and energy consumption as output. The model is trained using an optimized CNN-BiLSTM-Attention (OCBA) network architecture. In comparison to other methods, the OCBA-trained model for predicting PHEV driving energy consumption is more accurate in simulating the time-dependency between SOC and instantaneous fuel and power consumption, as well as the power distribution relationship within PHEVs. This provides an excellent framework for the digital modeling of complex power systems with multiple power sources. The model requires only 54 vehicle tests for training, which is significantly fewer than over 2000 tests typically needed to obtain parameters for power system components. The model's prediction error for fuel consumption under unknown conditions is reduced to 5%, outperforming the standard error benchmark of 10%. Furthermore, the model demonstrates high generalization capability with an R2 value of 0.97 for unknown conditions. [ABSTRACT FROM AUTHOR]

In response to the strength performance requirements of the new energy Off road Vehicle steering system, this article analyzes and studies the strength performance and failure modes of the new energy Off road Vehicle steering rod based on a certain new energy Off road Vehicle model through simulation, bench experiments, and vehicle strength testing teams. Based on various vehicle misuse conditions, the force law of the steering rod under different vehicle driving conditions was analyzed, and a load spectrum for the steering rod strength bench test was developed to meet the actual vehicle usage conditions. The results indicate that the steering rod strength failure is mainly manifested as bending failure of the compression rod under alternating tension and compression conditions. Finally, based on the stability principle of the compression rod, the requirements for the strength performance index of the steering rod are proposed. The research results can provide certain technical support for the strength assessment of the vehicle steering rod. [ABSTRACT FROM AUTHOR]

This paper presents the process of creating a model for electric vehicle (EV) energy consumption, enabling the rapid generation of results and the creation of energy maps. The most robust validation indicators were exhibited by an artificial intelligence method, specifically neural networks. Within this framework, two predictive models for EV energy consumption were developed for winter and summer conditions, based on actual driving cycles. These models hold particular significance for microscale road analyses. The resultant model, for test data in summer conditions, demonstrates validation indicators of an R2 of 86% and an MSE of 1.4, while, for winter conditions, its values are 89% and 2.8, respectively, confirming its high precision. The paper also presents exemplary applications of the developed models, utilizing both real and simulated microscale data. The results obtained and the presented methodology can be especially advantageous for decision makers in the management of city roads and infrastructure planners, aiding both cognitive understanding and the better planning of charging infrastructure networks. [ABSTRACT FROM AUTHOR]

Hybrid electric vehicles are certainly one of the key solutions for improving fuel efficiency and reducing emissions, especially in terms of special vehicles and with the use of CO2-neutral fuels. Determining the energy management strategy and finding the optimal solution with regard to the aforementioned goals remains one of the main challenges in the design of HEVs. This paper presents a new vehicle modeling method, with an emphasis on HEVs, which is based on the frequency analysis of emissions and consumption according to the current specific traction power of the vehicle. An evaluation of the newly introduced model in the RDE, NEDC and WLTP cycle was performed, and the results were compared with the standard verified vehicle model that was created in AVL's CruiseM R2021.2 software package. Positive traction energies have positive deviations of between 0.35% and 2.85%. The largest deviation in CO2 emissions was recorded for the HEV model in the RDE cycle and in the non-hybrid model in the WLTP cycle and were 3.79% and 4.4%, respectively. All other combinations of cycle and vehicles had deviations of up to about 1%. As expected, the largest relative deviations were recorded for NOx emissions and ranged from 0.13% to 9.62% for HEVs in the WLTP cycle. [ABSTRACT FROM AUTHOR]

Current emission models primarily focus on traditional combustion vehicles and may not accurately represent emissions from the increasingly diverse vehicle fleet. The growing presence of hybrid and electric vehicles requires the development of accurate emission models to measure the emissions and energy consumption of these vehicles. This issue is particularly relevant for low-emission zones within cities, where effective mobility planning relies on simulation models using continuously updated databases. This research presents a two-dimensional emission model for hybrid vehicles, employing artificial neural networks for low-emission zones. The key outcome is the methodology developed to create a CO2 emission model tailored for hybrid vehicles, which can be used to simulate various road solutions. The CO2 emission model achieved an R2 coefficient of 0.73 and an MSE of 0.91, offering valuable information for further advancements in emission modelling. [ABSTRACT FROM AUTHOR]

Accurate prediction of vehicle acceleration has significant practical applications. Deep learning, as one of the methods for acceleration prediction, has shown promising applications in acceleration prediction. However, due to the influence of multiple factors on acceleration, a single data model may not be suitable for various driving scenarios. Therefore, this paper proposes a hybrid approach for vehicle acceleration prediction by combining clustering and deep learning techniques. Based on historical data of vehicle speed, acceleration, and distance to the preceding vehicle, the proposed method first clusters the acceleration patterns of vehicles. Subsequently, different prediction models and parameters are applied to each cluster, aiming to improve the prediction accuracy. By considering the unique characteristics of each cluster, the proposed method can effectively capture the diverse acceleration patterns. Experimental results demonstrate the superiority of the proposed approach in terms of prediction accuracy compared to benchmarks. This paper contributes to the advancement of sensor data processing and artificial intelligence techniques in the field of vehicle acceleration prediction. The proposed hybrid method has the potential to enhance the accuracy and reliability of acceleration prediction, enabling applications in various domains, such as autonomous driving, traffic management, and vehicle control. [ABSTRACT FROM AUTHOR]

Vehicular safety is of considerable significance to the intelligent development of hybrid vehicles. However, the real-time stability control or reasonable torque distribution under the extreme road conditions remain a huge challenge due to the multiple uncertain parameters and difficulties to reconcile the handling and stability performance. To address the above problems for a through-the-road (TTR) 4-wheel-drive (4WD) hybrid vehicle, this study provides a handling and stability management (HSM) approach by incorporating the offline optimization rules and on-line model predictive control (MPC). Firstly, the vehicle dynamic model with seven degrees of freedom (7-DOF) is used to offline extract torque distribution rules (Offline-ETDR), and the online MPC feedback (Online-MPCF) is utilized to compensate the extra torque requirements for the poor effect under the extreme conditions. Accordingly, the offline optimization results and online correction are fused to provide the total torque demand given the real-time road condition detection. Finally, the real vehicle test are implemented to validate the effectiveness of the proposed torque coordination strategy. In comparison to the vehicle with no torque control strategy, the proposed method significantly improves the vehicle's cornering ability while also ensuring the high stability performance. [ABSTRACT FROM AUTHOR]

