Powertrain Fuel Consumption Modeling and Benchmark Analysis of a Parallel P4 Hybrid Electric Vehicle Using Dynamic Programming

The goal of this work is to develop a hybrid electric vehicle model that is suitable for use in a dynamic programming algorithm that provides the benchmark for optimal control of the hybrid powertrain. The benchmark analysis employs dynamic programming by backward induction to determine the globally optimal solution by solving the energy management problem starting at the final timestep and proceeding backwards in time. This method requires the development of a backwards facing model that propagates the wheel speed of the vehicle for the given drive cycle through the driveline components to determine the operating points of the powertrain. Although dynamic programming only searches the solution space within the feasible regions of operation, the benchmarking model must be solved for every admissible state at every timestep leading to strict requirements for runtime and memory. The backward facing model employs the quasi-static assumption of powertrain operation to reduce the fidelity of the model to accommodate these requirements. Verification and validation testing of the dynamic programming algorithm is conducted to ensure successful operation of the algorithm and to assess the validity of the determined control policy against a high-fidelity forward-facing vehicle model with a percent difference of fuel consumption of 1.2%. The benchmark analysis is conducted over multiple drive cycles to determine the optimal control policy that provides a benchmark for real-time algorithm development and determines control trends that can be used to improve existing algorithms. The optimal combined charge sustaining fuel economy of the vehicle is determined by the dynamic programming algorithm to be 32.99 MPG, a 52.6% increase over the stock 3.6 L 2019 Chevrolet Blazer.

Different human-simulated characteristics of the driver models in the forward simulation of hybrid electric vehicles

Based on the control theories of PID, fuzzy logic and expert PID, the driver models are built and applied in the forward simulation for hybrid electric vehicles （HEV）. The impact to the vehicle speed tracking and the fuel economy is compared among the different driver models. The different human-simulated characteristics of the driver models are emphatically analyzed. The analysis results indicate that the driver models based on PID, simple fuzzy logic and expert PID are corresponding to the handling characteristics of different drives. The driver models of different human-simulated characteristics bring the handling divergence of drivers with different driving level and habit to the HEV forward simulation, and that is significant to the all-around verification and validation of the control strategy for HEV. System simulation results of different driver models validate the impact of driver models to the dynamic and fuel economy performance of HEV.

MODELING AND IMPLEMENTATION OF SPEED GOVERNOR FOR THE HYBRID ELECTRIC VEHICLE ENGINE

A speed control analysis for an in-line gasoline fueled internal combustion （IC） engine is presented for the purpose of alleviation of high frequency oscillations in engine revolutions. A dynamic cylinder-by-cylinder model is proposed, base on slider-crank mechanism, which is extended to develop a digital governor providing a high fidelity estimation of rotary speed oscillation for hybrid vehicle engines. A modified PID controller that P and I gain is placed in feedback path is also described for hybrid electric vehicle （HEV） engine speed regulation, By comparison between measured and estimated signals, it is demonstrated that a good agreement has been achieved and the governor behaves an excellent damping speed ripple.

Power Matching and Simulation Analysis of ISG Hybrid Electric Vehicle

Aiming at the limitation of ISG light hybrid electric vehicle, the power matching design of ISG moderate hybrid electric vehicle was studied. According to the requirements of vehicle performance indicators, the parameters of vehicle power drive system including engine, ISG motor, battery and related power transmission system were designed;the basic control strategy of ISG moderate hybrid electric vehicle was put forward;the dynamic model of vehicle was established by using MATLAB/Simulink software platform, and the vehicle performance was simulated under selected cycle conditions. The simulation results show that the parameter matching of the drive system is reasonable, the power performance of the vehicle meets the corresponding requirements, and the fuel economy has been significantly improved.

