Hybrid Electric Vehicle System Research Articles

Cooperative control of connected hybrid electric vehicles and traffic signals at isolated intersections

The development of connected and automated vehicle technologies allows for cooperative control of vehicles and traffic signals at intersections. This study aims at exploring the cooperation between traffic signal control and eco-driving control for a connected hybrid electric vehicle (HEV) system. A two-level cooperative control method for integrating traffic signal control, vehicle speed control, and energy management is proposed with the objective of improving both traffic and fuel efficiency for HEVs at isolated intersections. In view of the energy management and recuperation system of HEV, the vehicle energy consumption characteristic is considered in the proposed method. More specifically, at the traffic level, a traffic signal control strategy is designed to minimise the total travel time and fuel consumption of all HEVs using dynamic programming, which explicitly considers the arrival time and recuperation information of vehicles. At the vehicle level, a hierarchical control architecture is applied to optimise the speed trajectories and powertrain of each HEV using model predictive control and adaptive equivalent consumption minimisation strategy. Simulation results show that compared with the fixed-time and cycle-based signal control strategies with eco-driving, the proposed method can significantly reduce the travel time and fuel consumption by up to 27 and 24%, respectively.

Optimal Scenario-based Operation and Scheduling of Residential Energy Hubs Including Plug-in Hybrid Electric Vehicle and Heat Storage System Considering the Uncertainties of Electricity Price and Renewable Distributed Generations

The concept of the residential energy hub (REH) including electrical and thermal energy storage system has been developed as a good scheme for optimizing the residential energy systems. Although the optimal operation of REHs has received a great deal of attention, there is a research gap to propose a stochastic-based optimization model, which simultaneously concerns the uncertainties of renewable energy resources and electricity prices. This article tries to fill such a research gap by introducing a probabilistic scenario-based model that concerns the renewable energy sources’ uncertainties as well as electricity price's uncertainties. The proposed scenario-based model for optimization of REH operation cost is validated by the comparison of test results and Monte Carlo simulation (MCS)-based ones. It has been concluded that less than 2% inaccuracy occurs due to the simplifying of the proposed model in comparison with simulation-based models like MCS. Also, the proposed model is compared with other available ones. The study of seasonal heat and power demand changes is one of the most crucial contributions of this paper. Furthermore, test results illustrated that the PHEV and heat storage system could act as appropriate solutions to decrease the operation costs of REH.

Power distribution strategy of a dual-engine system for heavy-duty hybrid electric vehicles using dynamic programming

In order to enhance the power and bring additional energy-saving possibilities of heavy-duty vehicles, a novel dual-engine system is utilized in a series hybrid electric vehicle. However, the minimum fuel consumption of this configuration is still unclear, and the effective online energy management strategy for this system is absent. The studied vehicle adopts the dual-engine configuration, which is powered by Li-ion batteries and two auxiliary power units. This paper presents a two-step framework to address the energy management problem. A map-based strategy is adopted to distribute power between engines. The dynamic programming (DP) algorithm is incorporated to distribute power between engines and batteries and find the minimum fuel consumption. Simulation results show that the DP algorithm can save 7.3% fuel compared to the charge depleting and charge sustaining strategy. The dual-engine system achieves a 1.9% lower fuel consumption compared with the conventional hybrid system with one engine. The operation points of engines and the power profiles of batteries derived from the DP are analyzed, and a rule-based strategy is designed correspondingly. The proposed rule-based algorithm can reduce 2.2%–6.0% fuel consumption and is not sensitive to the variations of battery size, parameters of the rule-based strategy, and driving cycles.