This work is focused on the simulation of a complete hybrid bus vehicle model performing a real-world driving cycle. The simulation framework consists of a coupled co-simulation environment, where all the vehicle sub-system models are linked to achieve a real time exchange of input and output signals. In the vehicle model also the electric devices of the powertrain and accumulation system are included. This co-simulation platform is applied to investigate the hybridization of a 12-m city bus, performing a typical urban driving mission. A comparison between the conventional powertrain is performed against the hybridized version, to highlight the advantages and challenges. In particular, the novelty of this modeling approach is that the IC engine simulation does not rely on pre-processed look-up tables, but exploits a high-fidelity one-dimensional thermo-fluid dynamic model. However, it was necessary to develop a fast simulation methodology to exploit this predictive tool, achieving a low computational cost. The 1D engine model is first validated against the experimental engine map data available, showing a good model predictivity. Then the 1D engine model and the other models of the powertrain are coupled to the vehicle model, in order to follow the prescribed velocity profile of the driving cycle. The complete model is applied under different conditions, to evaluate the impact on performance and emissions and assess the simulation predictivity. [ABSTRACT FROM AUTHOR]

In order to explore the influence of the thermal management system (TMS) on vehicle energy management and tap the energy saving potential of TMS, this study establishes a vehicle energy management strategy control model oriented to reduce energy consumption of the TMS based on MATLAB/Simulink for a plug-in hybrid electric vehicle with planetary hybrid configuration. Firstly, a simulation model of vehicle dynamic machine - electric - thermal coupling working process is introduced, to evaluate the impact of TMS in the high and low temperature environment on energy consumption of the vehicle running. Then, based on the equivalent fuel consumption minimum strategy (ECMS) and considering the influence of TMS on energy consumption, an adaptive equivalent consumption minimum strategy model considering thermal characteristics (TAECMS) is established, which propose an improved adaptive equivalent factor adjustment method considering the thermal characteristics of the system is proposed. By establishing the Hamiltonian function to achieve the goal of minimum equivalent fuel consumption, considering the temperature penalty, the power of the engine and the power of the battery is reasonably allocated. Finally, the TAECMS control strategy achieves fuel saving of 6.2 % and 8.4 % respectively in high and low temperature environments through simulation verification and comparison. [ABSTRACT FROM AUTHOR]

In the development of a range-extended electric vehicle, it is necessary to determine the control strategy of the range extender (REX) and evaluate the influence of its performance and operating conditions. In this paper, we propose a range-extender in-the-loop (REIL) method that is used to predict the vehicle fuel consumption more accurately. To develop the REIL method and perform experimental verification, the vehicle plant model, electric-drive control strategy, and REX control strategy are integrated into a vehicle control unit (VCU) for REX control, bench data acquisition, and bench monitoring. The VCU controls the REX to meet the power requirements of the simulated vehicle operations under a given driving cycle. The fuel consumed by the engine and power generated by the integrated starter-and-generator are measured during the tests, which are used in the vehicle fuel consumption calculation. We first describe the methodology and implementation of the REIL, and subsequently discuss the predicted results of a range-extended electric vehicle equipped with a 30 kW REX under the WLTC driving cycle. Comparing the predicted results of the REIL and software simulation methods, it is demonstrated that the REIL method improves the prediction accuracy of vehicle fuel consumption. [ABSTRACT FROM AUTHOR]

Ever stricter emission regulations, declining petroleum resources, increasing pollution, and global warming triggered an interest in e-mobility. Although fully electrified transportation is targeted, hybrid electric vehicles have become attractive during this transition period due to reasons such as battery challenges, range anxiety, grid capacity, and charging infrastructure. Hybrid electrical vehicles require challenging energy management systems due to the increasing number of components and energy conversions. This paper aims to introduce a simple yet effective control scheme to control the battery state-of-charge (SOC) and regenerative braking of a hybrid electric vehicle. For this purpose, a fuzzy logic controller is developed, three inputs as the SOC, driver demand, and vehicle velocity are defined. Instead of torque or power requirement, which are commonly used as controller inputs in the literature, a more straightforward method is adopted by using the accelerator and brake pedal positions. The controller manages the engine power and regenerative braking intensity. A series hybrid electric vehicle model is created in the MATLAB/Simulink environment to validate the performance of the proposed controller. The proposed controller aims to keep the SOC between 30-40% after charge depleting mode, and ensures prevention of regenerative braking at high SOC values to prevent overcharging. Simulations have been performed according to NEDC and WLTC, show that the proposed controller is able to realize design objectives. [ABSTRACT FROM AUTHOR]

Since the coupling relationship of excitation sources is complicated, meantime the motor torque changes quickly under the acceleration condition, the problem of torsional vibration is prominent. This paper studies an active damping control (ADC) strategy for a parallel hybrid electric vehicle (HEV) under acceleration condition. Primarily, a full-order dynamic model is built, and the corresponding motion equations are derived. Moreover, a controller design–oriented model is established based on model reduction algorithm. Furthermore, a method that considers time delay characteristics of actuator based on model predictive control (MPC) theory is proposed to solve the torsional vibration problem. The controller handles delay characteristics of an actuator by state-space reorganization method, and the optimal control sequence is obtained by solving the objective function. Finally, the controller is tested using Simulink simulation and hardware-in-loop simulation platform, which mainly includes the verification of model reduction and vibration damping effect. The results show that simplified third-order model has a good consistency with the original full-order model in time and frequency domain. Meanwhile, the designed controller has a considerable damping effect and ensures the comfort performance of the vehicle. This study provides an important reference for vibration control of the hybrid powertrain. [ABSTRACT FROM AUTHOR]

The use of simulation and modelling has been proposed for determining the excitations to be applied in the procedures of laboratory testing of a car's structural components, without the need to test the complete vehicle. The paper presents the general concept as well as an example of the procedure. It covers determining the spectrum and time-domain realization of a load on a selected node of the vehicle structure under durability tests. The author used both the mathematical and physical model of the tractor-semitrailer unit, where the input was considered as a random process resulting from the road profile. He calculated the transmittance modules and the power spectral densities of the vertical force on the joint between the tractor's fifth wheel and the kingpin of the semitrailer and the extreme values of the dynamic components of this force. The inverse discrete Fourier transform makes it possible to generate the realization of the said force. It can be used in durability studies. The limitation of the work to the testing of a specific structural node reduces the scope and, in consequence, the cost of the process. The method presented may also be used for various types of vehicle models, including hybrid and electric vehicles. [ABSTRACT FROM AUTHOR]

