Hybrid Electric Vehicle System Research Articles

A Smart ANN-Based Converter for Efficient Bidirectional Power Flow in Hybrid Electric Vehicles

Electric vehicles (EV) are promising alternate fuel technologies to curtail vehicular emissions. A modeling framework in a hybrid electric vehicle system with a joint analysis of EV in powering and regenerative braking mode is introduced. Bidirectional DC–DC converters (BDC) are important for widespread voltage matching and effective for recovery of feedback energy. BDC connects the first voltage source (FVS) and second voltage source (SVS), and a DC-bus voltage at various levels is implemented. The main objectives of this work are coordinated control of the DC energy sources of various voltage levels, independent power flow between both the energy sources, and regulation of current flow from the DC-bus to the voltage sources. Optimization of the feedback control in the converter circuit of HEV is designed using an artificial neural network (ANN). Applicability of the EV in bidirectional power flow management is demonstrated. Furthermore, the dual-source low-voltage buck/boost mode enables independent power flow management between the two sources—FVS and SVS. In both modes of operation of the converter, drive performance with an ANN is compared with a conventional proportional–integral control. Simulations executed in MATLAB/Simulink demonstrate low steady-state error, peak overshoot, and settling time with the ANN controller.

Multi-Level Model Predictive Control for the Energy Management of Hybrid Electric Vehicles Including Thermal Derating

This paper presents an online-capable controller based on model predictive control for the energy management system of a parallel hybrid electric vehicle, which is equipped with two electric motors (EM). Its task is to minimize the vehicle’s fuel consumption along a predicted driving mission. If the fuel consumption is the only cost to be minimized, a frequent use of the electric components ensues. This can result in their overheating, which is prevented in practice by defining a maximum motor temperature, beyond which the motor’s maximum capability is decreased drastically. This is referred to as thermal derating. A multi-level control structure is proposed that explicitly includes maximum temperature bounds on both EMs. The high-level controller is developed based on the model predictive control approach. The low-level controller is based on Pontryagin’s maximum principle and is an extension of the equivalent consumption minimization strategy. To validate the multi-level control structure, three test scenarios of increasing difficulty are presented in simulation. They include thermal disturbances as well as driving mission mispredictions and are provided to demonstrate the robustness of the control algorithm. The proposed controller is able to recover 70% of the loss of optimality of a state-of-the-art predictive controller. A comparison to a dynamic programming optimization reveals close to optimal results.

Multi Source Electric Vehicles: Smooth Transition Algorithm for Transient Ripple Minimization

Any engineering system involves transitions that reduce the performance of the system and lower its comfort. In the field of automotive engineering, the combination of multiple motors and multiple power sources is a trend that is being used to enhance hybrid electric vehicle (HEV) propulsion and autonomy. However, HEV riding comfort is significantly reduced because of high peaks that occur during the transition from a single power source to a multisource powering mode or from a single motor to a multiple motor traction mode. In this study, a novel model-based soft transition algorithm (STA) is used for the suppression of large transient ripples that occur during HEV drivetrain commutations and power source switches. In contrast to classical abrupt switching, the STA detects transitions, measures their rates, generates corresponding transition periods, and uses adequate transition functions to join the actual and the targeted operating points of a given HEV system variable. As a case study, the STA was applied to minimize the transition ripples that occur in a fuel cell-supercapacitor HEV. The transitions that occurred within the HEV were handled using two proposed transition functions which were: a linear-based transition function and a stair-based transition function. The simulation results show that, in addition to its ability to improve driving comfort by minimizing transient torque ripples and DC bus voltage fluctuations, the STA helps to increase the lifetime of the motor and power sources by reducing the currents drawn during the transitions. It is worth noting that the considered HEV runs on four-wheel drive when the load torque applied on it exceeds a specified torque threshold; otherwise, it operates in rear-wheel drive.

A Control Algorithm of an Idle Stop and Go System With Traffic Conditions for Hybrid Electric Vehicles

This paper proposes a control algorithm for IGS (Idle Stop and Go) systems and a stop prediction algorithm. A new ISG system has been introduced to reduce fuel consumption by improving the conventional ISG system for hybrid electric vehicles (HEVs), including plug-in hybrid electric vehicles (PHEVs). When the HEVs stop or pass in front of a traffic light, the control algorithm determines when to turn off the internal combustion engine (ICE) while comparing the driving energy consumption. A stop prediction algorithm was proposed to determine whether a vehicle will stop at traffic lights. Traffic information and driving test results were obtained using a driver-in-the-loop simulator, and the stop prediction algorithm was verified through these results. The proposed ISG with the stop prediction function can reduce fuel consumption. With the developed traffic flow model, the effectiveness of the proposed ISG was evaluated in terms of the transportation system.