Cyber–Physical Security of Energy-Efficient Powertrain System in Hybrid Electric Vehicles Against Sophisticated Cyberattacks

In this article, an innovative approach to improving the cyber-physical security of energy-efficient powertrain system in a hybrid electric vehicle (HEV) against sophisticated cyberattacks is presented. To the best of our knowledge, cyberattacks, especially sophisticated and subtle cyberattacks, have not yet been studied in energy management systems (EMSs) for HEVs. First of all, we present a systemic assessment of long-term sophisticated cyberattacks that aim to deteriorate the battery lifetime and energy efficiency of HEVs. Specifically, three levels of attack taxonomy according to the skill level of the attackers are considered, which are sophisticated and can hardly be detected by the human driver. In addition to levels 1 and 2, cyberattacks that do not or partially require prior knowledge of the vehicle, and we explore two other types of level 3 damage-oriented controller attacks made by highly skilled attackers who have sufficient knowledge of the system. Such sophisticated attacks will potentially cause severe damages, such as decreasing battery capacity and energy by up to 50%. For a comprehensive vulnerability assessment, we propose innovative evaluation metrics to analyze the impact and stealthiness of sophisticated attacks. Finally, a preliminary probability-based detection method for sophisticated damage-oriented controller attacks is developed to improve the cyber-physical security of energy-efficient powertrain system in HEVs.

Organic Rankine Cycle Waste Heat Recovery for Passenger Hybrid Electric Vehicles

Electrification of road transport is a major step to solve the air quality problem and general environmental impact caused by the still widespread use of fossil fuels. At the same time, energy efficiency in the transport sector must be improved as a steppingstone towards a more sustainable future. Multiple waste heat recovery technologies are being investigated for low-temperature waste heat recovery. One of the technologies that is being considered for vehicle application is the Organic Rankine Cycle (ORC). In this paper, the potential of ORC is discussed in detail for hybrid vehicle application. The modelling and testing of multiple systems such as the hybrid vehicle, engine, and ORC waste heat recovery are performed using the computational approach in GT-SUITE software environment correlated against available engine data. It was found that the maximum cycle efficiency achieved from the ORC system was 5.4% with 2.02 kW of delivered power recovered from the waste heat available. This led to 1.0% and 1.2% of fuel economy improvement in the New European Driving Cycle (NEDC) and Worldwide Harmonised Light Vehicle Test Procedure (WLTP) driving cycle test, respectively. From the driving cycle analysis, Hybrid Electric Vehicles (HEV) and ORC are operative in a different part of the driving cycle. This is because the entire propulsion power is provided by the HEV system, resulting in less engine operation in some part of the cycle for the ORC system to function. Apart from that, a brief economic analysis of ORC Waste Heat Recovery (WHR) is also performed in this paper and a comparative analysis is carried out for different waste heat recovery technologies for hybrid vehicle application.

Heat spreading performance of SiC-based power module with bonded vapour chamber for electric powertrain integration

After evolving side by side over the past three decades, insulated gate bipolar transistors (IGBTs) and metal–oxide–semiconductor field-effect transistor (MOSFETs) become two main options in high power semiconductor market of Hybrid Electric Vehicle (HEV) or Battery Electric Vehicle (BEV) systems. Recent improvements on high power performance are focused on the reduction of power losses and the enhancement of system reliability. Power losses of Si-based semiconductor are dissipated in the form of heat, which must be transferred away from the switching junction effectively. In this paper, we evaluate the new vapour chamber wick design and its heat spreading performance on a SiC-based MOSFETs, the results could be used as guides in order to provide a solution to tackle with thermal imbalance incurred by switching current. The selected cases showed that Silicon carbide (SiC) based MOSFETs could operate at more uniform temperature gradient with minimum 1.4 °C temperature difference across the whole bonding interfaces, and hotspots can be constrained below 120 °C by means of phase changing cooling methods. In addition, the replacement of conventional baseplate by vapour chamber could further reduce overall thermal resistance from junctions to coolant fluids. The simulation results show that wicks design with sintering methods could perform better than groove and mesh design in such high power application. At last, under low coolant flow rate chanlleges at 0.5 l/min, an integrated vapour chamber with ellipse pin–fin 16 × 20 is preferred with achieved temperature at 123.3 °C.

