Parallel Hybrid Vehicle Research Articles

Investigation With Rule-Based Controller of Energy Consumption of Parallel Hybrid Vehicle Model in Different States of Charge

Investigation With Rule-Based Controller of Energy Consumption of Parallel Hybrid Vehicle Model in Different States of Charge

Energy management strategy design and fuel consumption analysis for a parallel hydraulic hybrid vehicle

Energy management strategy design and fuel consumption analysis for a parallel hydraulic hybrid vehicle

Energy management strategy design and fuel consumption analysis for a parallel hydraulic hybrid vehicle

Energy management strategy design and fuel consumption analysis for a parallel hydraulic hybrid vehicle

Research on the complete vehicle control strategy of the composite accumulator hydraulic hybrid power system

The compound accumulator is an energy storage device consisting of a large accumulator and a small accumulator. Compared with the traditional single accumulator hydraulic hybrid vehicle, it has the characteristics and advantages of fast braking response for the small accumulator and more energy recovery for the large accumulator, combining braking characteristics and energy recovery rate. This paper proposes the control strategy for parallel hydraulic hybrid vehicle based on compound accumulator, which can determine the working timing for large and small accumulators according to the specific driving conditions, thereby improving the braking performance and fuel economy for the vehicle. Under NEDC conditions, this paper uses dynamic programming algorithm to find the optimal control sequence for torque distribution and energy management of the hydraulic hybrid vehicle with composite accumulator, obtains the control rules and logic thresholds of the logic threshold control strategy, and obtains the switching rule of the working timing of the composite accumulator. The simulation of specific cycle conditions is carried out to verify the rationality of vehicle control strategy and to explore the energy-saving mechanism of parallel hydraulic hybrid power system based on composite accumulator. The NEDC urban cycle test was carried out, and the comparison between the test and simulation results showed that the hydraulic hybrid operation mode, large and small accumulator pressure, and engine fuel consumption during the test were basically consistent with the simulation results and verified the control effect of the vehicle control strategy and the energy-saving mechanism of the parallel hydraulic hybrid system based on the composite accumulator.

Coordination Control Algorithm for Wheel Torque of Parallel Hybrid Vehicle

When the Parallel Hybrid Vehicle (PHV) wheel’s torque is coordinated and controlled, the issue of coordination control of wheel torque under mode switching has some limitations. Therefore, the experiment proposed a novel wheel torque coordination control technique for parallel hybrid vehicles. The feedforward and feedback joint control technique of the Wheel Drive Torque (WDT) control algorithm may be used to implement the wheel torque drive control. The technique for the wheel braking Torque Control (TC) is applied to obtain the wheel braking torque through the Fuzzy PID controller (F-PID). A Dynamic Coordination Controller (DCC) is created and the engine output torque is adjusted using the PID control technique of wheel angular velocity difference. The motor provides the compensation torque. The deviation of the intended angular velocity from the actual angular velocity is adjusted intime to realize the wheel's dynamic coordination while being controlled by a mode switch. The results demonstrates that the reaction time of the proposed algorithm can be reduced to 0.08s; The overall output TC precision is improved by 11.1%. The precision of the predicted velocity of vehicle speed tracking has risen by 8.0%. The vehicle dynamics during the transition procedure is improved by 4.4%, which enhances the ride comfort and the PHV’s power.

Control and Optimization of Pre-Transmission Parallel Hybrid Vehicle with Fuzzy Logic Method and Comparison with Conventional Rule Based Control Strategy

In this study, a model of a pre-transmission parallel hybrid vehicle was created in MATLAB/Simulink environment. A torque control strategy has been developed for the hybrid vehicle on the created model. The hybrid vehicle's torque control is provided by fuzzy logic and conventional rule-based control strategies. The control strategies of the hybrid vehicle have been developed based on four input parameters: accelerator and brake pedal position, battery state of charge and the operating mode of the electric motor. The regenerative braking system of the pre-transmission parallel hybrid vehicle has been provided with a fuzzy logic control strategy in both vehicle structures. In the fuzzy logic torque split controller, a control algorithm has been created in which the safety, performance and fuel consumption values of the vehicle meet at the optimum point. Under different driving cycle conditions, the effects of control strategies on engine operating points have been examined and average fuel consumption values have been obtained. In the control of the parallel hybrid vehicle with fuzzy logic method, it has been obtained that it provides 12.87%, 6.62%, 2.27% and 6.23% fuel savings under FTP-75, NEDC, EUDC and ECE-15 driving cycle conditions, respectively. It has been observed that the vehicle cannot provide sufficient performance in US06 driving cycle conditions with the use of conventional rule-based control strategy.