Optimal operation of residential energy Hubs include Hybrid electric vehicle & Heat storage system by considering uncertainties of electricity price and renewable energy

In this paper proposes an efficient hybrid method for optimizing the Residential Energy Hubs (REH), which is incorporated into Plug-in Hybrid Electric Vehicle (PHEV) and Heat Storage System (HSS). The proposed hybrid method is the hybrid wrapper of Balancing Composite Motion optimization (BCMO) and Political Optimizer (PO), named as BCMPO approach. The BCMO approach properties are enhanced by PO approach. The proposed approach is processed based on uncertainties of electricity price and uncertainties of renewable distributed generations. The main purpose of this work is to estimate the operating cost of REHs in a stochastic environment. The proposed BCMPO approach simultaneously considers factors like uncertainty of renewable energy sources and uncertainty of electricity prices. The proposed approach implementation is done by the MATLAB/Simulink platform and performance of the proposed model is compared with other techniques. The proposed approach is analyzed based on the seasons like spring, summer, fall and winter.

Research on a new power distribution control strategy of hybrid energy storage system for hybrid electric vehicles based on the subtractive clustering and adaptive fuzzy neural network

Research on a new power distribution control strategy of hybrid energy storage system for hybrid electric vehicles based on the subtractive clustering and adaptive fuzzy neural network

Modelling and Simulation if Hybrid Electric Vehicle

Owing to the concerns about the environmental and energy issues, many research studies have been carried out to enhance the performance of the internal combustion engine vehicles (ICEVs) and launch new-generation vehicles [1]. HEVs (Hybrid Electric Vehicles) is a viable option for improved fuel economy and reduced emissions. HEV architecture are dependent on how much braking energy is regenerated, and how well the regenerated energy is utilized. In this project, we show how model based design can be applied in the development of hybrid electric vehicle system.

A hybrid technique based energy management in hybrid electric vehicle system

A hybrid technique based energy management in hybrid electric vehicle system

Deep Reinforcement Learning-Based Real-Time Joint Optimal Power Split for Battery–Ultracapacitor–Fuel Cell Hybrid Electric Vehicles

Hybrid energy storage systems for hybrid electric vehicles (HEVs) consisting of multiple complementary energy sources are becoming increasingly popular as they reduce the risk of running out of electricity and increase the overall lifetime of the battery. However, designing an efficient power split optimization algorithm for HEVs is a challenging task due to their complex structure. Thus, in this paper, we propose a model that jointly learns the optimal power split for a battery/ultracapacitor/fuel cell HEV. Concerning the mechanical system of the HEV, two propulsion machines with complementary operation characteristics are employed to achieve higher efficiency. Additionally, to train and evaluate the model, standard driving cycles and real driving cycles are employed as input to the mechanical system. Then, given the inputs, a temporal attention long short-term memory model predicts the next time step velocity, and through that velocity, the predicted load power and its corresponding optimal power split is computed by a soft actor–critic deep reinforcement learning model whose training phase is aided by shaped reward functions. In contrast to global optimization techniques, the local velocity and load power prediction without future knowledge of the driving cycle is a step toward real-time optimal energy management. The experimental results show that the proposed method is robust to different initial states of charge values, better allocates the power to the energy sources and thus better manages the state of charge of the battery and the ultracapacitor. Additionally, the use of two motors significantly increases the efficiency of the system, and the prediction step is shown to be a reliable way to plan the HESS power split in advance.

Research on thermal management system of next-generation hybrid electric vehicle

Abstract New energy vehicles play a bigger role under the National VI emission standard and the double-point policy conditions, which is the focus of Original Equipment Manufacturers (OEMs) and consumers due to the advantages of low fuel consumption and long battery life. The front cabin of a hybrid electric vehicle (PHEV) includes the cooling system of the engine, electric motors, controller and condenser, etc. Compared with fuel and pure electric vehicles, the front cabin has a higher heat load, smaller space, and a great influence of the radiators for PHEV, which has more requirements for the cooling module. As people’s requirements for power and comfort continue to increase, the cost and energy consumption of thermal management systems are also rising. Hence, all OEMs have shifted their focus to developing a new generation of thermal management systems. The performance of the new-generation hybrid vehicle thermal management system can meet the performance requirements of the entire vehicle, and it has the advantages of being suitable for platformization, reducing costs, comprehensive utilization of vehicle energy to reduce energy consumption, high radiating efficiency, small front-end module size, and low fan power.

