Electric Hydraulic Hybrid Research Articles

Extending battery lifetime of electric-hydraulic hybrid wheel loader through system parameter optimization

Parallel electric hydraulic hybrid powertrain (PEHH) is a promising substitute for electric direct drive to extend battery lifetime. The convex programming (CP) based simultaneous optimization method has been adopted in electric hybrid system parameter optimization, but it is difficult to extend it for PEHH due to its non-convexity. In this paper, a combined exhaustive search – convex programming (ES-CP) system parameter optimization method is proposed to address the non-convexity of PEHH system parameter optimization problem. In this method, the PEHH system parameter optimization problem is modeled and reformulated in convex form and the convex part is decoupled from the optimization problem. The CP method is used for the convex part and the ES method is used for the remained non-convex part. The ES-CP method is applied to the PEHH powertrain to extend its battery lifetime. Optimization results show that the battery lifetime of the ES-CP optimized PEHH system is 6.8% longer than that of the non-optimized system. The computing time of ES-CP is 97% lower than Latin hypercube sampling and 90% lower than genetic algorithm methods.

Improving productivity of a battery powered electric wheel loader with electric-hydraulic hybrid drive solution

Powertrain electrification is one of the effective ways to improve energy efficiency and reduce emissions for off-road mobile machines. However, the batteries in the electric mobile machine always go through frequent and large power shocks, weakening the machine's productivity. To improve the productivity of the battery powered wheel loader, an electric-hydraulic hybrid parallel propulsion system solution is proposed. The hybrid powertrain is reconstructed from a battery powered electric powertrain by introducing a hydraulic pump-accumulator subsystem. The hydraulic powertrain is coupled to the electric powertrain in parallel providing strong power assistance and hydraulic energy recovery. A torque distribution energy management strategy is developed to manage the power flows of two energy sources. With the proposed hybrid powertrain, the productivity of the wheel loader is improved by up to 17.4%. The energy efficiency and carbon emissions are analyzed.

Mode Switching and Consistency Control for Electric-Hydraulic Hybrid Steering System

Mode Switching and Consistency Control for Electric-Hydraulic Hybrid Steering System

Powertrain parameters and control strategy optimization of a novel master-slave electric-hydraulic hybrid vehicle

ABSTRACT The electric-hydraulic hybrid vehicle (EHHV) is an important research area of hybrid electric vehicles (HEV), which provides a competitive project compared to other hybrid technologies. This paper conducts comprehensive research on a master-slave electric-hydraulic hybrid vehicle (MSEHHV). After an integrated driving cycle, the battery state of charge (SOC) values for MSEHHV and electric vehicle (EV) are 44.65% and 38.27%. The economy of the MSEHHV is verified, which is obviously superior to the EV. To further explore the energy conservation potential of the MSEHHV, the research proposes a cooperative optimization method of powertrain parameters and control strategy. Specifically, the optimization objective is to improve SOC. The response surface method (RSM) fits the functional relation between design variables and optimization objective. An optimization model is constructed based on the response surface model. Ultimately, the particle swarm optimization (PSO) algorithm is used for the optimal solution to obtain the optimal parameter combination. To evaluate the adaptability of the method, the performance of three models in the actual driving cycle is compared. Simulation results suggest that the energy consumption of the optimized MSEHHV is 33.41% and 6.33% lower than that of EV and initial MSEHHV. The research provides a valuable reference for the optimal design of electric-hydraulic hybrid technology.

An energy saving rule-based strategy for electric-hydraulic hybrid wheel loaders

Recently, hybrid electric vehicles are getting popular as they are both clean and efficient. As one of the hybrid electric powertrains, the battery powered parallel electric-hydraulic hybrid powertrain (PEHHP) is zero emission and has better drive performance. But the energy use of the PEHHP highly depends on the control strategy. A proper energy management strategy is critical for torque distribution and hybrid powertrain efficiency improvement. In this paper, a real-time rule-based strategy is proposed to determine the torque distributions of the PEHHP. The proposed rule-based strategy is based on the component efficiency analysis. And it optimizes the electric motor operating points. Experiments are conducted to show the operation results of the parallel electric-hydraulic hybrid wheel loaders. The powertrain energy use of the proposed rule-based strategy is close to that of the global optimal dynamic programing strategy, with energy use gaps of 2.99%, and 6.10% in simulation and experiment respectively.