Investigation of the Effects of Transmission Layout on Powertrain Efficiency, Performance, and Exhaust Emissions in Parallel Hybrid Vehicles

Abstract The investigation studies about increasing powertrain efficiency and reducing exhaust emissions and fuel consumption have been focused on hybrid vehicles for a few decades. However, the investigations are related to improvement of an individual powertrain component or energy management strategy for hybrid vehicles. Therefore, this study investigates the effects of the layout of the three-speed transmission and various gear selection procedures on vehicle performance, component efficiency, and exhaust emissions for same vehicle parameters, gear ratios, and capacity of the components in a parallel hybrid powertrain. Three design alternatives have been modeled and powertrain simulations have been performed according to the three different drive cycles as New European Drive Cycle (NEDC), Federal Test Procedure (FTP-72), and Istanbul Drive Cycle (IDC) using matlab®—Simulink® environment. The main simulation results are given for vehicle performance (acceleration, grading capacity, and maximum speed) and component efficiencies (battery and electric motor), brake-specific fuel consumption, and exhaust emissions of the engine as CO2, CO, HC, and NOx during the operation on the three-drive cycle. The simulation results showed that better vehicle performance, fuel consumption, and exhaust emissions can be achieved separately by changing the layout of the transmission in parallel hybrid vehicles.

Research on the Intelligent Energy Governance of Parallel Hybrid Vehicle Based on Deep Learning

To realize the intelligent energy governance of hybrid vehicles, a Deep-Q-Network energy controller based on the construction of parallel hybrid vehicle model is proposed, which aiming at energy loss problem of parallel hybrid vehicles and combining deep learning with reinforcement learning, and it is simulated through the ADVISOR software platform and compared with the traditional fuzzy logic strategy. The experimental results indicate that the DQN-based control strategy proposed in this paper reduces both the energy consumption and exhaust emissions of parallel hybrid vehicles. Compared with the traditional fuzzy control strategy, fuel consumption is reduced by 0.43L while the fuel economy increases by 10.9%. and exhaust gas such as CO44, CO, NOxx the emission were reduced by 28.9%, 0.2%, and 7.4%, respectively. It shows the feasibility and effectiveness of the proposed methods.

Research on HEV energy management strategy based on improved deep reinforcement learning

A parallel hybrid vehicle was studied to establish the demand power and power system model of the whole vehicle and proposed an energy management strategy based on improved deep reinforcement learning (DRL). The DRB-TD3 algorithm was proposed to improve the sampling efficiency of the original algorithm by improving the twin delayed deep deterministic policy gradient (TD3) algorithm in DRL and introduced dual replay buffers. A rule-based constraint controller was designed and embedded into the algorithm structure to eliminate unreasonable torque allocation. The performance of the dynamic planning (DP)-based energy management strategy was used as a benchmark for simulation experiment under UDDS driving conditions. The experimental results show that the DRB-TD3 algorithm has the best convergence performance compared with the deep deterministic policy gradient (DDPG) algorithm and the conventional TD3 algorithm, with 61.2$ \% $ and 31.6$ \% $ improvement in convergence efficiency, respectively, the proposed energy management strategy reduces the average fuel consumption by 3.3$ \% $ and 2.3$ \% $ compared with the DDPG- and TD3-based energy management strategies, respectively, the fuel performance reaches 95.2$ \% $ of DP-based, which with the best fuel economy, and the battery SOC can be maintained at a better level, which helps to extend the battery life.

An Equivalent Consumption Minimization Strategy for a Parallel Plug-In Hybrid Electric Vehicle Based on an Environmental Perceiver.

An energy management strategy is a key technology used to exploit the energy-saving potential of a plug-in hybrid electric vehicle. This paper proposes the environmental perceiver-based equivalent consumption minimization strategy (EP-ECMS) for parallel plug-in hybrid vehicles. In this method, the traffic characteristic information obtained from the intelligent traffic system is used to guide the adjustment of the equivalence factor, improving the environmental adaptiveness of the equivalent consumption minimization strategy (ECMS). Two main works have been completed. First, a high-accuracy environmental perceiver was developed based on a graph convolutional network (GCN) and attention mechanism to complete the traffic state recognition of all graph regions based on historical information. Moreover, it provides the grade of the corresponding region where the vehicle is located (for the ECMS). Secondly, in the offline process, the search for the optimal equivalent factor is completed by using the Harris hawk optimization algorithm based on the representative working conditions under various grades. Based on the identified traffic grades in the online process, the optimized equivalence factor tables are checked for energy management control. The simulation results show that the improved EP-ECMS can achieve 7.25% energy consumption optimization compared with the traditional ECMS.

