Parallel Hybrid Electric Vehicle Research Articles

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.

Co-optimization on Ecological Adaptive Cruise Control and Energy Management of Automated Hybrid Electric Vehicles

Electrified drive systems and eco-driving technologies play a crucial role in promoting energy conservation. Eco-driving for hybrid electric vehicles(HEVs) is an intricate problem involving intertwined speed planning and energy management. In this context, an ecological adaptive cruise control (eco-ACC) and powertrain energy management strategy considering Signal Phase Timing Message (SPaT) can enhance both performance and real-time implementation. Specifically, this study develops a novel co-optimization method based on Pontryagin's minimum principle (PMP) that combines car-following control rules with the SPaT for a parallel HEV. The methodology involves the following steps: firstly, the parallel HEV model is established; secondly, the safe following distance model is constructed and the car-following control rules are devised to ensure safe driving. Subsequently, the co-optimization method based on PMP is then presented to simultaneously optimize the eco-driving problem of an ego-vehicle by converting the inter-vehicle distance constraint of the lead-vehicle into the limitation of the speed of the ego-vehicle. Finally, simulations are conducted under different scenarios for both fused SPaT and non-fused SPaT strategy. The simulation results demonstrate a reduction in fuel consumption by 6.27% and 5.69% in two different scenarios, respectively, and a shorter driving time for the fused SPaT strategy compared to the non-fused SPaT strategy.

Optimised fuzzy control strategy applied to parallel hybrid electric vehicle

As the Hybrid Electric Vehicle (HEV) includes at least two energy sources, appropriate control of these energy components is necessary to maximise the efficiency of the HEV, which is the purpose of the HEV control strategy. This paper describes a Particle Swarm Optimization (PSO)-Fuzzy control approach applied to HEV with parallel configuration. This approach employs a fuzzy controller, wherein the parameters of the Membership Functions (MFs) are fine-tuned using the PSO algorithm. The primary goal is to reduce energy consumption with consideration of battery State of Charge (SoC) maintenance. The effectiveness of this strategy will be proven by simulations under a variety of driving cycles.

Assessing the potential and energy distribution calibration of ethanol-biodiesel based RCCI mode of compression ignition engine in a plugin parallel hybrid electric vehicle

The focus of this research is to develop the resilient powertrain to combat the enforcement of stringent emission regulations and elevate its feasibility to power with the sustainable fuels to support the fuel crisis. The current solution in the reliable and adaptability point of view is the low temperature combustion engine-based hybrid electric powertrain powered with sustainable fuel is the better choice. The present work proposes the concept of an ethanol-biodiesel fuel based RCCI plugin parallel hybrid electric vehicles (PHEVs). The significant examination of this work in the aspects of optimal torque structure and injector control parameter calibration. The calibration of the RCCI engine torque structure operation is performed based on the multi-objective constraints of variance of indicated mean effective pressure and the maximum rate of pressure rise for the combustion stability. While the equivalence ratio and combustion temperature with emphasis on limiting the amount of NOx and soot emissions. The maximum ethanol energy share proportion of 66.98% has been achieved. The RCCI engine is most frequently operating in the torque zone of 10 Nm and then 17.81 Nm while the BLDC motor is most frequently operated in the range of 55 Nm even at max torque of 60 Nm. When comparing to a standard diesel hybrid setup, the ethanol-biodiesel fuelled RCCI setup has a 18%–19% reduction in harmful NOx emissions, with an equivalent decrease of 22.7% in fuel economy. This is achieved by only having a 13.4% more CO emission while a minimal increase in soot emission throughout the overall course of the driving cycle. The result of this work shows evidence that implementing an ethanol-biodiesel fuelled RCCI engine in a parallel hybrid configuration achieves a significant reduction in NOx emissions while achieving the performance targets and managing the fuel and battery energy utilization over a driving cycle efficiently.