A Mode-Driven Control Strategy to Reduce Electric Drive Peak Power of Hybrid Wheel Loader Propulsion System

Powertrain electrification is an effective solution to reducing emission of mobile machines. In this paper, an electric-hydraulic hybrid drive for wheel loader propulsion system is studied, where it combines high energy density of electric drive and high power density of hydraulic drive. The braking energy is captured hydraulically and stored in an accumulator. This reduces the peak power of electric drive due to large vehicle acceleration and deceleration, making the peak electric power reduction possible. The energy management strategy is essential for hybrid powertrain since it determines how efficiently the power is transferred between different sources. The existing control strategies such as thermostat strategy and power follower strategy are largely dependent on the cycles and it is challenging to achieve satisfactory results when the cycle changes. The wheel loader loading cycle is a characterized cycle consisting of multiple predictable operating modes, giving some opportunities to develop a mode-driven strategy. In this paper a mode-driven control strategy for electric-hydraulic hybrid wheel loader is proposed to achieve peak electric power reduction over power follower strategy (baseline strategy), without sacrificing electric energy use and vehicle operation hours. In the strategy four modes are defined and the expected hydraulic SOC (system pressure) profile in each mode is scheduled so that it can provide power assist and capture regenerative braking energy as much as possible. By setting the pressure profile, the hydraulic charge sustaining is guaranteed. The system operation with mode-driven strategy is compared with power follower strategy through simulation studies. Results show that peak powers of battery and electric motor with mode-driven strategy are reduced by nearly 30% compared to power follower strategy. Results also show that the vehicle operation hour has been slightly increased by 3% by using mode-driven strategy. These results verify the effectiveness of proposed strategy for electric-hydraulic hybrid wheel loader.

Investigation of energy management strategy for a novel electric-hydraulic hybrid vehicle: Self-adaptive electric-hydraulic ratio

The innovation and development of energy management strategies attract more and more attention as a key technology in hybrid electric vehicles. This paper focuses on a novel type of electric-hydraulic hybrid vehicle with multiple working modes and zero emissions, which offers a deeper potential for energy efficiency. Steady-state simulation with a rule-based mode switching strategy verifies that the electric-hydraulic ratio in the hybrid driving mode can interfere with energy management performance. Committed to filling the literature gap on the electric-hydraulic ratio, this paper proposes the idea of combining deep reinforcement learning with a rule-based control strategy and employs the Twin Delayed Deep Deterministic Policy Gradient to control the electric-hydraulic ratio. Thereafter, an energy management strategy framework based on the self-adaptive electric-hydraulic ratio was developed. Offline training and online test can demonstrate that the proposed energy management strategy enables the self-adaptive electric-hydraulic ratio under various driving cycles and a significant reduction in the energy consumption rate. The research findings in this paper are the crystallization of traditional control strategy and advanced algorithm, which has stronger practical value and development prospects. This is the first time that the self-adaptive electric-hydraulic ratio is applied to the establishment of energy management strategies.

