Optimization Control of Multi-Mode Coupling All-Wheel Drive System for Hybrid Vehicle

The all-wheel drive(AWD)hybrid system is a research focus on high-performance new energy vehicles that can meet the demands of dynamic performance and passing ability.Simultaneous optimization of the power and economy of hybrid vehicles becomes an issue.A unique multi-mode coupling(MMC)AWD hybrid system is presented to realize the distributed and centralized driving of the front and rear axles to achieve vectored distribution and full utilization of the system power between the axles of vehicles.Based on the parameters of the benchmarking model of a hybrid vehicle,the best model-predictive control-based energy management strategy is proposed.First,the drive system model was built after the analysis of the MMC-AWD’s drive modes.Next,three fundamental strategies were established to address power distribution adjustment and battery SOC maintenance when the SOC changed,which was followed by the design of a road driving force observer.Then,the energy consumption rate in the average time domain was processed before designing the minimum fuel consumption controller based on the equivalent fuel consumption coefficient.Finally,the advantage of the MMC-AWD was confirmed by comparison with the dynamic performance and economy of the BYD Song PLUS DMI-AWD.The findings indicate that,in comparison to the comparative hybrid system at road adhesion coefficients of 0.8 and 0.6,the MMC-AWD’s capacity to accelerate increases by 5.26%and 7.92%,respectively.When the road adhesion coefficient is 0.8,0.6,and 0.4,the maximum climbing ability increases by 14.22%,12.88%,and 4.55%,respectively.As a result,the dynamic performance is greatly enhanced,and the fuel savings rate per 100 km of mileage reaches 12.06%,which is also very economical.The proposed control strategies for the new hybrid AWD vehicle can optimize the power and economy simultaneously.

Parameters Matching and Control Method of Hydraulic Hybrid Vehicles with Secondary Regulation Technology

Hydraulic hybrid vehicles （HHV） with secondary regulation technology has the potential of improving fuel economy by operating the engine in the optimum efficiency range and making use of regenerative braking. Hydrostatic transmission technology has the advantage of higher power density and the ability to accept the high rates and high frequencies of charging and discharging, both of which are not favorable for batteries, but the lower energy density requires special power matching design and control strategy to coordinate all the powertrain components in an optimal manner. A multi-objective optimization method is proposed to distinguish the components size values of HHV by considering the requirements of driving cycles and technology aspects. The regenerative braking strategy and energy control strategy based on the optimized HHV is proposed to recovery the braking energy and distribute the regenerated braking energy. Simulation results show that by taking the optimized configuration of HHV, adopting the regenerative braking strategy and energy control strategy are helpful to improve the system efficiency and fuel economy of HHV under urban driving cycles.

Design and Fuel Consumption Optimization for a Bio-Inspired Semi-floating Hybrid Vehicle

Based on a bionic concept and combing air-cushion techniques and track driving mechanisms, a novel semi-floating hybrid concept vehicle is proposed to meet the transportation requirements on soft terrain. First, the vehicle scheme and its improved duel-spring flexible suspension design are described. Then, its fuel consumption model is proposed accordingly with respect to two vehicle operating parameters. Aiming at minimizing the fuel consumption, two Genetic Algorithms （GAs） are designed and implemented. For the initial one （GA-1）, despite getting an acceptable result, there still existed some problems in its optimiza- tion process. Based on an analysis of the defects of GA-1, an improved algorithm GA-2 was developed whose effectiveness and stability were embodied in the optimization process and results. The proposed design scheme and optimization approaches can provide valuable references for this new kind of vehicle with industry, military or scientific exploitations, etc. promising applications in the areas of agriculture, petroleum industry, military or scientific explaitations, etc.

Energy control strategy for parallel hydrostatic transmission hybrid vehicles

Aimed at the relatively lower energy density and complicated coordinating operation between two power sources,a special energy control strategy is required to maximize the fuel saving potential.Then a new type of configuration for hydrostatic transmission hybrid vehicles(PHHV) and the selection criterion for important components are proposed.Based on the optimization of planet gear transmission ratio and the analysis of optimal energy distribution for the proposed PHHV on a representative urban driving cycle,a fuzzy torque control strategy and a braking energy regeneration strategy are designed and developed to realize the real-time control of energy for the proposed PHHV.Simulation results demonstrate that the energy control strategy effectively improves the fuel economy of PHHV.