Despite massive investment and carbon-neutral transition goals set around the world, gasoline-based internal combustion engine vehicles still form an absolute majority in the transportation sector. With the advent of technology, access to electricity, uncertainties in fuel prices and health awareness, people worldwide are moving towards a better, reliable, cost-effective and environmentally friendly mode of transportation, a hybrid electric vehicle. Such a vehicle, with its powerful electric motor and compact gasoline-based engine, offers better efficiency in terms of operating cost and reliability. Considering the advent of hybrid electric vehicles taking pace, studies related to its architecture types, power flow dynamics, control and modelling of its various components will form an essential part of the automobile industry and research. This paper proposes a power flow topology for a hybrid electric vehicle with series-parallel architecture. The developed vehicle model with such an architecture type consists of three main sub-systems: the electrical system, the control system and the mechanical system. The presented power flow topology being modelled and analysed in detail in the Simulink tool is being implemented via a mode logic controller, which forms part of the central control system. The developed hybrid electric vehicle model demonstrates various modes of operation, from starting to accelerating to de-accelerating and then finally coming to a complete rest. Each mode yields and explains the following: the vehicle reference speed; the engine and generator turn functions on/off; the dc bus and battery voltage; the motor, battery, and generator current; the motor, generator, and engine speed; engine torque; engine power; throttle demand; and the vehicle's actual speed. The results thus obtained show that the waveforms associated with such topology, during its various modes of operation, are pretty stable and acceptable, thereby depicting and validating the operation of the developed hybrid electric vehicle model with proposed power flow topology in a precise and transparent manner. [ABSTRACT FROM AUTHOR]

The Equivalent Consumption Minimization Strategy (ECMS) is a well-known control strategy for the definition of optimal power-split in hybrid-electric vehicles, because of its effectiveness and reduced calibration effort. In this kind of Energy Management Systems (EMS), the correct identification of an equivalence factor (K), which translates electric power in equivalent fuel consumption, is of paramount importance. To guarantee charge sustaining operation, the K factor must be adjusted to different mission profiles. Adaptive ECMS (A-ECMS) techniques have thus been introduced, which automatically determine the optimal equivalence factor based on the vehicle mission. The aim of this research activity is to assess the potential in terms of fuel consumption and charge sustainability of different A-ECMS techniques on a gasoline hybrid-electric passenger car. First, the 0D vehicle and powertrain model was developed in the commercial CAE software GT-SUITE. An ECMS-based EMS was used to control the baseline powertrain and three alternative versions of an auto-adaptive algorithm were implemented on top of that. The first A-ECMS under study was based on feedback from the battery State of Charge, while the second and third on a Driving Pattern Recognition/Prediction algorithm. Fuel consumption was assessed using the New European Driving Cycle (NEDC), the Worldwide Harmonized Light Vehicles Test Cycle (WLTC) and Real Driving Emissions (RDE) driving cycles by means of numerical simulation. A potential improvement of up to 4% Fuel Economy was ultimately achieved on an RDE driving cycle with respect to the baseline ECMS. [ABSTRACT FROM AUTHOR]

The key challenge in the innovation of hybrid electric vehicles is the energy management control strategies to split the energy between the sources. The objective of adding an auxiliary source of power to vehicles is to minimize battery stress as well as energy consumption rate (ECR) of the main source and to increase the range of the vehicle. Based on Taguchi and grey relational analysis, the optimal control factors were determined for each of the desired performance factors. In this experimental design, control strategy, vehicle model, battery SOC, and UC SOC are considered as control factors, and ECR, range of the vehicle, and battery stress remain as vehicle performance factors. The confirmatory results show the minimal error between the initial set and the optimum factor to be 7.68 for ECR, 8.54 for range, and 5.49 for battery stress, respectively. The results also reveal the importance of fuzzy-based strategy on hybridization of energy storage systems in electric vehicles and also identified the most and the least influencing factors that affect the vehicle performance. [ABSTRACT FROM AUTHOR]

Current environmental concerns on nitrogen oxides (NO x) and particulate matter (PM) emissions caused by diesel engines have led researchers to be interested in investigating vehicles with alternative power sources. Because of this reason, vehicle models with SI engine were adopted in the conducted study. Firstly, as an initial step, 1-D SI engine models were created with use of AVL Boost software. A four-cylinder engine model was created for conventional vehicle model, while a two-cylinder downsized engine was adopted as a subsystem of hybrid vehicle model. The models were based on experimental data obtained from a laboratory test setup with a single-cylinder engine. Subsequently, detailed engine maps on emissions and fuel consumption were generated with the developed ANN model. The fuel consumption and emission data, which were gathered from NEDC and WLTC simulations, were compared for conventional ICE, PEM FC and PEM FC + ICE powered vehicles with the help of the vehicle model which was developed by using Matlab Simulink software. Based on the results, it was concluded that there might be sufficient improvement in fuel consumption and significant improvement in emissions with the use of PEM FC that a hybrid driving system (PEM FC + ICE) can be utilized, and that emissions can be at 0 with the sole use of PEM FC. • The PEMFC power source resulted in a significant reduction in fuel consumption. • The use of ICE-PEMFC provided slightly lower fuel consumption reduction than PEMFC. • FC reduction by ICE-PEMFC was higher under NEDC due to longer low power regions. • ICE-PEMFC model led to remarkable improvements in emissions especially in CO and HC. • NOx reduction by ICE-PEMFC was lower due to more efficient operations of ICE. [ABSTRACT FROM AUTHOR]

In this article, we aim to investigate the path following control problem for four-wheel-independent-drive electric vehicles with consideration of modeling errors and complex driving scenarios. In order to guarantee operational safety and robustness, a supertwisting second-order sliding mode (SOSM) control strategy is employed to suppress the heavy chattering issue existing in the traditional sliding mode control (SMC). To degrade the lumped disturbance, a nonlinear disturbance observer (NDOB) technique is proposed to estimate the disturbance and compensate the modeling error. Then, a composite control strategy, which combines the SMC algorithm and NDOB technique is proposed based on stability analysis of the corresponding closed-loop system. Finally, hardware-in-the-loop experimental results show that the supertwisting SOSM control algorithm with the NDOB technique can achieve better tracking performance and suppress the chattering phenomenon. [ABSTRACT FROM AUTHOR]

Due to the lack of precision in the modeling of the vehicle power system, the fault diagnosis and alarm accuracy of the current hybrid vehicle dynamic performance fault alarm method is reduced. Therefore, a hybrid vehicle dynamic performance fault alarm method based on hydraulic technology is designed. The power system model of hybrid electric vehicle is built by hydraulic technology, which is the data base of fault diagnosis. The method of self-diagnosis and the knowledge base of fault diagnosis are used to realize the fault diagnosis of vehicle power system. The interface between mobile network and on-board information is selected to realize vehicle fault alarm by setting alarm information content. The fault diagnosis accuracy of this method is always between 88.5% and 94.5%, the fault diagnosis time is less than 0.5 s, and the highest effective failure alarm rate is 99.8%. The experimental results show that the research method has the advantages of high accuracy, short diagnosis time, high effective alarm rate, low cost, superior economic performance and good application effect. [ABSTRACT FROM AUTHOR]