A Comprehensive Review of Energy Regeneration and Conversion Technologies Based on Mechanical–Electric–Hydraulic Hybrid Energy Storage Systems in Vehicles

The primary purpose of this paper is to investigate energy regeneration and conversion technologies based on mechanical–electric–hydraulic hybrid energy storage systems in vehicles. There has been renewed interest in hydraulic storage systems since evidence has been presented that shows that they have the distinct advantages of high energy output and energy recuperation compared to electrical energy recovery systems, which are widely applied in electric vehicles; however, they are known to be high-cost, with a complicated structure and not zero carbon. In this paper, we first review recent research on hydraulic energy regeneration and conversion technologies. Secondly, as the main part of this paper, the latest technological progress and breakthroughs of the mechanical–electric–hydraulic hybrid energy storage systems in vehicles—which are divided into four categories: passenger, minibus and bus, commercial vehicle and special vehicle—are analyzed and discussed in depth. In addition, the current research status of energy management techniques is presented and summarized. Finally, prospects and challenges are suggested and explained. It is evident from the literature review that the mechanical–electric–hydraulic hybrid systems perform excellently in vehicles. Clearly, this review will be helpful to understand, explore and define the hydraulic vehicle of the future concerning energy optimization and environmental friendliness.

Energy management strategy of a novel parallel electric-hydraulic hybrid electric vehicle based on deep reinforcement learning and entropy evaluation

An excellent energy management strategy is paramount to the new energy vehicle safety, durability, and reliability, which invariably affects the driving experience. This paper proposes a novel parallel electric-hydraulic hybrid electric vehicle (PEHHEV), which has the characteristics of multiple working modes and power sources. Aiming at the defects of outmoded control strategies, this paper applies the long short-term memory (LSTM) neural network to the proximal policy optimization (PPO) algorithm. A PPO-LSTM-based energy management strategy for PEHHEV to achieve optimal working mode switching is established. To avoid paying too much attention to the economy and neglecting other performance parameters, we design a local sample Shannon entropy to realize dynamic evaluation for performance parameters. Through offline training, online testing, and entropy evaluation, the energy consumption rate under WLTC and NEDC increased by 18.51% and 15.74% respectively. It is indicated that PEHHEV can maintain dynamic performance and obtain lower energy consumption under the PPO-LSTM-based energy management strategy, which verifies its feasibility and robustness. The investigation in this paper can improve energy management performance and fill the literature gap, which has more preponderance than other strategies. This is the first of its kind to apply PPO-LSTM and entropy evaluation to developing control strategies for modern vehicles.

Double deep Q-network guided energy management strategy of a novel electric-hydraulic hybrid electric vehicle

The configuration and development of energy management strategies (EMSs) are generating considerable interest regarding vehicles due to the rapid blossoming momentum of electric vehicles. Battery state of charge is one of the main characterization parameters for evaluating EMSs, so the practical advancement of the range is critical to the evolution of electric vehicles. A novel electric-hydraulic hybrid electric vehicle (EHHEV) is investigated in this paper, which has the characteristics of various working modes and multi-energy sources. According to the hybrid system's energy flow, a rule-based control strategy is established, and the superiority of EHHEV in energy management is verified by steady-state simulation. Further, this paper combines Q-learning with deep neural networks to construct a double deep Q-network (DDQN)-guided EMS to solve traditional control strategy and reinforcement learning issues. After appropriate hyperparameters setting and batch training, the EMS can make EHHEV realize the optimal switching among working modes. Experimental results showcase that the EMS can significantly enhance vehicle economy. This is the first of its kind to apply the DDQN to developing EMS for EHHEV.

Pressure optimization for hydraulic-electric hybrid biped robot power unit based on genetic algorithm

Biped robots have attracted increasing attention because of their flexible movement and strong adaptability to the surroundings. However, the small output torque and the weak impact resistance of the motor drive, as well as the large energy consumption of the hydraulic drive limit the performance of the biped robot drive system. Aiming at these shortcomings, an electric-hydraulic hybrid drive system of biped robot was proposed in this paper. The robot platform was designed based on the prototype of the Zhejiang Lab biped robot. The model of the hydraulic drive system and mechanical structure was established to analyze the dynamic characteristic and the load force during walking. The value function reflecting the energy consumption of the hydraulic drive system was proposed. The pressure of the accumulator in the hydraulic power unit was selected as the control parameter. In order to get the minimum value of the value function, so as to reduce the energy consumption of the hydraulic driving system, the control parameters were optimized by using the genetic algorithm. From the simulation results, the proposed optimization algorithm can improve efficiency by 3.49%.