Operation control strategy of series-parallel hydraulic hybrid vehicle

A series-parallel hydraulic hybrid system applied to public buses is put torwaro, ano parameters of key components are analyzed and determined. Energy management strategy based on logic thresh- old is designed which is aimed at efficient operation of the overall system considering the operational characteristic of the components and taking the curves of engine, hydraulic pump/motor and hydrau- lic pump as the main design basis; regenerative control strategy which makes regenerative brake sys- tem and frictional brake system work harmoniously is designed to raise recovery rate of regenerative brake energy. System dynamic modeling and simulation results show that the energy control strategy designed here is able to adapt system to changes of working condition and switch the operating mode reasonably. The regenerative braking control strategy is effective in raising the utilization of energy and improving fuel economy.

Electromechanical Aspects of an Experimental Hybrid Vehicle

The design and construction of an experimental solar hybrid vehicle based on the combination of photovoltaic solar energy as the main source of electricity and electric power supplied by a generator activated by the driver＇s pedaling is introduced. The vehicle has a battery to store the energy provided by both systems. The development was motivated by a Latin American solar car race through the Atacama Desert in Chile and the initiative to promote the use of clean energy for transport. A general description of the vehicle, its energetic aspects and experimental results are presented.

Series-parallel Hybrid Vehicle Control Strategy Design and Optimization Using Real-valued Genetic Algorithm

Despite the series-parallel hybrid electric vehicle inherits the performance advantages from both series and parallel hybrid electric vehicle, few researches about the series-parallel hybrid electric vehicle have been revealed because of its complex co nstruction and control strategy. In this paper, a series-parallel hybrid electric bus as well as its control strategy is revealed, and a control parameter optimization approach using the real-valued genetic algorithm is proposed. The optimization objective is to minimize the fuel consumption while sustain the battery state of charge, a tangent penalty function of state of charge（SOC） is embodied in the objective function to recast this multi-objective nonlinear optimization problem as a single linear optimization problem. For this strategy, the vehicle operating mode is switched based on the vehicle speed, and an ＂optimal line＂ typed strategy is designed for the parallel control. The optimization parameters include the speed threshold for mode switching, the highest state of charge allowed, the lowest state of charge allowed and the scale factor of the engine optimal torque to the engine maximum torque at a rotational speed. They are optimized through numerical experiments based on real-value genes, arithmetic crossover and mutation operators. The hybrid bus has been evaluated at the Chinese Transit Bus City Driving Cycle via road test, in which a control area network-based monitor system was used to trace the driving schedule. The test result shows that this approach is feasible for the control parameter optimization. This approach can be applied to not only the novel construction presented in this paper, but also other types of hybrid electric vehicles.

Controlling Torque Distribution for Parallel Hybrid Vehicle Based on Hierarchical Structure Fuzzy Logic

The Hierarchical Structure Fuzzy Logic Control (HSFLC) strategies of torque distribute for Parallel Hybrid Electric Vehicle (PHEV) in the mode of operation of the vehicle i. e. , acceleration, cruise, deceleration etc. have been studied. Using secondly developed the hybrid vehicle simulation tool ADVISOR, the dynamic model of PHEV has been set up by MATLAB/SIMULINK. The engine, motor as well as the battery characteristics have been studied. Simulation results show that the proposed hierarchical structured fuzzy logic control strategy is effective over the entire operating range of the vehicle in terms of fuel economy. Based on the analyses of the simulation results and driver’s experiences, a fuzzy controller is designed and developed to control the torque distribution. The controller is evaluated via hardware-in-the-loop simulator (HILS). The results show that controller verify its value.

An adaptive fuel cell hybrid vehicle propulsion sizing model

As we enter the age of electrochemical propulsion,there is an increasing tendency to discuss the viability or otherwise of different electrochemical propulsion systems in zero-sum terms.These discussions are often grounded in a specific use case;however,given the need to electrify the wider transport sector it is evident that we must consider systems in a holistic fashion.When designed adequately,the hybridisation of power sources within automotive applications has been demonstrated to positively impact fuel cell efficiency,durability,and cost,while having potential benefits for the safety of vehicles.In this paper,the impact of the fuel cell to battery hybridisation degree is explored through the key design parameter of system mass.Different fuel cell electric hybrid vehicle(FCHEV)scenarios of various hydridisation degrees,including light-duty vehicles(LDVs),Class 8 heavy goods vehicles(HGVs),and buses are modelled to enable the appropriate sizing of the proton exchange membrane(PEMFC)stack and lithium-ion battery(LiB)pack and additional balance of plant.The operating conditions of the modelled PEMFC stack and battery pack are then varied under a range of relevant drive cycles to identify the relative performance of the systems.By extending the model further and incorporating a feedback loop,we are able to remove the need to include estimated vehicle masses a priori enabling improving the speed and accuracy of the model as an analysis tool for vehicle mass and performance estimation.