Closed-form solution to the dynamic programming for a heavy-duty parallel hybrid vehicle energy management

Dynamic programming (DP) is frequently used to obtain the optimal solution to the hybrid electric vehicle (HEV) energy management. The trade-off between the accuracy and the computational effort is the biggest problem for the DP method. The closed-form solution to the DP is proposed to solve this problem. Firstly, the affine linear model of the engine fuel rate is obtained based on engine test data. The piecewise linear approximation of the motor power demand is obtained considering the different energy flows in the charging and discharging stages of the battery. Then, the second-order Taylor expansion for the cost matrix at each time and state grid point is introduced to get the closed-form solution of the optimal torque split. The results show that this method can greatly reduce the computing burden by 93% while ensuring near-optimal fuel economy compared with the conventional DP method.

Complete vehicle energy management using adaptive primal–dual operator splitting

Complete vehicle energy management (CVEM) aims at minimizing the energy consumption of all subsystems inside a vehicle. When the subsystems consist of energy buffers with linear dynamics and/or power converters with quadratic power losses, the CVEM becomes a large-scale and ill-conditioned optimal control problem for which well-known solution approaches (e.g. Pontryagin’s maximum principle, dynamic programming, convex and derivative-free optimization methods) are not effective. In this paper, we design an efficient solution algorithm for this problem by leveraging the framework of operator splitting methods. To facilitate its implementation, we propose a spectral method that automatically tunes the step sizes at every iteration and, in practice, accelerates convergence. Finally, we consider a case study for a parallel-hybrid vehicle and we illustrate the numerical performance of our method by showing that we can efficiently solve a CVEM problem with very large horizons.

Parallel fuzzy relation matrix factorization towards algebraic formulation, universal approximation and interpretability of MIMO hierarchical fuzzy systems

Multi-input multi-output (MIMO) hierarchical fuzzy systems (HFSs) play a significant role in industrial process control and power systems. Using the max-min semi-tensor product of matrices, this paper proposes the algebraic formulation of MIMO HFSs, and discusses the corresponding interpretability. Firstly, the parallel fuzzy relation matrix (FRM) of MIMO fuzzy systems is proposed, based on which, an algebraic formulation of MIMO fuzzy systems is developed. Secondly, the factorization of parallel FRM is used to partition the MIMO fuzzy systems into fuzzy logic units (FLUs). Based on this operation, the algebraic formulations and construction algorithms of serial, parallel and hybrid MIMO HFSs are established. Thirdly, the algebraic formulations are used to study the universal approximation and interpretability of MIMO HFSs, and the optimal interpretability is obtained. Finally, the effectiveness of the obtained results is verified by the energy control systems in parallel hybrid vehicles.

An Energy Management Strategy of Power-Split Hybrid Electric Vehicles Using Reinforcement Learning

With the rapid development of science and technology, the automobile industry is also gradually expanding due to which energy security and ecological security are seriously threatened. This paper was aimed at studying the energy organization strategy of power-split hybrid electric vehicles based on a reinforcement learning algorithm. A power-split hybrid electric vehicle (HEV) combines the advantages of both series and parallel hybrid vehicle architectures by using a planetary gear set to split and combine the power generated by electric machines and a combustion engine. This improves the fuel economy to some extent. However, to increase the fuel economy to a greater extent. This study primarily introduces the hybrid electric vehicle’s structure and presents a reinforcement learning-based management of energy approach for hybrid electric vehicles. It constructs the vehicle power model of HEV and Markov probability transfer model, then designs the energy control strategy based on reinforcement learning, and finally compares it with the energy control strategy based on PID (Proportional-Integral-Derivative). Using MATLAB/Simulink, the cycle conditions of NEDC (New European Driving Cycle) and FTP-75 (Federal Test Procedure) are selected to carry out simulation experiments. The energy management technique suggested in this study, which is based on reinforcement learning, may efficiently enhance the usage rate of automotive gasoline. The replication results show that the fuel consumption per 100 km (kilometers) based on reinforcement learning management strategy is 4.6% and 2.7% lower than the PID management strategy under two working conditions. The economy of fuel of the hybrid electric vehicle is also effectively improved.