Quantitative analysis of fuel‐saving potential for waste heat recovery system integrated with hybrid electric vehicle

Hybrid electric vehicles (HEVs) with low fuel consumption, low emissions, and long driving range are the ideal transition models between conventional fuel vehicles and pure electric vehicles. The growing demand for increased vehicle efficiency has motivated the introduction of waste heat recovery (WHR) technology in the automotive industry, with the organic Rankine cycle (ORC) as the most promising measure for recycling waste energy. Currently, only a few studies have been conducted to couple HEV and WHR systems. These studies have mainly focused on the hybrid powertrain control strategy, but lack quantitative methods to comprehensively analyze the fuel-saving potential due to the WHR system. In this study, an HEV-WHR integrated system that includes a mechanism-based dynamic model of ORC and a hybrid diesel-electric truck model is established. Further, a quantitative evaluation method that simultaneously considers the negative integrated effects (increased vehicle weight and increased exhaust back pressure) and the positive impact values of the engine, motor, and WHR system on the fuel-saving potential is proposed. Finally, the influence of two environmental factors (wind speed and ambient temperature) on the fuel-saving performance is analyzed. The results reveal that under the standard highway driving cycle (HWY), the negative integrated effects reduce the ideal fuel-saving potential of the HEV-WHR system from 6.10% to 5.42%. However, the optimized performances of the engine, motor, and WHR system improve the fuel-saving rate by 0.39%, 1.81%, and 3.22%, respectively. The results also indicate that the fuel-saving potential increases from 1.62% to 8.60% with increasing wind speed and decreases from 6.70% to 4.25% with increasing ambient temperature.

Smart Power Electronics–Based Solutions to Interface Solar-Photovoltaics (PV), Smart Grid, and Electrified Transportation: State-of-the-Art and Future Prospects

The need to reduce the use of fossil fuels and greenhouse gas (GHG) emissions produced by the transport sector has generated a clear increasing trend in transportation electrification and the future of energy and mobility. This paper reviews the current research trends and future work for power electronics-based solutions that support the integration of photovoltaic (PV) energy sources and smart grid with charging systems for electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEV). A compressive overview of isolated and non-isolated DC–DC converters and AC–DC converter topologies used to interface the PV-grid charging facilities is presented. Furthermore, this paper reviews the modes of operation of the system currently used. Finally, this paper explores the future roadmap of research for power electronics solutions related to photovoltaic (PV) systems, smart grid, and transportation electrification.

On the Hybridization of Microcars with Hybrid UltraCapacitors and Li-Ion Batteries Storage Systems

The objective proposed by the EU to drastically reduce vehicular CO2 emission for the years up to 2030 requires an increase of propulsion systems’ efficiency, and accordingly, the improvement their technology. Hybrid electric vehicles could have a chance of achieving this, by recovering energy during braking phases, running in pure electric mode and allowing the internal combustion engine to operate under better efficiency conditions, while maintaining traditionally expected vehicle performances (mileage, weight, available on-board volume, etc.). The energy storage systems for hybrid electric vehicles (HEVs) have different requirements than those designed for Battery Electric Vehicles (BEVs); high specific power is normally the most critical issue. Using Li-ion Batteries (LiBs) in the designing of on-board Energy Storage Systems (ESS) based only on power specifications gives an ESS with an energy capacity which is sufficient for vehicle requirements. The highest specific power LiBs are therefore chosen among those technologically available. All this leads to an ESS design that is strongly stressed over time, because current output is very high and very rapidly varies, during both traction and regeneration phases. The resulting efficiency of the ESS is correspondingly lowered, and LiBs lifetime can be relevantly affected. Such a problem can be overcome by adopting hybrid storage systems, coupling LiBs and UltraCapacitors (UCs); by properly dimensioning and controlling the ESS’ components, in fact, the current output of the batteries can be reduced and smoothed, using UCs during transients. In this paper, a simulation model, calibrated and validated on an engine testbed, has been used to evaluate the performances of a hybrid storage HEV microcar under different operative conditions (driving cycles, environment temperature and ESS State of Charge). Results show that the hybridization of the powertrain may reduce fuel consumption by up to 27%, while LiBs lifetime may be more than doubled.

