Series Hybrid Electric Vehicle Research Articles

Managing fuel consumption and emissions for hybrid electric vehicles through optimization of engine operation

This paper presents a multi-objective optimization framework for improving internal combustion engine performance in hybrid electric vehicles, specifically targeting the minimization of fuel consumption and emissions (CO, NOx, HC, PM). The proposed method integrates normalized objective functions with weighted factors to develop a unified performance index, facilitating the simultaneous optimization of multiple conflicting objectives. Utilizing the NSGA-II algorithm, a diverse set of Pareto optimal points is generated, each representing different trade-offs between the objectives. The study?s results demonstrate significant improvements in engine performance through the application of the unified ICE operation map, showcasing a notable reduction in emissions with only a slight increase in fuel consumption. The methodology was validated via MATLsimulations on two case studies involving parallel and series hybrid electric vehicles, employing a custom synthesized drive cycle for energy management strategy evaluation. The unified map enabled real-time control and efficiency improvements by balancing different emission parameters, thus optimizing ICE operation across various conditions.

Optimization of Energy Management Strategy for Series Hybrid Electric Vehicle Equipped with Dual-Mode Combustion Engine Under NVH Constraints

Energy management strategies (EMSs) are a core technology in hybrid electric vehicles (HEVs) and have a significant impact on their fuel economy. Optimal solutions for EMSs in the literature usually focus on improving fuel efficiency by operating the engine within a high efficiency range, without considering the drivability, which is affected by noise–vibration–harshness (NVH) constraints at low vehicle speeds. In this paper, a dual-mode combustion engine was implemented in a plug-in series hybrid electric vehiclethat could operate efficiently either at low loads in homogeneous charge compression ignition (HCCI) mode or at high loads in spark ignition (SI) mode. An equivalent consumption minimization strategy (ECMS) combined with a dual-loop particle swarm optimization (PSO) algorithm was designed to solve the optimal control problem. A MATLAB/Simulink simulation was performed using a well-calibrated model of the target HEV to validate the proposed method, and the results showed that it can achieve a reduction in fuel consumption of around 1.3% to 9.9%, depending on the driving cycle. In addition, the operating power of the battery can be significantly reduced, which benefits the health of the battery. Furthermore, the proposed ECMS-PSO is computationally efficient, which guarantees fast offline optimization and enables real-time applications.

An optimal energy consumption calculation method of hybrid electric vehicles with frequency distribution characteristics of driving conditions

For a series of problems in the design and optimisation of series hybrid electric vehicles caused by the driving condition input, such as a variety of user conditions, which may hinder the energy-saving ability of such vehicles. Condition construction will inevitably lead to information loss, and traditional design methods require high computational power and encounter a time disaster during the real-world condition data explosion. This study proposes a condition representation method with driving condition frequency distribution characteristics (DCFDCs) and an optimal energy consumption calculation method, that is, instantaneous battery energy balance solution and global battery energy balance correction (IBGB–GBEB) with DCFDCs. The energy consumption calculation results show that the proposed optimal energy consumption method can guarantee the accuracy of the energy consumption calculation and reduce the calculation time cost. The difference in fuel consumption between the optimal energy consumption method and the dynamic planning (DP) algorithm is only 2.50%, but the difference in the calculation time between the two methods is 87.00%. Furthermore, the practical example of hybrid power system (HPS) design parameter optimisation shows that the energy consumption calculation results obtained by the TDCR–DP and DCFDCs–MIGA methods are consistent, and the relative error is only 1.81%. However, the calculation time of the TDCR–DP method is 472 h, whereas that of the DCFDC–MIGA method is only 2.17 h, which is a calculation time reduction of more than 99.54%. The results highlight the effectiveness and time advantage of the optimal energy consumption calculation method with DCFDCs. This study can provide theoretical guidance and technical support for solving system optimisation problems caused by the explosion of driving condition data.