Adoptability assessment of HCDI and RCCI modes in plug-in parallel hybrid electric vehicles using sustainable fuels and model-based torque structure calibration strategies

In this research, a comparative analysis of plug-in hybrid electric vehicle (PHEV) engine was conducted with two advanced low temperature combustion (LTC) techniques viz. Homogeneous charge with direct injection (HCDI) and reactivity-controlled compression ignition (RCCI), utilizing alternative fuels diethyl ether (DEE) and ethanol. This was attempted to exhibit the promising potential of LTC based PHEV to achieve the highest efficiency and low emissions. The experimental results disclosed that DEE powered HCDI can achieve superior thermal efficiency with an impressive peak torque of 14 Nm. On the other hand, ethanol powered RCCI produced a peak torque of 17.5 Nm with remarkable emission reduction characteristics, especially CO2. It was vindicated that HCDI-PHEV outperformed the RCCI-PHEV in terms of fuel economy despite both HCDI and RCCI being lower than the conventional diesel PHEV. The NOx emissions from HCDI were almost negligible. About 20 % lower nitrogen oxide (NOx) emissions were recorded for RCCI respectively as compared to diesel (36 g/s). HCDI and RCCI showcased about 2 and 1.3 times higher soot emissions than diesel (7.12 g/s). Fuel economy was improved by 15 % and 43 % respectively under HCDI and RCCI modes over the conventional diesel mode of PHEV operation. When the PHEV was operated continuously for about 3.5 h, the battery's SOC dropped from 80 % to 45 % under diesel operation, whereas it only reached 50 % and 60 % under RCCI and HCDI mode operation. Low fuel consumption characteristics, acceptable torque production capabilities and ultralow NOx reduction potential established HCDI as the preferable choice for PHEV over RCCI.

Optimal Rule-Interposing Reinforcement Learning-Based Energy Management of Series—Parallel-Connected Hybrid Electric Vehicles

P2–P3 series–parallel hybrid electric vehicles exhibit complex configurations with multiple power sources and operational modes, presenting a difficulty in developing efficient energy management strategies. This paper takes a P2–P3 series–parallel hybrid power system-KunTye 2DHT system as the research object and proposes a deep reinforcement learning framework based on pre-optimized energy management to improve the energy consumption performance of the hybrid electric vehicles. Firstly, a control-oriented model is established based on its system configuration and characteristics. Then, the optimal distribution of the motor energy under different operating modes is pre-optimized, which aims to reduce the energy management task’s dimensionality by equating two motors as an equivalent motor. Subsequently, based on real-time traffic information under connected conditions, deep reinforcement learning is utilized to optimize the optimal operating modes of the hybrid system and the optimal distribution between the engine and equivalent motors. Combining the pre-optimized results, the optimal energy distribution between the engine and the two motors in the system is achieved. Finally, performance comparisons are made between the predictive control and the traditional Dynamic Programming and Adaptive Equivalent Consumption Minimization Strategy, revealing the proposed optimization algorithm’s promising potential in reducing fuel consumption.

Integrated Optimization of Component Parameters and Energy Management Strategies for A Series–Parallel Hybrid Electric Vehicle

Integrated Optimization of Component Parameters and Energy Management Strategies for A Series–Parallel Hybrid Electric Vehicle

Research on switching control based sliding mode coordination strategy of hybrid electric vehicle

The transient mode transition from pure electric driving to hybrid driving in a hybrid electric vehicle (HEV) involves multiple stages, each characterized by different models and control objectives, demanding diverse levels of controller robustness and convergence performance. Addressing the complexities of multi-stage transitions in a parallel HEV, the paper proposes a dynamic coordination strategy based on the switched control system concept. Distinct sliding mode controllers (SMC) are designed for each transition stage, tailored to the specific characteristics of powertrain operation. At the engine starting and speed synchronization stages, the global integral sliding mode control (GISMC) is integrated with a novel anti-saturation reaching law to ensure the system resides in the sliding mode, enhancing global robustness. During the engine speed regulation stage, the nonsingular terminal sliding mode control (NTSMC) facilitates rapid engine speed tracking for reduced mode transition time. By combining GISMC for global robustness and NTSMC for accelerated convergence, the strategy is designed to enhance mode transition quality at different stages. Simulation results demonstrate a 7.3% reduction in mode transition time and a 60.7% decrease in jerk compared to the conventional SMC strategy. In hardware-in-the-loop tests, the proposed control strategy proves effective in improving mode transition quality.

