Parallel Hybrid Electric Vehicle Research Articles

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

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

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.

Optimal rule extraction-based real-time energy management strategy for series-parallel hybrid electric vehicles

Energy management strategy plays an important role in improving the fuel economy of hybrid electric vehicles. In this paper, an energy management strategy optimized by dynamic programming for a series–parallel hybrid electric vehicle is proposed. The key components of the powertrain system are modeled, and the model accuracy is corrected with the experimental data. To construct the real-time energy management strategy, the dynamic programming algorithm is employed first to find the optimal control benchmark, and then the control sequence with optimal fuel economy is obtained to analyze and build the mapping relationship between control variables and driving conditions. To fulfill the requirement of real-world application, the influence of the engine warm-up process on fuel consumption is also investigated. Finally, according to the optimal control datasets, the optimal operating mode distribution of clutches and the optimal power of the engine and motor are extracted to generate the real-time strategy. Benefiting from the optimal control sequence of dynamic programming, this study explores optimal operating characteristics of powertrain components towards fuel saving and enables the strategy conversion from global optimal to real-time application, which provides opportunities to achieve approximate optimal fuel economy of hybrid electric vehicles in real-time.

Control strategy optimization of hybrid electric vehicle for fuel saving based on energy flow experiment and simulation

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.

Pulse-and-Glide Operations for Hybrid Electric Vehicles in the Car-Following Scenario

This paper is focused on the pulse-and-glide (PnG) control for a parallel hybrid electric vehicle (HEV) in car-following. PnG is an eco-driving technique that alternately turns on and off the engine to save fuel. For HEVs, the ride comfort concerns by PnG in conventional vehicles can be alleviated by oscillating the battery state of charge (SOC), instead of the vehicle speed. However, due to the ohmic losses during the battery charging and discharging process, the fuel saving potential is much reduced. In order to reach a balance between ride comfort and fuel economy, a control method is proposed to utilize both the vehicle body and the battery as the energy buffers simultaneously in the PnG operation. In particular, two minimum-time control problems, one for the pulsing phase and another for the gliding phase, are formulated and implemented as model predictive control (MPC). With the applications of a series of approximations and sparsity optimization techniques, these two control problems become quadratic programming (QP) problems after the application of the pseudo-spectral (PS) method. The online implementability is thus ensured. Numerical simulations using naturalistic driving data show on average 17.1% and 5.1% improvements of fuel economy for local and highway speeds, respectively. It is shown that the proposed method is able to improve the fuel economy while maintaining both ride comfort and SOC sustenance.

Comparative analysis of predictive models for SOC estimation in EV under different running conditions

Electric vehicles have been introduced to address environmental concerns related to excessive burning of fuels in the automobile industry. The key component solely responsible for its preferences over other modes is the battery. The Charging and discharging states under different running model of vehicle is directly related to the health of battery and thereby the degree of hybridization of the series-parallel hybrid electric vehicle (SP-HEV) considered in this research. In this study, the state of charge of a series parallel hybrid electric vehicle was continuously modeled under different driving conditions, and two models were used to predict the charging states of the battery. A swarm-based prediction model resulted in a prediction error of 0.9114, while a modified differential evolutionary method reduced the error to 0.6672, indicating its effectiveness in estimating battery charge conditions. The prediction of battery SOC will enhance the power efficiency of the vehicle along with its SOH.

Multi-objective real-time energy management for series–parallel hybrid electric vehicles considering battery life

The real-time control of energy management strategy (EMS) is becoming increasingly challenging as the complexity of the model and control strategy increases. To address this issue while ensuring the accuracy of the EMS, a multi-objective real-time EMS based on model predictive control (MPC) that considers battery life is proposed in this paper. Firstly, to balance the accuracy and efficiency of the prediction module, a kernel extreme learning machine based on the whale optimization algorithm is proposed as a short-term speed prediction model. Secondly, an adaptive state of charge (SOC) trajectory planning method is established to plan MPC reference trajectory. Next, to optimize fuel efficiency, electrical energy consumption, and battery aging in real-time, a multi-objective real-time MPC (MOR-MPC) algorithm is proposed. Finally, the effectiveness, real-time performance, and robustness of the proposed strategy are verified. Simulation results demonstrate that the total cost of the strategy is reduced by 6.15% compared to the equivalent consumption minimization strategy (ECMS), with 98.17% of dynamic programming (DP) performance achieved. Real-time performance is improved by 97.89% compared to DP-MPC. Hardware-in-the-loop (HIL) testing is also carried out to evaluate the proposed strategy.