Adaptive slip engagement control of a wet clutch in vehicle powertrain based on transmitted torque estimation

In the slip engagement of a wet clutch, the solenoid current-to-actuator piston pressure model (CPM) and the piston pressure-to-clutch transmitted torque model (PTM) are utilized for the clutch torque control. However, the CPM is changed depending on the amount of hysteresis, which is influenced by the hydraulic fluid temperature, and the PTM is changed due to the variation of the clutch friction coefficient and the touch point. Thus, the CPM and PTM should be adapted using the measurement information of the solenoid current and piston pressure for precise control of the clutch torque. In this study, an adaptive control method of the clutch torque in the slip engagement of a wet clutch is proposed which. The purpose of this study is to propose a clutch torque control method that does not use a piston pressure sensor when controlling the clutch torque of a wet clutch, so the suggested method does not require measurement of the piston pressure. In the method, the adaptation of the target piston pressure-to-clutch torque model (TPTM) is performed instead of the adaptation of the CPM and PTM, and the clutch torque is controlled using the compensated TPTM and nominal CPM instead of the compensated CPM and PTM. The proposed control method is verified experimentally by applying it to the slip engagement control of an engine clutch equipped in a production parallel hybrid vehicle.

A Sequential Quadratic Programming Approach to Combined Energy and Emission Management of a Heavy-Duty Parallel-Hybrid Vehicle⋆

Combined Energy and Emission Management (CEEM) problems are a class of optimal control problems that aim to minimize operational costs of (hybrid electric) powertrains with after-treatment system subject to constraints on emissions imposed by legislation. In this paper, a parallel-hybrid heavy-duty vehicle with a Variable Turbine Geometry (VTG) and an Exhaust-Gas Recirculation (EGR) system is considered. The CEEM problem is solved using Sequential Quadratic Programming (SQP) for which the powertrain and after-treatment models are approximated as smooth functions. It will be shown that solving the CEEM problem using SQP is computationally much more efficient when compared to other techniques like dynamic programming. It will also be shown that most of the benefits from CEEM come from the hybrid powertrain and not from regulating the VTG and ERG mass flows. Furthermore, zero emission zones and local emission constraints can also be included without too much effort.

Novel strategies to reduce engine emissions and improve energy efficiency in hybrid vehicles

Hybrid electric vehicles (HEV) can provide a same power as gasoline and diesel-powered vehicles while also reducing fuel consumption by 40–50%. However, due to a complex system of HEV, the power control strategies are used to improve the work efficiency, fuel economy and to reduce exhaust emissions. In the present work, four parameters are studied with the main focus on the optimization of power system of a parallel hybrid vehicle. These parameters are the influence of fuzzy logic algorithm on hybrid electric vehicle power train that is acceleration performance, climbing performance, engine efficiency and exhaust gas emissions. First, the vehicle’s power train is optimized using the Advanced Vehicle Simulator (ADVISOR). Secondly, by ensuring acceleration and climbing performance, the vehicle manufacturing cost is reduced by reducing the weight of the vehicle. Thirdly, two driving road test conditions, Extra-Urban Driving Cycle (EUDC) and Urban Dynamometer Driving Schedule (UDDS) are used to enhance the fuel consumption efficiency of the vehicle. Using a fuzzy logic algorithm for random operations, the operating range of an internal combustion engine is controlled as much as possible in the most efficient range. Through the control of 121 different algorithms, the operating efficiency of the powertrain is optimized to reduce exhaust emissions and improve fuel efficiency. The results showed that the proposed strategy can reduce fuel consumption by 5%, CO by 50% and improved the operating efficiency of engine by 15%. Not only this control strategy optimizes the efficiency of powertrain, but also the efficiency of the internal combustion engine. Moreover, the motor and battery pack itself can be optimized. These hybrid vehicles can minimize the fuel demand and are the best substitute for the classic internal combustion engines.