The vehicle is interfered by the lateral wind in the traditional anti-skid control method, which results in a bad anti-skid control effect of electric vehicle torque drive. For this reason, a method of torque adaptive drive anti-skid control for electric vehicle is proposed. In order to realize the driving force control of distributed driving electric vehicle, the driving motor is modeled and simplified. A battery model was established to analyze the influence of the bus voltage fluctuation on the motor torque response. The power system model is added to the existing vehicle model to complete the simulation model design of the distributed drive electric vehicle. According to the characteristics of the friction circle of the tire, the driving torque of the driving wheel is analyzed. In order to resist the influence of interference factors on vehicles during vehicle movement, through the concept of vehicle stability control, the left and right driving torque is dynamically adjusted by using the obtained ideal driving force distribution ratio of front and rear axles. The simulation results show that the proposed method can effectively increase the driving torque and improve the fitting coefficient of the driving anti-skid torque. [ABSTRACT FROM AUTHOR]

Summary: Electric vehicle (EV) is considered as a critical requirement to the future development of transportation. However, the battery performance in terms of power density and energy density limits the use of EVs. An energy management system (EMS) of plug‐in hybrid electric vehicle (PHEV) is very critical to achieve successful transition from the conventional vehicle to the pure electric vehicle (PEV). This paper proposes a hybrid EMS for the series‐parallel PHEV utilising a rule‐based control strategy and genetic algorithm (GA)‐based optimisation technique to overcome the battery limitations. A mathematical model was developed and verified by conducting simulation studies using the vehicle model from ADVISOR database and the GA Optimization Toolbox (GAOT) in the Matlab Simulink environment. The simulation results show that the GA optimization successfully achieved the sub‐targets set in the fitness function. To show the effectiveness of the proposed technique, the results were compared with the simulation results of a single function of the designed rule‐based control strategy—the proposed EMS achieved a significant improvement in the hydrocarbon (HC) emission and NOx emission. [ABSTRACT FROM AUTHOR]

[Display omitted] • Novel predictive ECMS algorithm for hybrid trucks based on projected power demand. • Algorithm operation validated on commercial plug-in hybrid electric truck. • Up to 5.9% energy efficiency gains over a municipal operation profile. • Power-predictive ECMS for energy management brings real CO 2 benefits. This paper examines potential fuel consumption savings through the implementation of a predictive equivalent consumption minimisation strategy, as a control algorithm in the energy management system of hybrid heavy-duty vehicles. A plug-in hybrid electric truck simulation model was developed as a demonstrator and was validated with experimental data. The baseline vehicle model was based on the same algorithm as VECTO, which is further extended in the current study by novel factors that contain the average predicted power demand. The potential benefits of the proposed algorithm are examined for different heavy-duty categories over regulatory mission profiles in both charge-sustain and charge-depletion modes. The results show that the fuel consumption can be reduced by up to 5.9 % and 4.4% in charge sustaining and charge depleting modes, respectively, depending on the mission profile. If such an algorithm is adopted by commercial vehicles, this could lead to substantial CO 2 and fuel consumption savings on the road. [ABSTRACT FROM AUTHOR]

Different electric vehicle (EV) charging algorithms result in different EV charging load profiles, that if aggregated, influence the power grid operation. The existing EV charging demand models are either based on the charging status upon EV arrival or smart charging algorithms reinforced with particular charging methods and/or charging levels. This article proposes a new data-driven approach for EV charging load modeling. We first introduce a mathematical model that characterizes the flexibility of EV charging demand. Advanced simulation procedures are then proposed to identify the parameters of different EV load models and simulate EV charging demand under different electricity market realizations. The proposed EV load modeling approach can simulate different EV operation schedules, charging levels, and customer participation as a benchmark system. The proposed framework will also provide informative guidelines to transmission system operators for EV charging infrastructure planning in modern power systems. [ABSTRACT FROM AUTHOR]

Summary: Energy consumption is increasing in parallel with the population and technological advancements. Albeit the energy efficiency efforts, industrial consumption, and transportation have quite important roles in this increase. The electric vehicles (EVs) are expected to constitute a positive solution to mitigate energy use in transportation. Increasing penetration of EVs into public transport provides new opportunities both for environmental sustainability and transportation infrastructure. To project the potential operator revenue, we set up a hybrid model (solar and storage), which optimizes scheduling for the EV charging unit and evaluates environmental and economic benefits. Different operational scenarios of PV‐assisted charging stations will be simulated and solved with Rule‐based decision making. Since regulations have an important role in building the optimization model, scenarios are created considering different European regulations. Uncertainties depending on the traffic jam (arrival‐departure times), technological differences of the charging stations are encountered in the model. The results are compared among three different scenarios. Net present value of the system changes for different scenarios and electricity sale price is one of the main factors. This value tends to increase after an annual electricity sale price increase rate of 11%. Novelty statement In this study, the effect of different economic parameters on an electric vehicle charging station were analyzed. A system with electric vehicles, solar panel support, and external battery was mathematically modeled and a case study was done. [ABSTRACT FROM AUTHOR]

A hybrid electric vehicle is powered by: the internal combustion engine and the battery-powered electric motor. These sources have specific operational characteristics, and it is necessary to match these characteristics for the efficient and smooth functioning of the vehicle. The nonlinearity and uncertainties in hybrid electric vehicle model require an intelligent controller to control the energy sharing between battery and engine. In this work, a fuzzy logic-enabled energy management strategy for the hybrid electric vehicle based on torque demand, battery state of charge and regenerative braking is designed and implemented. The proposed energy management strategy allows engine and motor to maneuver in their efficient operating regions. The designed hybrid electric vehicle and its control strategy follow the driver commands and regulations on vehicle performance and liquid fuel consumption. MATLAB/Simulink is used to carry out simulations, and then, the whole system is validated in real time on hardware-in-the-loop testing platform. This work employs an FPGA-based MicroLabBox hardware controller to validate real-time behavior. The proposed scheme results in better fuel economy, faster response and almost nil mismatch between desired and achieved vehicle speeds. [ABSTRACT FROM AUTHOR]