Design and Analysis of Control Strategy for an Efficient Battery Thermal Management System of an Electric Vehicle

Lithium-ion batteries (LIB) are being widely used as energy storage systems for Electric and Hybrid Electric Vehicles. The LIBs are particularly sensitive to temperatures and uncontrolled heating may lead to thermal runaway hence, a battery thermal management system is required to prevent the thermal abuse of the batteries. An efficient battery thermal management system ensures that the battery is working in its optimal temperature range thus, increasing its cycle life and performance. In this study, the heat generation profiles have been calculated for various drive cycles namely, US06, FTP-75, and UDDS, which were validated by a thermo-electric mathematical model in MATLAB Simscape. It was found that a cooling plate with an S-channel design is the most suitable for heat exchange applications and a mass flow rate value of 0.4 kg/s gives an optimum compromise between the heat transfer and the back pressure developed. The control strategy proposed in this research work regulates the battery temperature within the specified limit keeping the parasitic power at a minimum.

Learning-based supervisory control of dual mode engine-based hybrid electric vehicle with reliance on multivariate trip information

The combination of electrification and advanced dedicated hybrid engines (DHEs) has been attached with promising prospects for the abatement of fuel consumption and emissions. To explore the potential of this solution, experimental data are used from a high-efficiency DHE operated under dual-mode combustion: the lean-burn spark-induced compression ignition (SICI) and the stoichiometric spark ignition (SI). This work endeavors to develop the integrated energy management and engine control system for such DHE-based plug-in hybrid electric vehicles (PHEVs), to this end, the synergy of artificial intelligent control and trip preview can be exploited to boost the control performance, with the engine combustion process being optimally controlled in transient. This paper presents a multistate deep reinforcement learning (M−DRL)-based energy management strategy (EMS) with a discrete–continuous hybrid action space to select optimal combustion mode while output continuous engine power demand, and its state space is expanded to incorporate the multivariate traffic and terrain information processed by a spatio-temporal data processing (STDP) framework in real time. Comprehensive experiments are carried out to validate the capability of the proposed method, based on a standard driving cycle and a real-world driving cycle in China with real-time traffic data recorded. The results indicate that the proposed M−DRL strategy avoids frequent switchover of engine operation modes as well as achieves more than 93% of the dynamic programming’s performance in terms of the fuel consumption and NOx emissions abatement.

Exergoeconomic assessment of the ejector-based battery thermal management system for electric and hybrid-electric vehicles

The battery is a critical component for operating electric and hybrid electric vehicles where temperature control is vital for safe and efficient operation. In this paper, the ejector-based battery thermal management system (BTMS) performance is compared to the basic BTMS from thermal and exergoeconomic perspectives. The ejector is added to mix the refrigerant streams from the cabin evaporator and battery chiller, thus delivering the mixture to the compressor with higher pressure to reduce compression power and improve system performance. The exergoeconomic study is carried out for different evaporator, chiller, and condenser temperatures and different battery thermal loads to illustrate the ejector-based system's technical and economic feasibility. Results show an optimal (minimal) total system cost rate of 0.9092 US$/h for the ejector-based system, compared to 1.0222 US$/h for the basic system (11.1% reduction). Based on the applied conditions, the total exergy destruction is found to be 1.596 kW for the ejector-based BTMS and 2.243 kW for the basic BTMS, representing a 28.8% reduction compared to the basic system.

A Novel Deep Learning-Based State-of-Charge Estimation for Renewable Energy Management System in Hybrid Electric Vehicles