Optimization and performance analysis of the power system in hybrid electric vehicles

Hybrid electric vehicles (HEV) are the most industrialized and marketable in electric vehicles. Referring to the basic structure and parameters adopted by PRIUS Hybrid of Toyota, this paper puts forward an optimization scheme of HEV. The parameters and type selection of power components are obtained according to the vehicle's dynamic formula. The optimized hybrid power system is built and simulated through the simulation platform AVL Cruise. Then the obtained economic and dynamic parameters are analyzed and compared with the original performance parameters, verifying that the optimized system has better performance.

Integrated Regenerative Braking System and Anti-Lock Braking System for Hybrid Electric Vehicles & Battery Electric Vehicles

<div class="section abstract"><div class="htmlview paragraph">This paper describes development of an integrated regenerative braking system and anti-lock brake system (ABS) control during an ABS event for hybrid and electric vehicles with drivelines containing a single electric motor connected to the axle shaft through an open differential. The control objectives are to recuperate the maximum amount of kinetic energy during an ABS event, and to provide no degraded anti-lock control behavior as seen in vehicles with regenerative braking disabled. The paper first presents a detailed control system analysis to reveal the inherent property of non-zero regenerative braking torque control during ABS event and explain the reason why regenerative braking torque can increase the wheel slip during ABS event with existing regenerative braking control strategies. Then, the regenerative brake control problem during ABS events is formulated with a unified control system architecture where the regenerative braking torque is coordinated with the friction braking torque of ABS system. An integrated closed loop based wheel slip control including both regenerative braking control loop and friction braking control loop during ABS event, referred to as RBS-ABS event control, is developed. The maximum regenerative braking is achieved and optimal vehicle braking performances and vehicle stability are maintained during ABS event. Finally, simulation tests are provided to illustrate RBS-ABS event control as an effective solution to satisfy desired wheel slip with the same level of stop distance in comparison with that of ABS control only while performing energy recuperation.</div></div>

TRANSITION PROCESSES IN ELECTROMECHANICAL TRANSMISSION WITH A RESILIENT-ELASTIC COUPLING

Driving systems for hybrid cars and electric vehicles equipped with electric motors have different structures and characteristics. In the vast majority of hybrids, depending on the driving mode, the torque on the wheels of the car can be generated separately by both the internal combustion engine and the electric motor, or by working together. Based on the research results it is established that at the moment of starting the electric motor, the torque in the transmission sections steeply increases to 17 N•m, and for about 1 s decreases to the value of 7 N•m. In the period from 4 to 5.5 s, the torque increases to 14 N•m, which is explained by the overcoming of the inertial load during acceleration of the driven weight, and rapidly decreases to the value of 4 N•m, which corresponds to the consolidated moment of resistance to movement. The electromagnetic moment of electric motor thus also increases steeply in the initial stage of starting the motor up to 66 N•m and after 1 s decreases to the value of 15 N•m. After 5.5 s there is an increase in the moment to the value of 66 N•m and after 5.8 s it stabilizes and ranges from -6 to 22 N•m. In turn, the calculations for an electromechanical transmission equipped with a resilient-elastic coupling showed that the maximum torque in its sections Т2 during the start-up period decreased to 9 N•m, and the acceleration time to a steady turning velocity of the driven weight slightly increased to 6.8 s. The torque that occurs in the transmission sections during acceleration to a steady velocity does not exceed 13 N•m. The torque in the resilient-elastic coupling sections during the start-up period does not exceed 10 N•m, and its value, upon reaching the steady motion of the driven weight, is slightly less than 5 N•m. Peak torque in the resilient-elastic coupling sections Т1 reaches 22 N•m, while in the transmission Т2 it is 13 N•m, which confirms the efficiency of resilient-elastic coupling operation. Thus, the use of resilient-elastic coupling in an electromechanical transmission can reduce the amplitude of the torque in the drive sections during the start-up period by about 1.9 times, as compared to the amplitude of the torque without resilient-elastic coupling, and reduce the peak torque of the transmission sections by 1.7 times. Keywords: asynchronous electric motor, dynamic model, mathematical model, simulation model, torque.