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.

Enhanced Deep Reinforcement Learning Strategy for Energy Management in Plug-in Hybrid Electric Vehicles with Entropy Regularization and Prioritized Experience Replay

Plug-in Hybrid Electric Vehicles(PHEVs)represent an innovative breed of transportation,harnessing diverse power sources for enhanced performance.Energy management strategies(EMSs)that coordinate and control different energy sources is a critical component of PHEV control technology,directly impacting overall vehicle performance.This study proposes an improved deep reinforcement learning(DRL)-based EMSthat optimizes realtime energy allocation and coordinates the operation of multiple power sources.Conventional DRL algorithms struggle to effectively explore all possible state-action combinations within high-dimensional state and action spaces.They often fail to strike an optimal balance between exploration and exploitation,and their assumption of a static environment limits their ability to adapt to changing conditions.Moreover,these algorithms suffer from low sample efficiency.Collectively,these factors contribute to convergence difficulties,low learning efficiency,and instability.To address these challenges,the Deep Deterministic Policy Gradient(DDPG)algorithm is enhanced using entropy regularization and a summation tree-based Prioritized Experience Replay(PER)method,aiming to improve exploration performance and learning efficiency from experience samples.Additionally,the correspondingMarkovDecision Process(MDP)is established.Finally,an EMSbased on the improvedDRLmodel is presented.Comparative simulation experiments are conducted against rule-based,optimization-based,andDRL-based EMSs.The proposed strategy exhibitsminimal deviation fromthe optimal solution obtained by the dynamic programming(DP)strategy that requires global information.In the typical driving scenarios based onWorld Light Vehicle Test Cycle(WLTC)and New European Driving Cycle(NEDC),the proposed method achieved a fuel consumption of 2698.65 g and an Equivalent Fuel Consumption(EFC)of 2696.77 g.Compared to the DP strategy baseline,the proposed method improved the fuel efficiency variances(FEV)by 18.13%,15.1%,and 8.37%over the Deep QNetwork(DQN),Double DRL(DDRL),and original DDPG methods,respectively.The observational outcomes demonstrate that the proposed EMS based on improved DRL framework possesses good real-time performance,stability,and reliability,effectively optimizing vehicle economy and fuel consumption.

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

As government agencies continue to tighten emissions regulations due to the continued increase in greenhouse gas production, automotive industries are seeking to produce increasingly efficient vehicle technology. Hybrid electric vehicles (HEVs) have been introduced to mitigate problems while improving fuel economy. HEVs have led to the demand of creating more advanced controls software to consider multiple components for propulsive power in a vehicle. A large section in the software development process is the implementation of an optimal energy management strategy meant to improve the overall fuel efficiency of the vehicle. Optimal strategies can be implemented when driving conditions are known a prior. The Equivalent Consumption Minimization Strategy (ECMS) is an optimal control strategy that uses an equivalence factor to equate electrical to mechanical power when performing torque split determination between the internal combustion engine and electric motor for propulsive and regenerative torque. This equivalence factor is determined from offline vehicle simulations using a sensitivity analysis to provide optimal fuel economy results while maintaining predetermined high voltage battery state of charge (SOC) constraints. When the control hierarchy is modified or different driving styles are applied, the analysis must be redone to update the equivalence factor. The goal of this work is to implement a fuzzy logic controller that dynamically updates the equivalence factor to improve fuel economy, maintain a strict charge sustaining window of operation for the high voltage battery, and reduce computational time required during algorithm development. The adaptive algorithm is validated against global optimum fuel economy and charge sustaining results from a sensitivity analysis performed for multiple drive cycles. Results show a maximum fuel economy improvement of 9.82% when using a mild driving style and a 95% success rate when maintaining an ending SOC within 5% of the desired SOC regardless of starting SOC.