Modeling and Simulation of Hybrid Electric Vehicles for Sustainable Transportation: Insights into Fuel Savings and Emissions Reduction

Most motor vehicles have historically utilized Internal Combustion Engines powered by a fossil fuel. Despite the technological advancements in fuel-efficient engines, further improvements are required to reduce the effect of fuel costs and global warming. This study models and simulates Hybrid Electric Vehicles (HEVs) to evaluate their potential for fuel cost savings and emissions reduction compared to traditional vehicles. Using MATLAB® version R2023a Update 5 (9.14.0.2337262) Simulink version 10.7 (R2023a) and the Advanced Vehicle Simulator (ADVISOR) version 2003-00-r0116, the research examines Series and Parallel HEV configurations. The simulation explores various battery configurations, engine capacities, and power unit models to analyze their impact on energy utilization. The data, collected from the simulations, show significant fuel savings and emissions reduction with HEVs. The Parallel HEV configuration consistently saves fuel with fewer battery modules, while the Series HEV configuration performs better but requires more modules to maintain the system’s State of Charge.

Real-time analytical solution to energy management for hybrid electric vehicles using intelligent driving cycle recognition

For series hybrid electric vehicles, due to the limitation of engine operating characteristics, speed regulation is usually performed using discrete operating points. This leads to the problem of mixed integer programming when solving energy management problem. In this paper, an analytical method with strong real-time performance and small computational load is proposed. First, an intelligent cycle recognition system is designed. It can cluster driving cycles offline based on the probability distribution of the required power and efficiently recognize unknown driving cycles online. Then the analytical expression of power distribution is derived, and the selection scheme of engine operating point is given. Finally, the required power information obtained from the clustering is substituted into the analytical expression. The specific values of the corresponding analytical solutions for each type are computed offline. When facing unknown conditions, this method can match the power distribution scheme quickly once the driving cycle is recognized. The results of the simulation and hardware-in-loop experiments demonstrate that this method can effectively control the SOC trajectory and allocate power more optimally. Compared to the benchmark method, the proposed strategy improves the fuel economy by 5.52 % and 7.49 % in two cases, and the computational efficiency is significantly enhanced.

Energy Analysis of Waste Heat Recovery Using Supercritical CO2 Brayton Cycle for Series Hybrid Electric Vehicles

Waste heat recovery from exhaust gas is one of the most convenient methods to save energy in internal combustion engine-driven vehicles. This paper aims to investigate a reduction in waste heat from the exhaust gas of an internal combustion engine of a serial Diesel–electric hybrid bus by recovering part of the heat and converting it into useful power with the help of a split-flow supercritical CO2 (sCO2) recompression Brayton cycle. It can recover 17.01 kW of the total 33.47 kW of waste heat contained in exhaust gas from a 151 kW internal combustion engine. The thermal efficiency of the cycle is 38.51%, and the net power of the cycle is 6.55 kW. The variation in the sCO2 temperature at the shutdown of the internal combustion engine is analyzed, and a slow drop followed by a sudden and then a slow drop is observed. After 80 s from stopping the engine, the temperature drops by (23–33)% depending on the tube thickness of the recovery heat exchanger. The performances (net power, thermal efficiency, and waste heat recovery efficiency) of the split-flow sCO2 recompression Brayton cycle are clearly superior to those of the steam Rankine cycle and the organic Rankine cycle (ORC) with cyclopentane as a working fluid.

Research on the Performance Characteristics of a Waste Heat Recovery Compound System for Series Hybrid Electric Vehicles

In this paper, a waste heat recovery compound system for series hybrid electric vehicles is established. The existing components of vehicle air conditioning are used in the organic Rankine cycle (ORC) to realize miniaturization. The waste heat recovery compound system is constructed using GT-SUITE, and the objective of the analysis is to increase the power output and engine thermal efficiency increase ratio (ETEIR). The effects of the expander speed, pump speed, working fluid mass flow rate, and working fluid type on the waste heat recovery compound system are analyzed. The simulation results show that the optimal schemes for the ORC system and compound system corresponding to the expander speed and pump speed are 1000 pm, 2500 rpm, 1200 rpm, and 2500 rpm, respectively. Compared with the ORC system, the maximum power output of the compound system with the same working fluid in three states (1500 rpm, 2500 rpm, and 3500 rpm) of the engine is increased by 21.67%, 24.05%, and 28.23%, respectively. Working fluid supplies of 0.4 kg/s, 0.4 kg/s, and 0.6 kg/s in the three engine states are also considered the best solutions. The working fluid R1234yf and R1234ze are the preferred choices for a waste heat recovery compound system, which have a high system power output and ETEIR and are environmentally friendly.