In recent years, alternative engine technologies are necessary to resolve the problems related to conventional vehicles. Electric vehicles (EVs) and hybrid electric vehicles (HEVs) are effective solutions to decarbonize the transportation sector. It also becomes important to shift from traditional houses to smart houses and from classical vehicles to EVs or HEVs. It is needed to combine renewable energy sources (RESs) such as solar photovoltaics, wind energy systems, and various forms of bio-energies. Among various HEV technologies, an effective battery management system (BMS) still remains a crucial issue that is majorly used for indicating the battery state of charge (SOC). Since over-charging and over-discharging result in inevitable impairment to the batteries, accurate SOC estimation desires to be presented by the BMS. Although several SOC estimation techniques exist to regulate the SOC of the battery cell, it is needed to improvise the SOC estimation performance on HEVs. In this view, this paper focuses on the design of a novel deep learning (DL) with SOC estimation model for secure renewable energy management (DLSOC-REM) technique for HEVs. The presented model employs a hybrid convolution neural network and long short-term memory (HCNN-LSTM) model for the accurate estimation of SOC. In order to improve the SOC estimation outcomes of the HCNN-LSTM model, the barnacles mating optimizer (BMO) is applied for the hyperpower tuning process. The utilization of the HCNN-LSTM model makes the modeling process easier and offers a precise depiction of the input–output relationship of the battery model. The design of BMO based HCNN-LSTM model for SOC estimation shows the novelty of the work. An extensive experimental analysis highlighted the supremacy of the proposed model over other existing methods in terms of different aspects.

Processor-In-the-Loop Demonstration of MPC for HEVs Energy Management System

An Energy Management System for Hybrid Electric Vehicles is described based on a Model Predictive Control solution. This is implemented using a Processor-In-the-Loop simulation running on a GreenBox II development board from NXP Semiconductors. The hybrid vehicle considered contains an internal combustion engine and an electric motor in a parallel configuration. The MPC design involves a Linear Parameter-Varying model to approximate the nonlinear vehicle model and provide a simpler algorithm for implementation. The control policy was integrated into the GBII control board and assessed in simulation with the processor in the loop. The very promising performance of the proposed predictive controller in terms of mileage and battery degradation is compared with the well-established Equivalent Consumption Minimization Strategies.

IoT and artificial intelligence enabled state of charge estimation for battery management system in hybrid electric vehicles

IoT and artificial intelligence enabled state of charge estimation for battery management system in hybrid electric vehicles

IoT and artificial intelligence enabled state of charge estimation for battery management system in hybrid electric vehicles

IoT and artificial intelligence enabled state of charge estimation for battery management system in hybrid electric vehicles

Novel Thermal Management System for High Power Hybrid Electric Vehicles

Novel Thermal Management System for High Power Hybrid Electric Vehicles

A new partially isolated hybrid output of multiport multilevel converter for photovoltaic based power supplies

Multiport converters (MPCs), which can have multiple inputs and multiple output (MIMO) ports, have been suggested to integrate a variety of sources (including storage facilities) with diversified loads, thereby guaranteeing both an effective utilization of sources and the uninterruptible supply to loads. MPCs transfer the power between those sources and (active/passive) loads concurrently/independently. The attractive features of MPCs include minimal component count, higher power conversion efficiency, haste response, higher power density, and hence better sizing, combined control point, and bidirectional and soft switching abilities. MPCs are the perfect solution in applications where complex energy distribution prevails, such as renewable energy systems (RES), hybrid electric vehicle (HEV) systems, DC power distribution systems, etc. This paper proposes a hybrid output multiport multilevel converter (HOMPMLC), which is a unique, partially isolated breed of MPC. The HOMPMLC is the multiple input and dual output (MIDO) converter, which can deliver twosome outputs (both AC and DC) from several DC sources. In the HOMPMLC, a new multilevel inverter (MLI) topology, Single stage multilevel DC-link multilevel inverter (SSMLDCMLI), is involved to synthesize a high quality AC output, which utilizes five switches for the seven level improved quality AC output, and the DC output port is accomplished with the help of a forward power converter. The working of the HOMPMLC is studied for a solar photovoltaic system in the MATLAB®Simulink R2016a simulation platform as well as in an experimental investigation involving 4 panels (Schüco MPE-340-AL-01; each 340 Watts).

A Study of 1MHz Multisampling Deadbeat Control for Low Carrier Frequency PMSM Drive System

EVs (Electric Vehicles) and HEVs (Hybrid Electric Vehicles) have electric motor drive system, and its quick and smooth response is one of advantage in electric motor drive system. In order to utilize potential performance of control response, this paper introduces MSDB (Multi Sampling Deadbeat control) of PMSM (Permanent Magnet Synchronous Motor) drive system for EVs and HEVs using a FPGA (Field Programmable Gate Array). The proposed deadbeat control using a FPGA can realize good response and robustness, especially for electric motor drive system using low carrier frequency of PWM (Pulse Width Modulation) inverter like as EVs and HEVs.