Simulation research of energy management strategy for dual mode plug-in hybrid electrical vehicles

In this paper, a plug-in hybrid electrical vehicle(PHEV) is taken as the research object, and its dynamic performance and economic performance are taken as the research goals. Battery charge-sustaining(CS) period is divided into power mode and economy mode. Energy management strategy designing methods of power mode and economy mode are proposed. Maximum velocity, acceleration performance and fuel consumption are simulated during the CS period in the AVL CRUISE simulation environment. The simulation results indicate that the maximum velocity and acceleration time of the power mode are better than those in the economy mode. Fuel consumption of the economy mode is better than that in the power mode. Fuel consumption of PHEV during the CS period is further improved by using the methods proposed in this paper, and this is meaningful for research and development of PHEV.

A comparative study of hybrid electric vehicle fuel consumption over diverse driving cycles

Environmental pollution and declining resources of fossil fuels in recent years,have increased demand for better fuel economy and less pollution for ground transportation.Among the alternative solutions provided by researchers in recent decades,hybrid electric vehicles consisted of an internal combustion engine and an electric motor have been considered as a promising solution in the short-term.In the present study,fuel economy characteristics of a parallel hybrid electric vehicle are investigated by using numerical simulation.The simulation methodology is based on a fast forward facing simulation model of a parallel hybrid and an internal combustion engine powertrains.The objective of this study is to present the main parameters which result in an optimum combination of hybrid powertrain components in order to obtain a better fuel economy of hybrid powertrains regarding different driven cycles and hybridization factors.Then,the fuel consumption of the parallel hybrid electric vehicles are compared considering various driven cycles and hybridization factors.The results showed that the better fuel economy of hybrid powertrains increases by decreasing average load of the test cycle and the point of the best fuel economy for a particular average load of the cycle moves towards higher hybridization factors when the average load of the test cycle is reduced.

Realization and Analysis of Good Fuel Economy and Kinetic Performance of a Low-cost Hybrid Electric Vehicle

By using high-power and high-efficiency propulsion systems,current hybrid electric vehicles（HEVs） in market can achieve excellent fuel economy and kinetic performance.However,it is the cost of current HEVs that hinders HEVs coming into widespread use.A novel hybrid electric propulsion system is designed to balance HEV cost and performance for developing markets.A battery/supercapacitor-based hybrid energy storage system（HESS） is used to improve energy conversion efficiency and reduce battery size and cost.An all-in-one-controller（AIOC） which integrates engine electronic control unit（ECU）,motor ECU,and HESS management system is developed to save materials and energy,and reduce the influence of distribution parameters on circuit.As for the powertrain configuration,four schemes are presented：belt-driven starter generator（BSG） scheme,four-wheel drive HEV scheme,full HEV scheme,and ranger-extender electric vehicle（EV） scheme.Component selection and parameter matching for the propulsion system are performed,and an energy management strategy is developed based on powertrain configuration and selected components.Forward-facing simulation models are built,comprehending the control strategy based on the optimal engine torque for the low-cost hybrid electric propulsion system.Co-simulation of AVL CRUISE and Matlab/Simulink is presented and the best scheme is selected.The simulation results indicate that,for the best design,fuel consumption in urban driving condition is 4.11 L/（100 km） and 0-50 km/h accelerating time is 10.95 s.The proposed research can realize low-cost concept for HEV while achieving satisfactory fuel economy and kinetic performance,and help to improve commercialization of HEVs.

Designing the cooling system of a hybrid electric vehicle with multi-heat source

In order to reduce the power consumption and meet the cooling demand of every heat source component, three kinds of multi-heat source cooling system schemes were designed base on the characteristic of power split hybrid electric vehicle （HEV）. Using the numerical simulation meth- od, the power system heat transfer model was built. By comparing the performance of three differ- ent schemes through the Simulink simulation, the best cooling system scheme was found. Base on characteristics of these cooling system structures, the reasonableness of the simulation results were analyzed and verified. The results showed that the cooling system designation based on the numerical simulation could describe the cooling system performance accurately. This method could simplify the design process, improve design efficiency and provide a new way for designing a multi-heat source vehicle cooling system.