Vehicle stability control should accurately interpret the driving intention and ensure that the actual state of the vehicle is as consistent as possible with the desired state. This paper proposes a vehicle stability control strategy, which is based on recognition of the driver's turning intention, for a dual-motor drive electric vehicle. A hybrid model consisting of Gaussian mixture hidden Markov (GHMM) and Generalized Growing and Pruning RBF (GGAP-RBF) neural network is constructed to recognize the driver turning intention in real time. The turning urgency coefficient, which is computed on the basis of the recognition results, is used to establish a modified reference model for vehicle stability control. Then, the upper controller of the vehicle stability control system is constructed using the linear model predictive control theory. The minimum of the quadratic sum of the working load rate of the vehicle tire is taken as the optimization objective. The tire-road adhesion condition, performance of the motor and braking system, and state of the motor are taken as constraints. In addition, a lower controller is established for the vehicle stability control system, with the task of optimizing the allocation of additional yaw moment. Finally, vehicle tests were carried out by conducting double-lane change and single-lane change experiments on a platform for dual-motor drive electric vehicles by using the virtual controller of the A&D5435 hardware. The results show that the stability control system functions appropriately using this control strategy and effectively improves the stability of the vehicle. [ABSTRACT FROM AUTHOR]

One of the main research areas of the automotive sector is energy consumption and the reduction of pollutant emissions. Therefore, the hybrid electrical vehicle Vehicle electrical hybrid (VEH) is a possible approach to improve the vehicle components or its architecture to maximize system efficiency. This vehicle benefits from levels of consumption and emission of pollutants lower than the thermal vehicle. The problem is then to find the right split power between the two hybrid sources, the internal combustion engine (ICE) and the electrical machines (EM) to minimize fuel consumption. These energy management strategies are the algorithms that achieve instant split power between the two sources in the hybrid electric vehicle (HEV) while controlling the battery state of charge (SOC), in order to improve the fuel economy and optimize the performance HEV. In this research, a parametric study on the impact of optimization of the energy management strategy on the optimal operation of the vehicle is presented. A hybrid vehicle model is described and rules-based energy management strategy is presented. Later these rules are optimized with an optimization algorithm based on Grey Wolf optimizer (GWO). It is a new meta-heuristic based on the population. The results of the simulation make it possible to deduce the impact of this optimal strategy of energy management on fuel consumption and CO2 emissions. The obtained results show that the optimal operation of the vehicle varies according to the best energy management strategies. [ABSTRACT FROM AUTHOR]

Hybrid Electric Vehicles (HEV) offer improved fuel efficiency compared to conventional vehicles at the expense of adding complexity and at times, reduced total power. As a result, HEV generally lack the dynamic performance that customers enjoy. To address this issue, the paper presents a HEV with electric All-Wheel-Drive capabilities via the use of torque vectoring electric rear axle drive (TVeRAD) to power the rear axle. The addition of TVeRAD to a front wheel drive HEV improves the total power output. To improve the handling characteristics of the vehicle, the TVeRAD provides torque vectoring at the rear axle. A bond graph model of the drivetrain is developed and used in co-simulation with CarSim. The paper proposes a control system which utilises control allocation to optimise tyre forces. The proposed control system is tested in the simulation environment with a high fidelity CarSim vehicle model. Simulation results show the control system is able to maximise vehicle longitudinal performance while avoiding tyre saturation on low mu surfaces. More importantly, the control system is able to track the desired yaw moment request on a high speed double lane change manoeuvre through the use of the TVeRAD to improve the handling characteristic of the vehicle. [ABSTRACT FROM AUTHOR]

The parameters of the power system and the control strategy parameters are coupled,they work together to affect the power, economy and emission of the plug-in hybrid electric vehicle. Firstly, the real-time optimal control strategy based on the optimum of the vehicle power system's efficiency is developed. Secondly, the cost of the power system and indicators of vehicle's power performance are taken as constraints, the fuel consumption and emissions considering the engine's cold-heat effect are taken as the optimization objective, and in order to obtain the optimum solution of the power system's parameters and control strategy parameters, the standard genetic algorithm (SGA) and the quantum genetic algorithm (QGA) are applied to coordinated optimize them respectively. Finally, the simulation model of the vehicle system is established. After simulation, the simulation results show that compared with the original vehicle, the fuel economy and emission performance of the vehicle which parameters optimized by SGA and QGA has greatly improved, compared to the parameters optimization by SGA, the optimization by QGA can suppress premature convergence and have better fuel economy and emission performance. [ABSTRACT FROM AUTHOR]

A method for the optimal design of complex systems is developed by effectively combining multi-objective optimization and analytical target cascading techniques. The complex systems with high dimensionality are partitioned into manageable subsystems that can be optimized using dedicated algorithms. The multiple objective functions in each subsystem are treated simultaneously, and the interactions between subsystems are managed using linking variables and shared variables. The analytical target cascading algorithm ensures the convergence of the optimal solution that meets the system level targets while complying with the subsystem level constraints. A design optimization of electric vehicles with in-wheel motors is formulated as a two-level hierarchical scheme where the top level has a model representing the electric vehicle and the bottom level contains models of battery and suspension. The vehicle model includes an electric motor model and a power electronics model. Pareto-optimal solutions are derived holistically. The effectiveness of the proposed method for optimizing the complex systems is compared against the conventional all-in-one optimization approach. [ABSTRACT FROM AUTHOR]

• Framework for modeling well to wheels real-world plug-in vehicles CO 2 using big data. • Simulations conducted using open-source code and public data sets. • Considered sensitivity to fuel mix in electric grid as well as charging behavior. • Examined several electric range designs for three plug-in powertrain architectures. • Too long electric range can be detrimental to equivalent CO 2 emissions. This paper presents a modeling and simulation study of the greenhouse gas (GHG) emissions of plug-in vehicles subject to real-world driving conditions. The simulations utilize open-source software tools for estimation of second-by-second energy and/or fuel consumption in more than 65 thousand real-world recorded trips in the California household travel survey (CHTS) data set. Statistical distributions of the equivalent well-to-wheels (W2W) GHG emissions corresponding to CHTS trips are generated for three baseline public-domain plug-in vehicle models of: (i) parallel-drive plug-in hybrid, (ii) serial-drive plug-in hybrid, and (iii) pure electric vehicle. In addition to the baseline models, several derived vehicle models are created by upsizing of the battery and powertrain via an iterative procedure to attain the new all-electric range (AER). The emissions model also considers days when a pure electric vehicle cannot meet the travel demand by assuming that the driving on those days is done on a conventional vehicle. For each of the considered powertrain architectures, optimizing the AER for minimum GHG emissions becomes a trade-off between reducing the gasoline driven miles and maintaining reasonably sized (lightweight) powertrain components. As such, the equivalent GHG emissions of a vehicle are shown to have diminishing returns in terms of AER, and the optimum AER depends on both the carbon content in the electric grid, as well as the charging behavior. Within considered modeling assumptions and several vehicle usage conditions, the optimum AER is estimated to be between 30 and 60 miles for PHEVs, and between 120 and 200 miles for BEVs. [ABSTRACT FROM AUTHOR]