Robust engine slipping start control of hybrid electric vehicles with uncertainty in clutch slipping torque and change in driver demand torque

The engine slipping start (ESS) benefits parallel hybrid electric vehicles from stable ignition and emission reduction. However, inappropriate coordination between the traction motor torque and clutch slipping torque during the ESS will lead to poor smoothness of the vehicle and failed start of the engine. Uncertainty in clutch slipping torque and change in driver demand torque bring tough challenges with sluggish convergence and intensive vehicle jerk in practice. To deal with this problem, a novel two-layer model reference adaptive controller (MRAC) which contains two parallel reference models is proposed to improve robustness and convergence rate simultaneously. On one hand, uncertainties of clutch slipping torque are divided into a low-frequency part and a high-frequency part, and adaptive laws based on the output feedback are designed contrapuntally to enhance robustness. On the other hand, two parallel reference models are designed to accelerate the tracking error convergence rate without changing the reference profiles, which is generated according to the driver demand torque in real time. To test the robustness and convergence rate, the proposed two-layer MRAC is compared with the classical MRAC and proportional–integral controller under the driving scenario with uncertain clutch slipping torque and abrupt change in driver demand torque. The sensitivity with different adaptive gains and low-frequency and high-frequency uncertainties in clutch slipping torque are examined. Finally, hardware-in-the-loop experiments are performed to verify the effectiveness of the proposed two-layer MRAC.

Fuel consumption comparative optimization for parallel hybrid electric vehicle based on PSO and DIRECT algorithms

Fuel consumption is a serious environmental issue. Hybrid electric vehicles “HEVs” are highly energy efficient and reduce emissions. Parallel Hybrid Electric Vehicles “PHEVs” are currently the most popular architecture type in the HEV market. A PHEV manages energy between its components based on a proposed fuzzy logic control strategy. This strategy has been well researched and proven to be effective. PHEV control strategy has several simultaneous objectives. Reducing fuel consumption is the main goal of a PHEV while maintaining driving performance. Due to the complex nature of PHEVs, optimization algorithms must be used as the applied control strategies do not yield satisfactory global system efficiency. Several constraints were added as boundary conditions in the optimization process to allow optimized sizing of components not only to achieve goals, but also to meet specific vehicle performance requirements. In an acceleration test, the PHEV must reach a certain speed within a given time. In addition, in terms of reducing fuel consumption and emissions, the PHEV must have a zero energy balance, between the start and end of the cycle, at the battery level. PHEV optimization involves several decision variables, including key powertrain components and energy management strategy “EMS” system parameters. These variables greatly affect vehicle performance. A comparative study of optimization algorithms was conducted to determine the characteristics of PHEV to improve fuel economy. The particle swarm optimization “PSO” algorithm and the divided rectangular “DIRECT” algorithm are applied to the studied PHEV to solve optimization problems specific to fuel consumption. Conducted studies prove the effectiveness of the PSO algorithm compared to the DIRECT algorithm in terms of fuel economy improvement. This research has made it possible to determine the optimal size parameters for components that will make our PHEV more efficient and less polluting.

Research on Mode Switching and Energy Management of Hybrid Electric Vehicle

Aiming at the optimization of mode switching control and energy management strategy for parallel hybrid electric vehicles, a mode switching control method and A-ECMS steady-state energy management strategy is proposed. Firstly, in order to effectively suppress the impact caused by mode switching, and a fuzzy controller is designed for clutch engagement displacement. Then, on the basis of ensuring the smoothness of mode switching, A-ECMS control strategy based on SOC feedback is established. Finally, Isight and Cruise are used to build a co-simulation platform, and multi-island genetic algorithm is used to study the influence of initial value selection of equivalent factor on the controller. The results show that the proposed mode switching control and energy management control strategy, combined with A-ECMS strategy, can effectively improve the vehicle fuel economy on the basis of ensuring the mode switching smoothness.