Novel flexible hybrid electric system and adaptive online-optimal energy management controller for plug-in hybrid electric vehicles

In order to achieve the improvement of the driving comfort and energy efficiency,an new e-CVT flexible full hybrid electric system(E2FHS) is proposed,which uses an integrated main drive motor and generator to take the place of the original automatic or manual transmission to realize the functions of continuously variable transmission(e-CVT).The design and prototype realization of the E2FHS system for a plug-in hybrid vehicle(PHEV) is performed.In order to analyze and optimize the parameters and the power flux between different parts of the E2FHS,simulation software is developed.Especially,in order to optimize the performance of the energy economy improvement of the E2FHS,the effect of the different energy management controllers is investigated,and an adaptive online-optimal energy management controller for the E2FHS is built and validated by the prototype PHEV.

Characteristic analysis of hydraulic hybrid vehicle based on limit cycle

The theory of limit cycles was applied to hydraulic hybrid vehicle (HHV) to analyze the dynamic characteristics of the system. The exact mathematical models based on configuration diagram of HHV were built to study on equilibrium points, nonexistence of limit cycle and stability of equilibrium points. The analysis showed that if the Young's modulus of fluid is neglected, the equilibrium points of the system will be distributed on both sides of the initial function. In addition, there is a unique equilibrium point according to the practical signification of the system parameters. The nonexistence analysis showed that there is no limit cycle for the system, no matter how the viscosity coefficient B changes. The stability analysis of equilibrium points showed that the system is asymptotically stable about the equilibrium point at B≥0 and the equilibrium point is the center point of the system at B=0. Finally, the phase diagrams of global topological structure of HHV system were entirely described according to qualitative analysis of the singular points at infinity.

Modern Control and Diagnostic System of Traction Vehicle with Hybrid Drive System

In paper it introduced a review of modem traction vehicle drive system with induction motor drive system （PMSM with single or dual rotor drive system） or BLDC motor with different configuration of magnetic circuits. For particular part of drive system proposed a quasi intelligent control system version smart control enables multi criteria predictive control of vehicle work. In the paper presented also a selected diagnostic procedure, enables monitoring exploitation parameters, and prediction of probable failure state. For different vehicle work state realized a simulation models and crash test of exploitations failure models.

Performance Analysis of Plug-in Hybrid Passenger Vehicles

PHEVs （passenger plug-in hybrid electric vehicles） have shown significant fuel reduction potential. Furthermore, PHEVs can also improve longitudinal vehicle dynamics with respect to acceleration and engine elasticity. The objective of this study is to investigate potential of concurrent optimization of fuel efficiency and driving performance. For the studies, a backward vehicle model for a parallel PHEV was designed, where the power flow is calculated from the wheels to the propulsion units, the conventional ICE （internal combustion engine） and the EMG （electric motor/generator） unit. The hybrid drive train is according to a P2 layout, consequently the EMG is situated between the shifting clutch and the ICE. The implemented operation strategy distributes the power to both propulsion units depending on the vehicle speed, requested driving torque, the battery＇s SOC （state of charge） and SOP （state of power）. Additional information, such as the slope of the road, can be taken into account by the operation strategy. In the paper, the fuel saving potential as well as the longitudinal dynamics change of different PHEV configurations is presented as a function of battery capacity and EMG power. Consequently, applicable hybrid components can be defined. By using additional information of the environment like various sensor data, road slope amongst others, the fuel saving potential can be improved even more. By studying the dynamic model, the overall results of the backward model are confirmed. In conclusion, this study shows that it is possible to concurrently reduce fuel consumption and increase driving performance in PHEVs. The potential depends strongly on the configuration of the electric components and the implemented operation strategy. Consequently, the hybrid system configuration has to be chosen carefully and aligned to the vehicle performance.