• Propose a multi-period two-stage stochastic linear programming model. • A hybrid decomposition algorithm is developed to solve the mathematical model. • Impact of power demand uncertainty and flow rate on system performance are examined. • Several managerial insights are made based on our computational studies. This study proposes a novel formulation for designing and managing electric vehicle charging stations, considering both long-term planning decisions and short-term operational decisions over a pre-specified planning horizon and under stochastic power demand. To solve this challenging problem, we propose a hybrid algorithm that combines Sample Average Approximation with an enhanced Progressive Hedging Algorithm. A case study, based on the road network around Washington, D.C., is presented to visualize and validate the modeling results. Results indicate that the electric vehicle power demand are satisfied primarily via the grid and Vehicle-to-Grid when energy cost is low and solar power is unavailable. [ABSTRACT FROM AUTHOR]

Energy flow analysis is one of the most effective methods for assessing and improving vehicle efficiency. It can provide the most effective support for the development and optimization of a hybrid electric vehicle (HEV) control strategy for improving the performance and energy saving of HEVs. In this study, an energy flow experiment of a series–parallel HEV is conducted in a climate chamber under the Worldwide Light-duty Test Cycle to address high fuel consumption. Then, GT-Suite and MATLAB/Simulink co-simulation models for the tested vehicle are built and calibrated. The problems of the original HEV control strategy are addressed using the analysis of vehicle energy flow distributions, operating conditions, and energy efficiency of key parts. Then, two optimized vehicle control strategies (termed Opt1 and Opt2) are employed to test vehicle fuel savings using a calibrated co-simulation model. The specific optimized vehicle control strategies primarily include canceling the series and parallel charging in the braking state, adding the engine direct-drive mode, and reducing the proportion of the parallel mode. The simulated results show that the 100 km fuel consumptions of Opt1 and Opt2 are reduced by 7.1% and 7.5%, respectively, compared with the original vehicle. The proportion of energy that drives the vehicle directly through the engines of Opt1 and Opt2 is increased from 45.7% to 70.2% and 61.6%, respectively. The total vehicle energy losses for Opt1 and Opt2 decrease from 29.0% to 22.5% and 22.9%, respectively. Although the engine of the original vehicle exhibits high thermal efficiency, the efficiency of the entire powertrain system is the lowest, resulting in the highest fuel consumption. [ABSTRACT FROM AUTHOR]

• Empirically analyzes revealed adoption behavior of electric vehicles (EVs). • Distinguishes between hybrid EV (HEV), plug-in HEV (PHEV), and battery EV (BEV). • The latent green travel pattern construct negatively affects HEV and PHEV adoption. • Census tracts with more public EV charging stations encourage only PHEV adoption. • PHEV/BEV adopters live in non-apartment houses close to level 2 charging stations. This paper presents a behavioral model of public, revealed preferences (RP) for various types of electric vehicles (EVs) while accounting for a latent (green) travel pattern construct and charging infrastructure characteristics. Specifically, a two-level nested logit (NL) model is estimated to explain households' fuel type choice among five alternatives and three nests: (1) battery electric vehicles (BEVs); (2) hybrid vehicles including hybrid electric vehicles (HEVs) and plug-in HEVs (PHEVs); and (3) conventional vehicles including gasoline and diesel vehicles. Further, a latent travel pattern construct which captures a week-long number of trips by non-vehicle travel modes as well as daily vehicle and tollway use is estimated in a structural equation setting and subsequently fed into the NL model. Using a recent RP dataset from the California Household Travel Survey, we identify market segments for alternative fuel types based on households' socio-economic characteristics, built environment factors concerning public plug-in EV (PEV) charging infrastructure characteristics, latent and observable travel behavior factors of a household vehicle's principal driver, and vehicle attributes. The results highlight that the number of public PEV charging stations is only significant for households choosing PHEVs and interestingly insignificant in the BEV utility. Furthermore, the sensitivity analysis of the findings reveals that PHEV users are elastic with respect to household vehicle ownership ratio and the latent green travel pattern construct, while BEV users are inelastic to any explanatory variable. [ABSTRACT FROM AUTHOR]

To investigate the stability of in-wheel motor electric vehicles (IWMEVs) under extreme conditions, a novel control strategy including active rear steering (ARS) mode and direct yaw moment control (DYC) mode is proposed in this paper, utilizing the adaptive dynamic neural network (ADNN) algorithm to make the most of the two control modes. Firstly, a three-degree of freedom nonlinear vehicle model as well as some subsystems is established. Then, a two-layer stability control strategy is put forward, where the upper-layer calculates the desired rear steering angle as well as the differential torque of the rear wheels and the lower-layer executes commands and returns relevant signals. Besides, a stability controller based on ADNN algorithm is designed in the upper-layer so as to take advantage of the two modes under extreme conditions. Next, the impacts of initial values of the connection weights on the ability of ADNN algorithm to train and learn are revealed. Consequently, the optimal initial values can be ascertained before the following simulations. Finally, the closed loop simulations of ARS and DYC are carried out under some extreme conditions such as high velocity and low adhesion coefficient roads, and the results indicate that compared with DYC's difficulty in playing its role, ARS mode can significantly improve the stability of IWMEVs even under extreme conditions. [ABSTRACT FROM AUTHOR]

• Engine and vehicle level simulations are performed to optimize dual-loop EGR system. • Based on original EGR conditions, extended EGR rates are conducted under engine level. • Dual-loop EGR split strategy is conducted for vehicle driving mode under WLTP. • Multi-objective Pareto optimization is performed to minimize NO X and BSFC. • Optimizing gear strategy, fuel economy and deNO X rate are improved to 1.5% and 5%. Because of the increased need for virtual analysis during the vehicle manufacturing processes, more stringent optimization methods are required for the simulation field. Owing to the use of big data from engine testing, 1D analysis can provide more powerful approaches in the conceptual design phase for car makers. In this study, system-level optimization of dual-loop EGR was performed at both the engine and vehicle level, whereas our previous research had been performed at the engine level. Depending on the virtually developed engine and vehicle models and control scheme from our previous works, improved driving capabilities could be observed under light-duty diesel vehicle systems under the world-harmonized light-vehicles test procedure (WLTP). The numerical model was extended through two steps. The first step includes model conversion from the base engine model with an HP EGR system to a virtual engine model with a dual-loop EGR system. The second step represents mode extension from the virtual engine model with a dual-loop EGR system to a vehicle model with a dual-loop EGR system. Optimizing the dominant parameters and using design of experiment (DoE)-based multi-objective Pareto optimization methods in each step, fuel economy could be improved by approximately, 1.5% and the deNO X rate was approximately 5% that of the conventional NEDC. It is implied that the dual-loop EGR system and gear strategy could improve vehicle performance under difficult driving conditions. [ABSTRACT FROM AUTHOR]