Computationally Efficient Energy Management for a Parallel Hybrid Electric Vehicle Using Adaptive Dynamic Programming

Computationally Efficient Energy Management for a Parallel Hybrid Electric Vehicle Using Adaptive Dynamic Programming

Performance Comparison Between Differential Evolution and Bat Algorithm in P.I.D Tuning for Optimization of Speed Control on Parallel Hybrid Electric Vehicle

In the last decade, there have been many means of transportation that use fuel oil (Internal Combustion Engine (ICE)). This has a serious impact on the environment due to the resulting pollutant gas emissions. One solution is the use of hybrid electric vehicles (HEV) as a replacement for vehicles that use ICE. One of the performances that a HEV must have is to have a stable speed when driving. In this research, several methods used in the metaheuristic algorithm in the disturbance observer have the advantage of describing the inverse model of the plant without creating a mathematical model. Testing was carried out by comparing two methods of metaheuristic algorithms, namely Differential Evolution and Bat Algirthm. The simulation results show that the method used on this HEV is to maintain its speed, so according to the test results it shows that the Differential Evolution method is the best method for controlling speed on a Parallel Hybrid Electic Vehicle.

Perbandingan Performansi Antara Differential Evolution dan Bat Algorithm pada Tuning PID Untuk Optimasi Kontrol Kecepatan Paralell Hybrid Electric Vehicle

In the last decade, there have been many means of transportation that use fuel oil (Internal Combustion Engine (ICE)). This has a serious impact on the environment due to the emission of pollutant gases. One solution is the use of a hybrid electric vehicle (HEV) as a substitute for vehicles that use ICE. One of the performance that must be owned by the HEV is to have a stable speed when driving. In this study, several methods used in the metaheuristic algorithm in the disturbance observer have the advantage of describing the inverse model of the plant without making a mathematical model. The test is carried out by comparing the two methods of the metaheuristic algorithm, namely Differential Evolution and Bat Algirthm. The simulation results show that the method used in this HEV is to maintain its speed, so according to the test results it shows that the Differential Evolution method is the best method for controlling the speed of the Parallel Hybrid Electric Vehicle.

Implementation of Fuzzy Logic Control into an Equivalent Minimization Strategy for Adaptive Energy Management of a Parallel Hybrid Electric Vehicle

Implementation of Fuzzy Logic Control into an Equivalent Minimization Strategy for Adaptive Energy Management of a Parallel Hybrid Electric Vehicle

A Comparative Study on Optimal Engine-Start Performance of Parallel Hybrid Electric Vehicles with Various Starter Motors

A Comparative Study on Optimal Engine-Start Performance of Parallel Hybrid Electric Vehicles with Various Starter Motors

Determination of Energy Savings via Fuel Consumption Estimation with Machine Learning Methods and Rule-Based Control Methods Developed for Experimental Data of Hybrid Electric Vehicles

In this study, a parallel hybrid electric vehicle produced within the scope of our project titled “Development of Fuel Efficiency Enhancing and Innovative Technologies for Internal Combustion Engine Vehicles” has been modeled. Firstly, a new rule-based control method is proposed to minimize fuel consumption and carbon emission values in driving cycles in the experimental model of the parallel hybrid electric vehicle produced within the scope of this project. The proposed control method ensures that the internal combustion engine (ICE) operates at the optimum point. In addition, the electric motor (EM) is activated more frequently at low speeds, and the electric motor can also work as a generator. Then, a new dataset was also created on a traffic-free racetrack with the proposed control method for fuel consumption estimation of a parallel hybrid electric vehicle using ECE-15 (Urban Driving Cycle), EUDC (Extra Urban Driving Cycle), and NEDC (New European Driving Cycle) driving cycles. The data set is dependent on 11 different input variables, which complicates the system. Afterward, the fuel estimation process is made with seven different machine learning methods (ML), and these methods are compared using the obtained data set. To avoid overfitting machine learning, two different test data sets were created. The Random Forest algorithm is the most suitable technique in terms of training and testing the fuel consumption model using correlation coefficient (R2), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) simulation appropriateness for both test datasets. Moreover, the random forest algorithm achieved an impressive accuracy of 97% and 90% for both test datasets, outperforming the other algorithms. Furthermore, the proposed method consumes 4.72 L of fuel per 100 km, while the gasoline-powered vehicle consumes 7 L of fuel per 100 km. The results show that the proposed method emits 4.69 kg less CO2 emissions. The effectiveness of the Random Forest Algorithm has been verified by both simulation results and real-world data.