Design and analysis of driving motor system for hybrid electric vehicle

In order to improve the reliability and stability of hybrid electric vehicle driving motor system, according to the performance parameters of the hybrid electric vehicle, the driving motor system is designed and analyzed for the hybrid electric vehicle. Based on the performance parameters of the hybrid electric vehicle, the power parameters of the permanent magnet synchronous motor (PMSM) are calculated and determined, then the parameters of the stator core, the permanent magnet and the rotor core are designed and calculated, as well as other main characteristic parameters of the driving motor system are calculated. The model of a PMSM is established and simulated by ANSOFT Maxwell according to the obtained motor parameters, and then the steady state and transient state of the driving motor are simulated in different working points, and the electromagnetic and performance curves are combined to determine the overall performance requirements of the driving motor, which can be used to match the hybrid electric vehicle. The simulation results show that the designed PMSM can be used to match the hybrid electric vehicle and meet the performance requirements of the vehicle. The final simulation analysis results are in good agreement with the theoretical calculation results, which indicates that this method can be used to afford a theoretical basis to reduce the cogging torque and optimize the in-wheel motor of electric vehicle in the future.

WASTE HEAT RECOVERY FOR HYBRID ELECTRIC VEHICLES USING THERMOELECTRIC GENERATION SYSTEM

Thermal energy can be easily converted to electrical energy using the thermal electric generator (TEG). This paper is aimed to developing a model of TEG array for Hybrid Electric Vehicle (HEV) system. The proposed system is implemented experimentally by increasing the temperature difference between the two sides of TEG array. To achieve the high temperature difference between these sides, the hot side is putting on the exhaust manifold of car, and the other side is colded by using the air-condition system in the car. The voltage generated from TEG is used for achieving maximum energy to recharge a battery car. Simulation results are carried out using MATLAB/Simulink software package to investigate the system performance. The experimental results are presented to demonstrate the effectiveness of the proposed array of TEG for HEV system. Also, the obtained results prove that the designed array of TEG gives good performance for different operating conditions.

Driver-Identified Supervisory Control System of Hybrid Electric Vehicles Based on Spectrum-Guided Fuzzy Feature Extraction

This article introduces the concept of the driver-identified supervisory control system, which forms a novel architecture of adaptive energy management for hybrid electric vehicles (HEVs). As a man-machine system, the proposed system can accurately identify the human driver from natural operating signals and provides driver-identified globally optimal control policies as opposed to mere control actions. To help improve the identifiability and efficiency of this control system, the method of spectrum-guided fuzzy feature extraction (SFFE) is developed. First, the configuration of the HEV model and its control system are analyzed. Second, design procedures of the SFFE algorithm are set out to extract 15 groups of features from primitive operating signals. Third, long-term and short-term memory networks are developed as a driver recognizer and tested by the features. The driver identity maps to corresponding control policies optimized by dynamic programming. Finally, the comparative study includes involved extraction methods and their identification system performance as well as their application to HEV systems. The results demonstrate that with help of the SFFE, the driver recognizer improves identifiability by at least 10% compared to that obtained using other involved extraction methods. The improved HEV system is a significant advance over the 5.53% reduction on fuel consumption obtained by the fuzzy-logic-based system.

Development of a heat pipe–based battery thermal management system for hybrid electric vehicles

Enhanced battery pack cooling remains an open thermal management challenge in hybrid electric vehicle applications. A robust cooling system should maintain the battery pack core temperature within a prescribed operating range to improve system performance, durability, and reliability while minimizing power consumption. This paper proposes a smart battery thermal management system utilizing heat pipes as a thermal bus to efficiently remove heat. The system couples a standard air conditioning system with traditional ambient air ventilation. The two loops can run independently or in tandem to achieve the desired control. A nonlinear model predictive controller was developed to maintain the battery core temperature within a designated range using the compressor and fan speeds as the control inputs. A mathematical battery thermal model was developed to estimate the core and surface temperatures. The system performance and power requirements were evaluated for various driving cycles and ambient conditions. Numerical results showed that the proposed cooling system can regulate the battery core temperature within the desired temperature range (maximum tracking error of 2.1°C) while compensating for ambient temperature conditions using a suitable cooling strategy. The simulation results showed the ability to remove up to 1135 kJ of heat. The simulation also presents the power consumed by system components under varying modes and ambient conditions.