A Matlab-Based Simulation for Hybrid Electric Motorcycle

This paper presents a simulation and modeling package based on Matlab for a parallel hybrid electric motorcycle (HEM). The package consists of several main detailed models: internal combustion engine (ICE), motor, continuously variable transmission (CVT), battery, energy management system (EMS) etc . Each component is built as a library, and can be connected together according to the parallel HEM's topology. Simulation results, such as ICE power demand, motor power demand, battery instantaneous state of charge (SOC), pollution emissions etc. are given and discussed. Lastly experimental data verify our simulation results.

Estimation Method of State-of-Charge For Lithium-ion Battery Used in Hybrid Electric Vehicles Based on Variable Structure Extended Kalman Filter

Since the main power source of hybrid electric vehicle（HEV） is supplied by the power battery,the predicted performance of power battery,especially the state-of-charge（SOC） estimation has attracted great attention in the area of HEV.However,the value of SOC estimation could not be greatly precise so that the running performance of HEV is greatly affected.A variable structure extended kalman filter（VSEKF）-based estimation method,which could be used to analyze the SOC of lithium-ion battery in the fixed driving condition,is presented.First,the general lower-order battery equivalent circuit model（GLM）,which includes column accumulation model,open circuit voltage model and the SOC output model,is established,and the off-line and online model parameters are calculated with hybrid pulse power characteristics（HPPC） test data.Next,a VSEKF estimation method of SOC,which integrates the ampere-hour（Ah） integration method and the extended Kalman filter（EKF） method,is executed with different adaptive weighting coefficients,which are determined according to the different values of open-circuit voltage obtained in the corresponding charging or discharging processes.According to the experimental analysis,the faster convergence speed and more accurate simulating results could be obtained using the VSEKF method in the running performance of HEV.The error rate of SOC estimation with the VSEKF method is focused in the range of 5% to 10% comparing with the range of 20% to 30% using the EKF method and the Ah integration method.In Summary,the accuracy of the SOC estimation in the lithium-ion battery cell and the pack of lithium-ion battery system,which is obtained utilizing the VSEKF method has been significantly improved comparing with the Ah integration method and the EKF method.The VSEKF method utilizing in the SOC estimation in the lithium-ion pack of HEV can be widely used in practical driving conditions.

Energy Control of Plug-In Hybrid Electric Vehicles Using Model Predictive Control With Route Preview

The paper proposes an adoption of slope,elevation,speed and route distance preview to achieve optimal energymanagement of plug-in hybrid electric vehicles(PHEVs).Theapproach is to identify route features from historical and real-time traffic data,in which information fusion model and trafficprediction model are used to improve the information accuracy.Then,dynamic programming combined with equivalent con-sumption minimization strategy is used to compute an optimalsolution for real-time energy management.The solution is thereference for PHEV energy management control along the route.To improve the system's ability of handling changing situation,the study further explores predictive control model in the real-time control of the energy.A simulation is performed to modelPHEV under above energy control strategy with route preview.The results show that the average fuel consumption of PHEValong the previewed route with model predictive control(MPC)strategy can be reduced compared with optimal strategy andbase control strategy.

Identification of dynamic model parameters for lithium-ion batteries used in hybrid electric vehicles

An electrical equivalent circuit model for lithium-ion batteries used for hybrid electric vehicles （HEV） is presented. The model has two RC networks characterizing battery activation and concentration polarization process. The parameters of the model are identified using combined experimental and extended Kalman filter （EKF） recursive methods. The open-circuit voltage and ohmic resistance of the battery are directly measured and calculated from experimental measurements, respectively. The rest of the coupled dynamic parameters, i.e. the RC network parameters, are estimated using the EKF method. Experimental and simulation results are presented to demonstrate the efficacy of the proposed circuit model and parameter identification techniques for simulating battery dynamics.