Time-delay-aware power coordinated control approach for series hybrid electric vehicles

Series hybrid electric vehicles (S-HEVs) have attracted extensive attention thanks to the simple structure and excellent efficiency, which power transfer rely on high-voltage microgrids consisting of engine-generator set, battery, motor, and other actuators. However, with possible communication time delays (CTDs), the interaction between the actuators and control unit in terms of state and control command is desynchronize, resulting in the unstable operation of S-HEVs, such as engine stalls and bus voltage jitter. This presents a great challenge to the power coordinated control approach (PCCA) design that controls different actuators. Motivated by the mentioned above, this paper presents a time-delay-aware PCCA to achieve the stable driving of S-HEVs. First, an integrated powertrain control-oriented model that incorporates communication is built. Second, an increment model predictive control method (MPC) is designed. And then by leveraging the Lyapunov-Razumikhin stability theorem, a stability condition is derived by defining a parameterization weight matrix and known upper bound of CTDs, reducing the complexity of parameter designing. Additionally, an adaptive mechanism is designed to extend application of the proposed approach in unknown CTDs condition. Finally, the hardware-in-the-loop experiment is performed, and the results demonstrate that the presented approach in the mean and root mean square of the engine speed error and DC-bus voltage error decrease 39.87%, 43.85%, 44.67%, and 79.94% than nominal MPC in one period CTDs condition, respectively. And for the larger CTDs condition, the proposed method also shows efficient robustness. These substantiate that the proposed control method effectively improves vehicle running stability when encountering CTDs.

OPTIMISATION OF HYBRID ELECTRIC VEHICLE ENERGY MANAGEMENT USING MACHINE LEARNING

The focus of the research is on the optimization of the hybrid electric vehicle energy management using machine learning. The objective of this research is to develop the algorithm by using Support Vector Machine (SVM) and to identify the optimal operation mode for SHEV based on the power demand use by using the algorithm. First of all, this research focus on the series hybrid electric vehicle and the vehicle used is an all-terrain vehicle (ATV). Suitable formula will be used to complete the modeling of the vehicle in Energetic Macroscopic Representation (EMR) form and the model constructed by using Matlab Simulink. Then, Support Vector Machine is used to optimize the energy management in the vehicle. The training data from New European Driving Cycle (NEDC) and Worldwide harmonized Light vehicles Test Cycles (WLTC) with 3 classes will put be inside the SVM to undergo training and then the optimal operation modes for each driving cycle will be obtained by using Linear SVM. Then, the obtained results which are the predicted operation mode using the driving cycles and are plotted in the graph. The pattern of the graph is analyzed and then the best predicted operation mode with the highest accuracy among all the driving cycles is chosen. The trained model of the driving cycle with the highest accuracy is used to predict the optimal operation mode for ATV so that to have higher efficiency in energy management by using the Classification Learner inside the Matlab.

High conductive polymer PANI link Bi2MoO6 and PBA to establish “tandem hybrid catalysis system” by coupling photocatalysis and PMS activation technology

In order to design an efficient advanced oxidation system to improve the inherent limitations in removing refractory organic pollutants, we couple photocatalysis and peroxymonosulfate (PMS) activation technology to establish a tandem hybrid catalysis system inspired by series hybrid electric vehicles. Among them, Bi2MoO6, as an electric power source, can be driven by fuel (light) to generate continuous power (electron) input into the prussian blue analogues (PBA) through the transmission shaft polyaniline (PANI), allowing it to respond quickly and continuously activate PMS. Therefore, it exhibits excellent catalytic performance to degrade DC increased by 55%, and the catalytic rate constant k increased by 7 times compared with the PMS alone. Continuous flow experiments are explored to reveal the feasibility of engineering applications. This study provides a new strategy of tandem hybrid catalysis system to drive future advanced water treatment technologies.