Non linear model predictive control strategy for the energy management of a P4 parallel hybrid electric vehicle

In this paper, the energy management strategies (EMS) as main fuel saving approaches are studied for a P4 parallel hybrid electric vehicle (HEV). The multiple power sources of the analysed HEV (one thermal engine and two electric motors) and the different vehicle driving conditions increase the complexity in designing an optimal EMS. To efficiently solve the fuel minimization problem, a non linear Model Predictive Control (NL-MPC) is proposed as energy optimization strategy of the examined HEV. First, a vehicle simulation model is developed in Matlab/Simulink environment. A NL-MPC-controller is designed, implemented into the adopted code and coupled to the vehicle model. The effectiveness of developed NL-MPC approach is evaluated in two different driving cycles, also including various initial battery State of Charge. A comparison with a well-recognized real-time EMS strategy, namely heuristic/rule based (RB) approach, is performed over WLTC and a Real Driving Cycle (RDC). The numerical outcomes demonstrate the capability of NL-MPC controller at significantly improving the fuel consumption with respect to the RB strategy (maximum advantage of 9% and 15% over WLTC and RDC), thus providing an excellent and robust method in the HEV powertrain control with satisfactory performance.

Improvised energy management control through neuro-fuzzy based adaptive ECMS approach for an optimal battery utilization in non-plugin parallel hybrid electric vehicle

This work proposed to integrate intelligent techniques in an adaptive equivalent consumption minimization strategy (AECMS) for a robust equivalence factor correction based on the demand torque and battery state of charge (SOC). The two intelligent approaches of fuzzy logic and genetic algorithmically optimized adaptive neuro-fuzzy inference system (ANFIS) have been employed. The entire performance is validated using the prepared three hybrid standard driving cycles and also for training the fuzzy inference system (FIS). The proposed ANFIS-ECMS delivers a narrow battery charging and discharging profile within the optimal battery utilization zone. Also, it delivers the higher fuel economy for the three driving cycles in the D1 4.20%, D2 1.175%, and D3 0.90% improvisation is occurred compared to fuzzy-PI AECMS. Even in the reliability assessment of ANFIS-AECMS, the performance has been tested on the self-developed real-world driving cycle. The fuel economy increases by 1.26% and 0.80% are compared to rule-based and fuzzy-PI AECMS. The entire results of this work evidence that the proposed strategy achieves significant improvement in battery and fuel energy utilization and also the reduction in emissions compared with rule-based, conventional fixed PI ECMS and fuzzy-PI based ECMS.

An Integrated Control Approach for Shifting Process of Single-Axis Parallel Hybrid Electric Vehicle with a Multi-Speed AMT Gearbox

When a single-axis parallel hybrid electric vehicle (HEV) equipped with a multi-speed AMT gearbox is in its shifting process, the superposition of dynamic characteristics of multiple power sources and the intervention and withdrawal of AMT transmissions can easily cause significant vehicle longitudinal jerk. To achieve rapid and smooth output changes during the shifting process, this paper proposes an integrated multi-stage robust shifting control method for a single-axis parallel hybrid electric vehicles with a multi-speed AMT gearbox. First, models of key driveline components are constructed, and the shifting process is divided into five stages to provide a clear description of the control problem. Subsequently, we reproduce an integrated multistage robust control method to achieve favorable switching performance and control robustness under external disturbances. We propose a data-driven model predictive control strategy based on additional constraints in the torque unloading and recovery phases. Simultaneously, we present a joint control algorithm that integrates the optimal control and disturbance suppression in the speed synchronization phase. In addition, we develop a sliding mode auto-disturbance rejection control algorithm to achieve accurate position tracking of the shift actuator in the pickup and engage phases. Finally, simulations and bench tests are carried out to verify the effectiveness of the robust control method under different driving conditions. The results demonstrate that the proposed control method can not only coordinate the torque across different power sources and clutch while minimizing vehicle longitudinal jerk, shift time, and friction work, but also provides apparent robustness to model uncertainties and external disturbance. Therefore, the proposed method may offer a theoretical reference for the actual vehicle controller during shifting.