A multivariable output neural network approach for simulation of plug-in hybrid electric vehicle fuel consumption

This study is laser focused on the simulation of fuel consumption and fuel economy label parameters of plug-in hybrid electric vehicles.While fuel economy is a key factor in the design of plug-in hybrid electric vehicles,a fuel economy label can educate customers about the economic advantage of purchasing a particular car.The fuel economy label of a PHEV consists of parameters like driving range,electrical energy consumption,fuel economy for city,highway,and combined use,battery recharge time,and fuel consumption rates.The study used an inverse function model of an artificial neural network to simulate and calculate the parameters of the fuel economy labels of PHEVs.Firstly,the selected parameters of the fuel economy label of plug-in hybrid electric vehicles were used to develop a single output model.The output variable of the single output model was then merged with dummy functions to form input variables for the inverse function model.The output variables simulated were engine size in litres;estimated driving range when the battery is fully charged in km,battery recharged time in hours,city fuel consumption(L/100 km),highway fuel consumption(L/100 km),combined fuel consumption(L/100 km),estimated driving range when the tank is full,carbon dioxide(CO_(2))emission in grams/km,electric motor power in kW,number of cylinders,and electrical charges consumed in kWh/100 km.Different cases of input variables were considered for the inverse function model.The accuracy of the model was 29.1 times greater than that of the conventional inverse artificial neural network model.

Co-simulation platform for single-shaft parallel HEVs based on Cruise+Matlab/Simulink

We reversely analyzed the energy management strategy （EMS） for a single-shaft parallel hybrid electric vehicle （HEV）, and build a forward co-simulation platform based on Cruise and Matlab. The vehicle dynamics model is built with Cruise, and control model is set up with Matlab/Simulink environment. The data between the two models are transferred by the Matlab API interface in Cruise. After mechanical and signal connections are completed, we establish the computing tasks and take the simulations of vehicle＇ s power performance, economy, and emission performance. The simulation results match the actual measurement results, which show that the co-simulation platform is correct and feasible. The platform can be used not only for a basic simulation platform to optimize further EMS, but also for the development of actual control system.

Analysis and Optimization of Transient Mode Switching Behavior for Power Split Hybrid Electric Vehicle with Clutch Collaboration

The power split hybrid electric vehicle(HEV)adopts a power coupling configuration featuring dual planetary gearsets and multiple clutches,enabling diverse operational modes through clutch engagement and disengagement.The multi-clutch configuration usually involves the collaboration of two clutches during the transient mode switching process,thereby substantially elevating control complexity.This study focuses on power split HEVs that integrate multi-clutch mechanisms and investigates how different clutch collaboration manners impact the characteristics of transient mode switching.The powertrain model for the power-split HEV is established utilizing matrix-based methodologies.Through the formulation of clutch torque curves and clutch collaboration models,this research systematically explores the effects of clutch engagement timing and the duration of clutch slipping state on transient mode switching behaviors.Building upon this analysis,an optimization problem for control parameters pertaining to the two collaborative clutches is formulated.The simulated annealing algorithm is employed to optimize these control parameters.Simulation results demonstrate that the clutch collaboration manners have a great influence on the transient mode switching performance.Compared with the pre-calibrated benchmark and the optimal solution derived by the genetic algorithm,the maximal longitudinal jerk and clutch slipping work during the transient mode switching process is reduced obviously with the optimal control parameters derived by the simulated annealing algorithm.The study provides valuable insights for the dynamic coordinated control of the power-split HEVs featuring complex clutch collaboration mechanisms.

Stochastic Modeling of Total Ground Irradiance of Horizontal Surface

This paper describes relatively simple stochastic model of the total ground irradiance of horizontal surface. For this purpose clearness index is modeled as a stochastic signal. The parameters of clearness index stochastic signal are chosen to fit values of daily mean insolation for each month for the location of Zagreb, Croatia. Complete model has been done in MATLAB. This model can be used for Monte Carlo simulations of technical solar systems such as photovoltaic systems or solar thermal energy systems.