Critical Performance Analysis of Four-Wheel Drive Hybrid Electric Vehicles Subjected to Dynamic Operating Conditions

Hybrid electric vehicle technology (HEVT) is emerging as a reliable alternative to reduce the constraints of battery-only driven pure electric vehicles (EVs). HVET utilizes an electric motor as well as an internal combustion engine for its operation. These components would work on battery power and fossil fuels, respectively, as a source of energy for vehicle mobility. The power is delivered either from battery or fuel or both sources based on user requirements, road conditions, etc. HEVT uses three major propelling systems, namely, front-wheel drive (FWD), rear-wheel drive (RWD), and four-wheel drive (4WD). In these propelling systems, the 4WD model provides torque to all four wheels at the same time. It uses all four wheels to propel thereby offering better driving capability, better traction, and a strong grip on the surface. The 4WD-based HEVs comprise four architectures, namely, series, parallel, series-parallel, and complex. The literature focuses primarily on any one type of architecture for analysis in the context of component optimization, fuel reduction, and energy management. However, a focus on dynamic analysis that gives a real performance insight was not conducted, which is the main motivation for this paper. The proposed work provides an extensive critical performance analysis of all four 4WD architectures subjected to various dynamic operating conditions (continuous, pulse, and step-up accelerations). Under these conditions, various performance parameters such as speed (of vehicle, engine, and motor), power (of engine and battery), battery electrical losses, charge patterns, and fuel consumption are measured and compared. Further, the 4WD architecture performance is validated with FWD and RWD architectures. From MATLAB/Simulink-based evaluation, 4WD HEV architectures have shown superior performance in most of the cases when compared to FWD type and RWD type HEVs. Moreover, 4WD parallel HEV architecture has shown superior performance compared to 4WD series, 4WD series-parallel, and 4WD complex architectures.

An apprenticeship-reinforcement learning scheme based on expert demonstrations for energy management strategy of hybrid electric vehicles

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.

An adaptive receding horizon-based flexible mode switching control strategy of parallel hybrid electric vehicles

Mode switching is crucial to efficient drive in variable driving conditions for hybrid electric vehicles (HEVs), thus many scholars are attracted to implement its high-quality control. The receding horizon control method is an attractive method to solve this problem. However, the choice of prediction horizon is arduous due to the contradiction between the optimization performance and the computational cost. Motivated by this issue, this paper proposes an adaptive receding horizon control (RHC) method, which contains optimization layer (OPL) and feedback control layer (FCL). First, an OPL adaptive receding horizon control method is proposed to obtain the optimal control command, in which a novel adaptive factor, considering the proportional between the maximum traction motor torque and powertrain demand torque, is proposed to adjust the prediction horizon length and control constraints. Then, regarding OPL output as the reference trajectory, a lower-RHC method is designed in FCL to track the reference trajectory. Finally, the proposed control method is validated in simulation and bench experiment. Compared with the existing method, the clutch trajectory is optimized by RHC with adaptive prediction horizon and constrains, and the anti-disturbance ability is enhanced by the lower-RHC. The simulation and bench experiment results indicate that the 0–40 km/h acceleration time and jerk are reduced using the proposed method, respectively.

Global Optimization-Based Energy Management Strategy for Series–Parallel Hybrid Electric Vehicles Using Multi-objective Optimization Algorithm

Global Optimization-Based Energy Management Strategy for Series–Parallel Hybrid Electric Vehicles Using Multi-objective Optimization Algorithm