Research on the Characteristics of Wheel Loader Boom Driven by the Electric Hydraulic Hybrid Drive System

Research on the Characteristics of Wheel Loader Boom Driven by the Electric Hydraulic Hybrid Drive System

Energy management strategy of a novel electric–hydraulic hybrid vehicle based on driving style recognition

Driving style is one of the typical factors that impact vehicle energy management during real-world vehicular operation.

Extending battery lifetime for electric wheel loaders with electric-hydraulic hybrid powertrain

Electrification is the future trend for construction machinery due to its advantage of zero-carbon emission. The battery of electric construction machinery has an inevitable degradation phenomenon, which will increase the maintenance cost. In this paper a parallel electric-hydraulic hybrid powertrain is proposed to extend battery lifetime of an electric wheel loader by utilizing a hydraulic powertrain to provide and capture power during launch and braking. Different operation modes are analyzed and a rule-based energy management strategy is developed to determine the holding and switching conditions of each mode. Powertrains and battery aging models are introduced in detail and dynamic simulation is developed in Simulink. Results show that the parallel electric-hydraulic hybrid powertrain with the proposed strategy can reduce the battery full equivalent cycles, thereby achieving a 15.64% improvement in the battery lifetime compared with the pure electric powertrain. Moreover, influences of the strategy parameters on extending the battery lifetime are further discussed, which gives a parametric design guideline to the energy management strategy.

Energy management strategy of four-wheel drive SUV electric-hydraulic hybrid (EHH) power system based on optimal instantaneous efficiency

To address the problem of a large inrush current in pure electric vehicles under high-power driving and braking conditions, which damages the cycle life of the battery, a hydraulic auxiliary drive system is used to design a four-wheel-drive sport utility vehicle (SUV) electric–hydraulic hybrid(EHH) power system. Energy management strategy (EMS) research on this EHH power system is conducted. An EMS based on optimal instantaneous efficiency is proposed. The simulation results under the China light-duty vehicle test cycle for passenger car (CLTC-P) show that control effect of the EMS can improve by 3.27% than the EMS based on rule and achieve 97.43% of control effect of the dynamic programming(DP) global optimal EMS in terms of economic performance. Then the model predictive control (MPC) method is used, and the instantaneous efficiency optimal EMS is optimized. The simulation results under the CLTC-P show that the instantaneous efficiency optimal EMS optimized by MPC has an economic performance improvement of 1.13% compared with the performance before optimization. It can improve by 4.36% than the EMS RB and achieve 98.54% of control effect of the dynamic programming(DP) global optimal EMS in terms of economic performance.

Optimal Braking Torque Distribution of Dual-Motor Front-Rear Individually Driven Electric-Hydraulic Hybrid Powertrain Based on Minimal Energy Loss

Regenerative braking is one of the most important methods for improving energy utilization in electric vehicles. Electric vehicles with front-rear, individually driven configurations exhibit significant potential and flexibility for recovering braking energy. To improve the system’s high-power impact tolerance, a high-power density hydraulic energy storage system can be incorporated to facilitate a full-drive dual-motor electric–hydraulic hybrid (DMEHH) powertrain. The DMEHH system is composed of an independently driven electric–hydraulic hybrid front axle and a purely electric rear axle. In this study, a method for distributing braking torque to minimize energy loss was devised based on the proposed DMEHH powertrain. Power loss models for both the electric and hydraulic subsystems have been developed. Loss minimization control was adapted for the power loss model of the permanent magnet synchronous motors in the powertrain, and the front-rear and front electric–hydraulic torque braking distribution maps were calculated using the energy-loss minimization rule. The application of torque distribution maps resulted in energy losses less than those of the general ideal torque distribution for all braking conditions. The energy loss decreased by 27.2% when the loss minimization control method was used in the WLTC cycle, and decreased by 29.1%, 25.5%, and 21.6% in UDDS, NEDC, and US06 driving cycles, respectively.