Modeling and optimal energy management of a power split hybrid electric vehicle

With the combination of engine and two electric machines, the power split device allows higher efficiency of the engine. The operation modes of a power split HEV are analyzed, and the system dynamic model is established for HEV forward simulation and controller design. Considering the fact that the operation modes of the HEV are event-driven and the system dynamics is continuous time-driven for each mode, the structure of the controller is built and described with the hybrid automaton control theory. In this control structure, the mode selection process is depicted by the finite state machine （FSM）. The multi-mode switch controller is designed to realize power distribution. Furthermore, the vehicle mode operations are optimized, and the nonlinear model predictive control （NMPC） strategy is applied by implementing dynamic programming （DP） in the finite pre- diction horizon. Comparative simulation results demonstrate that the hybrid control structure is effective and feasible for HEV energy management design. The NMPC optimal strategy is superior in improving fuel economy.

Machine learning modeling for fuel cell-battery hybrid power system dynamics in a Toyota Mirai 2 vehicle under various drive cycles

Electrification is considered essential for the decarbonization of mobility sector, and understanding and modeling the complex behavior of modern fuel cell-battery electric-electric hybrid power systems is challenging, especially for product development and diagnostics requiring quick turnaround and fast computation. In this study, a novel modeling approach is developed, utilizing supervised machine learning algorithms, to replicate the dynamic characteristics of the fuel cell-battery hybrid power system in a 2021 Toyota Mirai 2nd generation (Mirai 2) vehicle under various drive cycles. The entire data for this study is collected by instrumenting the Mirai vehicle with in-house data acquisition devices and tapping into the Mirai controller area network bus during chassis dynamometer tests. A multi-input - multi-output, feed-forward artificial neural network architecture is designed to predict not only the fuel cell attributes, such as average minimum cell voltage, coolant and cathode air outlet temperatures, but also the battery hybrid system attributes, including lithium-ion battery pack voltage and temperature with the help of 15 system operating parameters. Over 21,0000 data points on various drive cycles having combinations of transient and near steady-state driving conditions are collected, out of which around 15,000 points are used for training the network and 6,000 for the evaluation of the model performance. Various data filtration techniques and neural network calibration processes are explored to condition the data and understand the impact on model performance. The calibrated neural network accurately predicts the hybrid power system dynamics with an R-squared value greater than 0.98, demonstrating the potential of machine learning algorithms for system development and diagnostics.

Cost-efficient Thermal Management for a 48V Li-ion Battery in a Mild Hybrid Electric Vehicle

The 48V mild hybrid system is a cost-efficient solution for original equipment manufacturers to meet increasingly stringent fuel consumption requirements.However,hybrid functions such as auto-stop/start and brake regeneration are unavailablewhen a 48V battery is at very low temperature because of its limited charge and discharge capability.Therefore,it is important to develop cost-efficient thermal management to warm-up the battery of a 48V mild hybrid electric vehicle(HEV)to recover hybrid functions quickly in cold climate.Following the model-based“V”process,we first define the requirements and then design different mechanisms to heat a 48V battery.Afterward,we build a 48V battery model in LMS AMESim and conduct co-simulation with simplified battery management system and hybrid control unit algorithms in MATLAB Simulink for analysis.Finally,we carry out a series of vehicle experiments at low temperature and observe the effect of heating to validate the design.Both simulation results and experimental data show that a cold 48V battery placed in a cabin with hot air can be heated effectively in the developed“Enhanced Generator Mode with 48V Battery”mode.The entire design is in a newly developed software that cyclically charges and discharges a 48V battery for quick warm-up in cold temperature without needing any additional hardware such as a heater,making it a cost-efficient solution for HEVs.

基于Plant Simulation的AGV输送系统仿真分析及其应用

在电表自动化检定线中,采用自动导引车(AGV)作为输送单元,连接电表检定单元与库房接驳处。针对AGV输送系统仿真建模问题,提出了一种基于Plant Simulation仿真工具的仿真建模方法,为生产实践中分析自动导引车系统(AGVS)探索出一条可行之路,建立了某电表自动化检定线中AGVS模型,运用所建模型分析了系统所需AGV数量,从而验证了所提建模方法的有效性。