A short-term prediction-based efficient optimization power control strategy for heavy-duty hybrid electric vehicle

This study proposes a short-term prediction-based power control strategy using the modified iteration sequential clustering quadratic programming (MISCQP) algorithm for heavy-duty series hybrid electric vehicles (SHEVs). In this strategy, a power preconditioning method is established on the basis of demand power prediction which guarantees the stable power output under transient high-power condition. Through the prediction sequence, MISCQP algorithm is proposed to solve receding horizon problem and achieve real-time control by improving the iteration efficiency. For this purpose, the clustering algorithm is designed to skip the unnecessary short step in the iteration which is too few to obtain sufficient descent. The iteration points in various iteration domains are clustered and the corresponding cluster centers are obtained. Next, the aforementioned clustering results are introduced to improve the termination criterion. The updated criterion turns to skip the short step when the distance of cluster centers of various iteration domains varies within the set threshold. Finally, the performance of the proposed strategy is validated both in simulation and hardware-in-loop tests. The results reveal that the proposed strategy achieves 5.00 %, 5.86 %, 6.27 % less fuel consumption while maintaining stable power output under all the driving cycles. And the average iteration number of proposed strategy is decreased by 10.36 %, 8.47 %, 9.21 %, respectively.

A component sizing prediction study for a series hybrid electric vehicle based on artificial neural network

In the present study, the predictive tool based on an artificial neural network is developed by means of the experimental data of two series hybrid electric vehicles. The experiments have been conducted on different driving conditions, including highways, traffic, and combined driving conditions. Then, the artificial neural network is developed to predict an arbitrary series hybrid electric vehicle’s required power. The instantaneous required power is divided into dynamic and steady power to size the combustion engine, electric motor, and high voltage battery of the series hybrid electric vehicle. The effects of different ambient conditions (including ambient temperature and altitude), the inverter and high voltage battery efficiencies, and the coast-down coefficients on the components sizing of the series hybrid electric vehicle are then investigated in different driving conditions. The results revealed that the maximum instantaneous power of the electric motor is associated with rapid acceleration in low-speed conditions, and the suburban driving route determines the combustion engine’s maximum power. Notably, the Worldwide Harmonized Light-duty vehicles Test Cycle is the most comprehensive among the available driving cycles, and most of the components’ sizes are determined by this cycle except the combustion engine’s maximum power. It is also realized that the cycle-wise investigation can be summarized into the Isfahan-Tehran route and Worldwide harmonized Light-duty vehicles Test Cycle calculations.

WITHDRAWN: Sensitivity analysis of boundary conditions on a series hybrid electric vehicle components through the artificial neural network

WITHDRAWN: Sensitivity analysis of boundary conditions on a series hybrid electric vehicle components through the artificial neural network

A Batteryless Series Hybrid Electric Vehicle Control

Energy management/control in hybrid electric vehicles (HEVs) is of a prime for many electric vehicle manufacturers and researchers. In our previous work, a control based on the singular perturbation (SP) approach was designed and applied to a series HEV for managing/controlling its power. This power supply, an internal combustion engine (ICE) that was mechanically connected to an electric motor (EM), which is a permanent magnet synchronous motor (PMSM), through a flywheel. However, the energy losses, which were not taken into account in the system model and the control function design, caused a significant current control error. In this article, we propose improving the control system performance using an observer that estimates the energy losses in a closed loop system with the time delay. Stability of the closed loop system with the observer is analyzed using a SP method. The proposed control strategy that uses the observer of the energy losses is compared to the control approach of our previous work and its efficacy is demonstrated experimentally on a reduced power scale series HEV.

Modelling and fuzzy logic based control scheme for a series hybrid electric vehicle

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.

Model Predictive Control Based Energy Management Strategy of Series Hybrid Electric Vehicles Considering Driving Pattern Recognition

This paper proposes an energy management strategy for a series hybrid electric vehicle based on driving pattern recognition, driving condition prediction, and model predictive control to improve the fuel consumption while maintain the state of charge of the battery. To further improve the computational efficiency, the discretization and linearization of the model is conducted, and the MPC problem is transferred into a quadratic programming problem, which can be solved by the interior point method effectively. The simulation is carried out by using Matlab/Simulink platform, and the simulation results verify the feasibility of the condition prediction method and the performance of the proposed method. In addition, the predictive control strategy successfully improves the fuel economy of the hybrid vehicle compared with the rule-based method.