This paper evaluates the voltage security of the distribution networks in the presence of electric vehicles in the optimization framework. Accordingly, the main objective functions of this optimization problem include maximization of voltage security margin and minimization of operational cost. Also, it is supposed that the electric vehicles are equipped with bidirectional chargers to control active and reactive power in smart distribution networks, simultaneously. The objective functions are subject to the constraints of power flow equations, system operating limits and electric vehicle constraints. The proposed model is implemented on the 33-bus distribution network to evaluate the performance of the proposed optimization scheme for the management of the smart distribution networks in the presence of electric vehicles. The results show that the operational cost and network voltage security margin are reduced in the case of the higher electric vehicle penetration rate when electric vehicles are used for charging active power and reactive power control capability, with respect to the case that does not include electric vehicles. [ABSTRACT FROM AUTHOR]

Although the need for more environmentally friendly vehicles was recognized some decades ago, this new market has not yet established itself. Consumer behavior needs to be studied to ascertain when people will decide to purchase hybrid or electric vehicles rather than conventional ones. An in-depth review of the state-of-the-art has identified existing deficiencies and these are addressed in this paper, proposing a new approach that is applied to the case of Santander in Spain. Emphasis is placed on the role of citizens in researching the local market and their requirements with respect to such vehicles; our model assumes variability in user preferences, an utmost requirement as concluded from the literature review. Results suggest that the highest demand for cleaner vehicles would be achieved in two ways: firstly, by penalizing conventional vehicles in terms of costs/km; secondly, by providing incentives directed at lowering the purchasing price of hybrid and electric vehicles. Finally, as demand becomes more elastic, the preferred strategy should initially focus on hybrid vehicles. [ABSTRACT FROM AUTHOR]

• Expert demonstration data of hybrid electric vehicle energy management are represented by domain adaptive meta-learning. • By embedding expert demonstration model, an apprenticeship-reinforcement learning framework is established to improve fuel economy. • It is verified under three hybrid vehicle models with different powertrain architectures and several driving cycles. • Compared to traditional reinforcement learning, the apprenticeship-reinforcement learning can effectively improve the fuel emergency and CO 2 emissions. Deep reinforcement learning (DRL) is a potential solution to develop efficient energy management strategies (EMS) for hybrid electric vehicles (HEV) that can adapt to the changing topology of electrified powertrains and the uncertainty of various driving scenarios. However, traditional DRL has many disadvantages, such as low efficiency and poor stability. This study proposes an apprenticeship-reinforcement learning (A-RL) framework based on expert demonstration (ED) model embedding to improve DRL. First, the demonstration data, calculated by dynamic programming (DP), were collected, and domain adaptive meta-learning (DAML) was used to train the ED model with the adaptive capability of working conditions. Then combined apprenticeship learning (AL) with DRL, and the ED model was used to guide the DRL to output action. The method was validated on three HEV models, and the results show that the training convergence rate increases significantly under the framework. The average increase that the apprenticeship-deep deterministic policy gradient (A-DDPG) based method applied to three HEVs achieved was 34.9 %. Apprenticeship-twin delayed twin delayed deep deterministic policy gradient (A-TD3) achieved 23 % acceleration in the power-split HEV. Because A-DDPG's EMS is more forward-looking and can mimic ED to some extent, the frequency of engine operation in the high-efficiency range has increased. Therefore, A-DDPG can improve the fuel economy of the series hybrid electric bus (HEB) by 0.2–2.7 %, and improvements averaged to about 9.6 % in the series–parallel HEV while maintaining the final SOC. This study aims to improve the sampling efficiency and optimal performance of EMS-based DRL and provide a basis for the design and development of vehicle energy saving and emission reduction. [ABSTRACT FROM AUTHOR]

—This paper proposes a real-time energy management strategy (EMS) for hybrid electric vehicles by incorporating reinforcement learning (RL) in a model predictive control (MPC) framework, which avoids the inherent drawbacks of RL—the excessive learning time and lack of adaptability—and remarkably enhances the real-time performance of MPC. First, the MPC framework for the energy management problem is formulated. In that, a novel long short-term memory (LSTM) neural network is utilized to construct the velocity predictor for a more accurate prediction, and its prediction capability is verified by a comparative analysis. Then, the HEV prediction model and the velocity predictor are regarded as the RL model with which the RL agent can interact. On this basis, the optimal control sequence in the prediction horizon can be learned through model-based RL, but only the first element is actually executed, and the RL process begins anew after the prediction horizon moves forward. In the simulation, the algorithm's convergence is analyzed and the influence of the prediction horizon length is evaluated. Then, the proposed EMS is compared with DP, conventional MPC, and RL method, the results of which demonstrate its performance and adaptability. As last, a hardware-in-the-loop test validates its actual applicability. • An energy management strategy is developed by incorporating RL in MPC framework. • The strategy avoids RL's inherent drawbacks and enhances MPC's real-time ability. • A long short-term memory neural network is utilized to predict the velocity. • Simulation and HIL test verify its fuel economy and real-time ability. [ABSTRACT FROM AUTHOR]

• A co-simulation platform for Hybrid Electric Vehicles is developed for virtual assessment of Energy Management, performance, CO 2 & pollutant emissions. • CO 2 & pollutant emissions models are based on convolutional neural network for accurate transient state prediction. • A continuous deep reinforcement learning based EMS of HEV is proposed. • DDQL is trained with random generated driving cycles based on Markov Chain approach from real recordings. • Cumulative d error for predicted pollutants emissions is below 8.5% while HEV simulation is running up to 10 times faster than real time in a standard laptop. The Energy Management Strategy (EMS) in an HEV is the key for improving fuel economy and simultaneously reducing pollutant emissions. This paper presents a methodology for developing hybrid models that enable EMS testing as well as the evaluation of fuel consumption, CO 2 and pollutant emissions (CO, NO x and THC). In this context, pollutant emissions are hard to quantify with static models such as the well-known map-based approach which is mainly due to the pronounced impact of transient effects. The novelty of this paper primarily comes from the characterization of pollutant emissions through Convolutional Neural Networks (CNN), providing high accuracy for both, instantaneous and cumulative values. The input parameters are classical Internal Combustion Engine (ICE) measurements such as engine speed, air mass flow, torque and exhaust temperature. The proposed CNNs are reduced to a minimum for low complexity and fast computability. These models are developed with experimental data from chassis dyno testing of a conventional turbo-charged gasoline engine vehicle. The pollutant emission models are used in conjunction with physical models of the remaining powertrain allowing for real time simulations of the complete HEV vehicle. The Double Deep-Q learning algorithm is proposed for the EMS and compared to the Dynamic programming (DP) solution. The introduced methodology is developed in a co-simulation framework between MATLAB-Simulink and AMESIM. The resulting model runs between 8 and 10 times faster than real time in an off-the-shelf PC. This provides the capability for developing models suitable for HIL (hardware-in-the-loop) and SIL (software-in-the-loop) applications. The final error in predicted CO 2 remains below 2.5% while the final cumulative error for pollutants is below 8.5% in the case of CO and HC emissions. [ABSTRACT FROM AUTHOR]