Comparative analysis of hybrid vehicle energy management strategies with optimization of fuel economy and battery life

Hybrid Electric Vehicles (HEV) form an important category of the automotive segment and most importantly fill the transition between internal combustion engines powered conventional vehicles and electric motor-powered vehicles. One of the main propagandas and sales factor of HEV is improved fuel economy over conventional vehicles. Over the recent years, considering the longevity of HEV usage, battery life evaluation has been brought to the forefront of research along with fuel economy and the key method to slow down the battery aging is through Energy Management Strategy (EMS). This research paper presents the comparative analysis of battery life optimization with different control strategies in a parallel hybrid vehicle. In the available research work, EMS considering battery aging is still lacking. This research work considers the impact of multiple parameters, including temperature and current on battery aging, providing more accurate battery life prediction. Four different control strategies are analyzed including, Thermostat, Fuzzy logic, Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) and Q-learning considering battery aging. Results are compared concerning battery aging and fuel economy. In this research, ECMS results show a 25% improved fuel economy compared to the rule-based strategy. Also, a market cost-analysis is depicted to show the monetary savings for each of the energy management strategy.

Evaluation of Hybrid, Electric and Fuel Cell Powertrain Solutions for Class 6-7 Medium Heavy-Duty Vehicles

<div class="section abstract"><div class="htmlview paragraph">Electrification of heavy-duty trucks has received significant attention in the past year as a result of future regulations in some states. For example, California will require a certain percentage of tractor trailers, delivery trucks and vans sold to be zero emission by 2035. However, the relatively low energy density of batteries in comparison to diesel fuel, as well as the operating profiles of heavy-duty trucks, make the application of electrified powertrain in these applications more challenging. Heavy-duty vehicles can be broadly classified into two main categories; long-haul tractors and vocational vehicles. Long-haul tractors offer limited benefit from electrification due to the majority of operation occurring at constant cruise speeds, long range requirements and the high efficiency provided by the diesel engine. However, vocational applications can realize a significant benefit from electrified powertrains due to their lower vehicle speeds, frequent start-stop driving and shorter operating range requirements.</div><div class="htmlview paragraph">As the heavy-duty industry deals with solving challenges around the application of electrified powertrains, there are multiple pathways that can be explored to meet future regulatory requirements. This paper is the second part of a two-paper series that focuses on evaluating electrified solutions for Class 6-7 medium heavy-duty vehicles in the 2027 and beyond time frame. Using a model-based approach, this paper presents a comprehensive analysis that compares the baseline diesel powertrain against multiple alternative powertrain configurations. These configurations include, an optimized parallel diesel hybrid, all-electric, diesel range extender, gasoline range extender, Compressed Natural Gas (CNG) range extender, and fuel cell range extender; which are evaluated on the basis of cost of ownership, fuel efficiency, emissions, payback period, lost payload opportunity, range, and battery life. The analysis shows that for a near-term less-disruptive scenario wherein the conventional diesel powertrain layout is preserved, a 48V P3 parallel hybrid vehicle configuration is the most cost-effective solution, while for a near-term more-disruptive scenario, the Internal Combustion Engine (ICE) based Range Extender Electric Vehicles (REEVs) have the most potential to reduce greenhouse gas emissions while achieving lower cost of ownership than the baseline vehicle. For a longer-term and more-disruptive scenario, the all-electric vehicle configuration can achieve parity with the baseline vehicle cost of ownership provided the vehicle driving range requirement is limited to 100-150 miles. The analysis further highlights that the overall benefit of an all-electric powertrain solution depends significantly on the future battery technology advancements and vehicle range requirements.</div></div>

Power on gear shift control strategy design for a parallel hydraulic hybrid vehicle

The power on gear shift control of a parallel hydraulic hybrid vehicle (PHHV) is researched in this paper. A multibody dynamic model is built to capture the main vibration characteristics of PHHV powertrain, based on which the power on gear shift control strategy is designed and verified. To avoid the vehicle driving torque interruption during gear shift, power on gear shift control strategy is designed. In the control strategy, hydraulic pump/motor (HPM) compensates the engine torque when the engine clutch is disengaged for gear shift. The engine clutch engagement process is carefully controlled by LQR control strategy to mitigate the vehicle powertrain vibration and improve the vehicle driving comfort. Extended Kalman filter (EKF) is adopted to estimate the PHHV parameters required by LQR control. The available HPM torque depends on its working pressure which varies a lot with different accumulator state of charge. So the HPM torque compensation capability is investigated by analysing the traction force requirement during gear shift. To obtain the gear shift traction force demand, a gear shift schedule considering both vehicle fuel economy and dynamic performance is designed. The simulation results show that the HPM could satisfy most of the torque compensation requirements during gear shifts with the designed gear shift schedule in typical driving cycles.