The Future Trend of E-Mobility in Terms of Battery Electric Vehicles and Their Impact on Climate Change: A Case Study Applied in Hungary

The transportation sector is responsible for 25% of the total Carbon dioxide (CO2) emissions, whereas 60.6% of this sector represents small and medium passenger cars. However, as noted by the European Union Long-term strategy, there are two ways to reduce the amount of CO2 emissions in the transportation sector. The first way is characterized by creating more efficient vehicles. In contrast, the second way is characterized by changing the fuel used. The current study addressed the second way, changing the fuel type. The study examined the potential of battery electric vehicles (BEVs) as an alternative fuel type to reduce CO2 emissions in Hungarys transportation sector. The study used secondary data retrieved from Statista and stata.com to analyze the future trends of BEVs in Hungary. The results showed that the percentage of BEVs in Hungary in 2022 was 0.4% compared to the total number of registered passenger cars, which is 3.8 million. The simple exponential smoothing (SES) time series forecast revealed that the number of BEVs is expected to reach 84,192 in 2030, indicating a percentage increase of 2.21% in the next eight years. The study suggests that increasing the number of BEVs is necessary to address the negative impact of CO2 emissions on society. The Hungarian Ministry of Innovation and Technologys strategy to reduce the cost of BEVs may increase the percentage of BEVs by 10%, resulting in a potential average reduction of 76,957,600 g/km of CO2 compared to gasoline, diesel, hybrid electric vehicles (HEVs), and plug-in hybrid vehicles (PHEVs).

Design and Analysis of Electro-mechanical Hybrid Anti-lock Braking System for Hybrid Electric Vehicle Utilizing Motor Regenerative Braking

Braking on low adhesion-coefficient roads, hybrid electric vehicle＇s motor regenerative torque is switched off to safeguard the normal anti-lock braking system （ABS） fimction. When the ABS control is terminated, the motor regenerative braking is readmitted. Aiming at avoiding permanent cycles from hydraulic anti-lock braking to motor regenerative braking, a novel electro-mechanical hybrid anti-lock braking system using fuzzy logic is designed. Different from the traditional single control structure, this system has a two-layered hierarchical structure, The first layer is responsible for harmonious adjustment or interaction between regenerative system and anti-lock braking system. The second layer is responsible for braking torque distribution and adjustment. The closed-loop simulation model is built. Control strategy and method for coordination between regenerative and anti-lock braking are developed. Simulation braking on low adhesion-coefficient roads with fuzzy logic control and real vehicle braking field test are presented. The results from simulating analysis and experiment show braking performance of the vehicle is perfect, harmonious coordination between regenerative and anti-lock braking function, significant amount of braking energy can be recovered and the proposed control strategy and method are effective.

Synthetical Efficiency-based Optimization for the Power Distribution of Power-split Hybrid Electric Vehicles

Now the optimization strategies for power distribution are researched widely, and most of them are aiming to the optimal fuel economy and the driving cycle must be preknown. Thus if the actual driving condition deviates from the scheduled driving cycle, the effect of optimal results will be declined greatly. Therefore, the instantaneous optimization strategy carried out on-line is studied in this paper. The power split path and the transmission efficiency are analyzed based on a special power-split scheme and the efficiency models of the power transmitting components are established. The synthetical efficiency optimization model is established for enhancing the transmission efficiency and the fuel economy. The identification of the synthetical efficiency as the optimization objective and the constrain group are discussed emphatically. The optimization is calculated by the adaptive simulated annealing （ASA） algorithm and realized on-line by the radial basis function （RBF）-based similar models. The optimization for power distribution of the hybrid vehicle in an actual driving condition is carried out and the road test results are presented. The test results indicate that the synthetical efficiency optimization method can enhance the transmission efficiency and the fuel economy of the power-split hybrid electric vehicle （HEV） observably. Compared to the rules-based strategy the optimization strategy is optimal and achieves the approximate global optimization solution for the power distribution. The synthetical efficiency optimization solved by ASA algorithm can give attentions to both optimization quality and calculation efficiency, thus it has good application foreground for the power distribution of power-split HEV.

OPTIMIZATION APPROACH FOR HYBRID ELECTRIC VEHICLE POWERTRAIN DESIGN

According to bench test results of fuel economy and engine emission for thereal power-train system of EQ7200HEV car. a 3-D performance map oriented quasi-linear model isdeveloped for the configuration of the powertrain components such as internal combustion engine,traction electric motor, transmission, main retarder and energy storage unit. A genetic algorithmbased on optimization procedure is proposed and applied for parametric optimization of the keycomponents by consideration of requirements of some driving cycles. Through comparison of numericalresults obtained by the genetic algorithm with those by traditional optimization methods, it isshown that the present approach is quite effective and efficient in emission reduction and fueleconomy for the design of the hybrid electric car powertrain.