Optimal Energy Management of Series Hybrid Electric Vehicles With Engine Start–Stop System

This paper develops energy management (EM) control for series hybrid electric vehicles (HEVs) that include an engine start-stop system (SSS). The objective of the control is to optimally split the energy between the sources of the powertrain and achieve fuel consumption minimization. In contrast to existing works, a fuel penalty is used to characterize more realistically SSS engine restarts, to enable more realistic design and testing of control algorithms. The paper first derives two important analytic results: a) analytic EM optimal solutions of fundamental and commonly used series HEV frameworks, and b) proof of optimality of charge sustaining operation in series HEVs. It then proposes a novel heuristic control strategy, the hysteresis power threshold strategy (HPTS), by amalgamating simple and effective control rules extracted from the suite of derived analytic EM optimal solutions. The decision parameters of the control strategy are small in number and freely tunable. The overall control performance can be fully optimized for different HEV parameters and driving cycles by a systematic tuning process, while also targeting charge sustaining operation. The performance of HPTS is evaluated and benchmarked against existing methodologies, including dynamic programming (DP) and a recently proposed state-of-the-art heuristic strategy. The results show the effectiveness and robustness of the HPTS and also indicate its potential to be used as the benchmark strategy for high fidelity HEV models, where DP is no longer applicable due to computational complexity.

Efficiency enhancement strategy implementation in hybrid electric vehicles using sliding mode control

Introduction. Hybrid electric vehicles are offering the most economically viable choices in today's automotive industry, providing best solutions for a very high fuel economy and low rate of emissions. The rapid progress and development of this industry has prompted progress of human beings from primitive level to a very high industrial society where mobility used to be a fundamental need. However, the use of large number of automobiles is causing serious damage to our environment and human life. At present most of the vehicles are relying on burning of hydrocarbons in order to achieve power of propulsion to drive wheels. Therefore, there is a need to employ clean and efficient vehicles like hybrid electric vehicles. Unfortunately, earlier control strategies of series hybrid electric vehicle fail to include load disturbances during the vehicle operation and some of the variations of the nonlinear parameters (e.g. stator’s leakage inductance, resistance of winding etc.). The novelty of the proposed work is based on designing and implementing two robust sliding mode controllers (SMCs) on series hybrid electric vehicle to improve efficiency in terms of both speed and torque respectively. The basic idea is to let the engine operate only when necessary keeping in view the state of charge of battery. Purpose. In proposed scheme, both performance of engine and generator is being controlled, one sliding mode controllers is controlling engine speed and the other one is controlling generator torque, and results are then compared using 1-SMC and 2-SMC’s. Method. The series hybrid electric vehicle powertrain considered in this work consists of a battery bank and an engine-generator set which is referred to as the auxiliary power unit, traction motor, and power electronic circuits to drive the generator and traction motor. The general strategy is based on the operation of the engine in its optimal efficiency region by considering the battery state of charge. Results .Mathematical models of engine and generator were taken into consideration in order to design sliding mode controllers both for engine speed and generator torque control. Vehicle was being tested on standard cycle. Results proved that, instead of using only one controller for engine speed, much better results are achieved by simultaneously using two sliding mode controllers, one controlling engine speed and other controlling generator torque.

Integrated motion control and energy management of series hybrid electric vehicles: A multi-objective MPC approach

This paper considers the integrated motion control and energy management problems of the series hybrid electric vehicles (SHEV) with constraints. We propose a multi-objective model predictive control (MOMPC)-based energy management approach, which is embedded with the motion control to guarantee driving comfort. In addition, due to the slow response of the engine, it may cause excessive batter power when HEVs work in different conditions (e.g., uphill or sudden acceleration) with a certain request power; this implies the discharge current is too large. A battery current constraint is designed and incorporated into the MOMPC optimization problem and hence avoids the extra high charge-discharge current. This prevents potential safety hazards and extends the battery's life. Finally, the numerical experiments are performed to verify the proposed approach.

Real-Time Optimal Control for Eco-Driving and Powertrain Energy Management

This paper presents a real-time receding-horizon control solution for the optimal control problem on combined eco-driving and powertrain energy management for a series-hybrid electric vehicle. By defining the optimal control problem in the spatial domain, discretizing the dynamics and applying several relaxations to the constraints, the problem is formulated as a second-order cone problem. The online real-time solution is achieved through the reformulation into a receding horizon control problem. To reduce computational complexity, several move-blocking strategies are applied. Combining powertrain control of the hybrid powertrain with eco-driving, leads to a reduction of 33.7%, compared to only applying energy management.