Development of a Power Management Algorithm for PV/Battery Powered Plug-In Dual Drive Hybrid Electric Vehicle (DDHEV)

In this paper, a Three-Port Dual Boost (TPDB) DC-DC converter operated Hybrid Electric Vehicle (HEV) based on dual motor drive system is presented to minimize Carbon Dioxide (CO2) emission and to increase the fuel efficiency. The Dual Drive Hybrid Electric Vehicle (DDHEV) system composed of Photovoltaic (PV) system/plug-in, dual drive system and power management algorithm improves the fuel efficiency of the DDHEV. The DDHEV system designed with the PV system needs efficient and simple DC-DC converter to export the power generated by the PV system to motor/battery. The simulation results of the HEV structured in the MATLAB/Simulink and the experimental prototype models are introduced to evaluate the proposed PV-DDHEV under various conditions. From the results, it is found that the dual motor drive system operated by the TPDB provides better stability and reduces the power consumption.

State of charge and state of power estimation for power battery in HEV based on optimized particle filtering

To improve the performance of the battery management system in hybrid electric vehicle (HEV), the core is to estimate the state of charge (SOC) and the state of power capability (SOP) of power battery quickly and accurately on-line. Firstly, in order to improve the SOC estimation accuracy and reduce the estimation error of battery, an improved particle filter algorithm based on particle swarm optimization (PSO) is proposed. Aiming at the uncertainty of system noise in traditional particle filter (PF) algorithm, the PSO algorithm is used to optimize the system noise of PF and to improve the estimation accuracy. Secondly, a method that regards the battery voltage, current and the optimized estimation of SOC as constraints to predict the actual maximum charge-discharge power of the battery is proposed. The simulation results show that the optimized SOC estimation and SOP prediction algorithm has higher accuracy and is applicable to the dynamic estimation of the actual driving cycles of hybrid electric vehicles.

Design of an Effective Double-Rotor Machine With Robust Mechanical Structure

Power split system is the key power transmission component in hybrid electric vehicles (HEVs). This paper presents a new consequent-pole inserted permanent magnet (PM) double-rotor machine., which is suitable as the power split system for HEV. The key is to insert the interior radical magnetized PM with the same polarity into the rotor's iron core., so the mechanical structure of the proposed machine is more robust. Also., the working principle is analyzed., and the structure is optimized. In addition., the performances of the conventional machine and proposed type are quantitatively compared. The result shows that proposed machine has more advantages to be applied for the power split system in HEVs.

Optimal Design of a Hybrid Energy Storage System in a Plug-In Hybrid Electric Vehicle for Battery Lifetime Improvement

This paper proposes a multi-dimensional size optimization framework and a hierarchical energy management strategy (HEMS) to optimize the component size and the power of a plug-in hybrid electric vehicle (PHEV) with the hybrid energy storage system (HESS). In order to evaluate the performance of size optimization and power optimization, a PHEV with a battery energy storage system (BESS) is used as a comparison reference, and the dynamic programming (DP) algorithm is set as a benchmark for comparison. The size optimization method explores the optimal configuration of the system, including the maximum power of the system, the maximum power and capacity of the battery, and the maximum power and capacity of the supercapacitor (SC). Compared with the BESS, the size-optimized HESS reduces the capacity of the system by 31.3% and improves the economy by 37.8%. The HEMS can simultaneously optimize vehicle fuel consumption and suppress battery aging. Its upper layer uses the DP algorithm to optimize fuel economy, and the lower layer apply the linear programming (LP) method to improve battery life. Based on the size optimization results and HEMS, compared with the benchmark, the battery aging rate has been reduced by 48.9%, and the vehicle economy has increased by 21.2%.