Electric hydraulic hybrid vehicle powertrain design and optimization-based power distribution control to extend driving range and battery life cycle

This paper presents a comprehensive optimization procedure of a series electric hydraulic hybrid vehicle powertrain and control through the interactive adaptive-weight genetic algorithm method. The optimization simultaneously maximizes the driving range and battery lifespan, while minimizing onboard energy storage system mass. In this context, the design variables of the overall hydraulic drivetrain and the electric system were optimized. Moreover, a fuzzy-logic controller, which outputs the electric motor start-stop state and the torque applied to the pump which pressurizes the accumulator, is likewise considered in the formulation of the optimization problem to tune its membership functions, rules and weights. To ensure robust solutions, the electric–hydraulic vehicle was optimized under a combination of three standard driving cycles, which have antagonistic characteristics. Among the obtained optimum solutions, two configurations stood out with the driving range of 167.95 km and 199.73 km when using a battery of 320.23 kg and 384.12 kg and obtaining a battery life cycle of 53177 h and 46531 h, respectively. Moreover, these solutions were also tested under four different real-world driving cycles to evaluate their autonomy and performance, reaching driving ranges from 147 km in highway scenarios to over 262 km in urban cycles. The optimum configurations were compared with an also optimum electric vehicle powered by a battery-ultracapacitor hybrid energy storage system, obtaining a reduction of up to 9.57% in the ratio between powertrain cost and driving range. Finally, the optimization results indicate that electric hydraulic hybrid vehicle powertrain architectures can be a very attractive propulsion technology regarding both sustainable and economical aspects, effectively reducing battery aging by the use of a high power density hydraulic accumulator, which acts as a peak power buffer unit.

Comparison on Energy Economy and Vibration Characteristics of Electric and Hydraulic in-Wheel Drive Vehicles

This paper compares the energy economy and vertical vibration characteristics of in-wheel drive electric vehicles (IEVs), in-wheel drive electric hydraulic hybrid vehicles (IHVs) and centralized drive electric vehicles (CEVs). The dynamic programming (DP) algorithm is used to explore the optimal energy consumption of each vehicle. The energy economy analysis shows that the IEV consumes more energy than the CEV due to its relatively lower electric motor efficiency, even with fewer driveline components. The IHV consumes much more energy than the IEV and CEV because of the energy loss in the hydraulic driveline. The vertical vibration analysis demonstrates that both IEV and IHV degrade the vehicle driving comfort due to increased unsprung mass. Taking the advantage of high power density of the hydraulic motor, IHV have less unsprung mass when compared with the IEV, which helps to mitigate the vibration problems caused by increased unsprung mass.

Real-time Energy Management Strategy for Oil-Electric-Liquid Hybrid System based on Lowest Instantaneous Energy Consumption Cost

For the oil–electric–hydraulic hybrid power system, a logic threshold energy management strategy based on the optimal working curve is proposed, and the optimal working curve in each mode is determined. A genetic algorithm is used to determine the optimal parameters. For driving conditions, a real-time energy management strategy based on the lowest instantaneous energy cost is proposed. For braking conditions and subject to the European Commission for Europe (ECE) regulations, a braking force distribution strategy based on hydraulic pumps/motors and supplemented by motors is proposed. A global optimization energy management strategy is used to evaluate the strategy. Simulation results show that the strategy can achieve the expected control target and save about 32.14% compared with the fuel consumption cost of the original model 100 km 8 L. Under the New European Driving Cycle (NEDC) working conditions, the energy-saving effect of this strategy is close to that of the global optimization energy management strategy and has obvious cost advantages. The system design and control strategy are validated.

Electric Hydraulic Hybrid Acceleration Strategy Based on Hydrostatic Transmission for Battery Rail Engineering Vehicle

Electric Hydraulic Hybrid Acceleration Strategy Based on Hydrostatic Transmission for Battery Rail Engineering Vehicle