Energy management of Parallel Hybrid Electric Vehicles (PHEVs) involves computation of energy efficient torque-speed set-points for the engine and the motor. Application of Nonlinear Model Predictive Control (NMPC) for this problem has been reported in the recent literature. In this work, a fast iterative implementation of the NMPC, known as Model Predictive Static Programming (MPSP) is deployed for the first time for energy management of PHEVs, with an appropriate multi-objective cost function and the applicable equality and inequality constraints. Additionally, a systematic approach based on the Theory of Dominance is used to tune parameters of the controller efficiently. Performance is evaluated with a validated PHEV model on the ADVISOR simulation environment, in terms of various performance metrics on standard drive cycles. The results show that the performance of the MPSP-based technique is close to the ideal performance achieved with Dynamic Programming (DP) and improved over a standard implementation using the Linear Time-Varying MPC (LTV-MPC) and Sequential Quadratic Programming MPC (SQP-MPC) algorithm. Moreover, its execution time on an industry standard micro-controller board is seen to be significantly less than that of LTV-MPC and SQP-MPC. This algorithm therefore appears to be one of the best candidates for on-board vehicle implementation of an optimal energy management strategy for a PHEV. • A new improved energy management strategy for a Parallel HEV based on the MPSP framework. • Numerical implementation showing improved performance over a contemporary MPC algorithm. • Low execution time and feasibility of an on-board implementation. • A new effective approach for tuning the algorithm. [ABSTRACT FROM AUTHOR]

This paper presents the performance analysis of a latest-generation hybrid vehicle (Toyota Yaris 2020) with a testing campaign in real road conditions and a comparison with the previous model (Toyota Yaris 2017). The study was conducted by applying the Real Drive Truth Test protocol, developed by the research group, validated and spread to other full hybrid vehicles: Toyota Prius IV (2016) and Toyota Yaris 2017 (2017). In the case of the 2020 tests, the co-presence on board—deemed unsafe in the usual ways given the ongoing pandemic—was achieved through precise and sophisticated remote control. An on-board diagnostic computer, video transmission and recording equipment guarantee the virtual co-presence of a technical control room and a driver. Thus, several engineers can follow and monitor each vehicle via a 4G modem (installed in each vehicle), analysing data, route and driver behaviour in real-time, and therefore even in the presence of a single occupant in the car under test. The utmost attention has also been paid to adopting anti-COVID behaviours and safety standards: limited personal interactions, reduced co-presence in shared rooms (especially in the control room), vehicle sanitising between different drivers, computers and technicians and video technicians working once at a time. The comparison between the two subsequent vehicle models shows a significant improvement in the performance of the new generation Yaris, both in terms of operation in ZEV (zero-emission vehicle) mode (+15.3%) and in terms of consumption (−35.1%) and overall efficiency of the hybrid powertrain (+8.2%). [ABSTRACT FROM AUTHOR]

With the development of technology, speed prediction has become an important part of intelligent vehicle control strategies. However, the time-varying and nonlinear nature of vehicle speed increases the complexity and difficulty of prediction. Therefore, a CNN-based neural network architecture with two channel input (DICNN) is proposed in this paper. With two inputs and four channels, DICNN can predict the speed changes in the next 5 s by extracting the temporal information of 10 vehicle signals and the driver's intention. The prediction performances of DICNN are firstly examined. The best RMSE, MAE, ME and R2 are obtained compared with a Markov chain combined with Monte Carlo (MCMC) simulation, a support vector machine (SVM) and a single input CNN (SICNN). Secondly, equivalent fuel consumption minimization strategies (ECMS) combining different vehicle speed prediction methods are constructed. After verification by simulation, the equivalent fuel consumption of the simulation increases by only 4.89% compared with dynamic-programming-based energy management strategy and decreased by 5.40% compared with the speed prediction method with low accuracy. [ABSTRACT FROM AUTHOR]

Exergy, or availability, is a thermodynamic concept representing the useful work that can be extracted from a system evolving from a given state to a reference state. It is also a system metric, formulated from the first and the second law of thermodynamics, encompassing the interactions between subsystems and the resulting entropy generation. In this paper, an exergy-based analysis for ground vehicles is proposed. The study, a first to the authors' knowledge, defines a comprehensive vehicle and powertrain-level modeling framework to quantify exergy transfer and destruction phenomena for the vehicle's longitudinal dynamics and its energy storage and conversion devices (namely, electrochemical energy storage, electric motor, and ICE). To show the capabilities of the proposed model in quantifying, locating, and ranking the sources of exergy losses, two case studies based on an electric vehicle and a parallel hybrid electric vehicle are analyzed considering a real-world driving cycle. This modeling framework can serve as a tool for the future development of ground vehicles management strategies aimed at minimizing exergy losses rather than fuel consumption. • Development of an exergy-based modeling framework for ground vehicles. • All the energy storage/conversion devices are modeled relying on exergy principles. • Formal technique for location/quantification of vehicle powertrain irreversibility. [ABSTRACT FROM AUTHOR]

Regenerative braking can significantly improve the energy efficiency of hybrid and electric vehicles, and many studies have been carried out in order to improve and optimize the energy recovery of the braking energy. In the paper, the optimization of regenerative braking by means of braking force modulation is analysed, with specific application to the case of cars converted into Through-the-road (TTR) hybrid vehicles, and an optimal modulation strategy is also proposed. Car hybridization is an emerging topic since it may be a feasible, low-cost, intermediate step toward the green transition of the transport system with a potential positive impact in third-world countries. In this case, the presence of two in-wheel-motors installed on the rear axle and of the original mechanical braking system mounted on the vehicle can result in limited braking energy recovery in the absence of proper braking management strategies. A vehicle longitudinal model has been integrated with an algorithm of non-linear constrained optimization to maximize the energy recovery for various starting speed and stopping time, also considering the efficiency map and power limitations of the electric components. In the best conditions, the recovery can reach about 40% of the vehicle energy, selecting the best deceleration at each speed and proper modulation, and with a realistic estimate of the grip coefficient. [ABSTRACT FROM AUTHOR]