Hybrid Electric Vehicles Operation Research Articles

Novel integration of dual evaporator loop heat pipe for improved electric vehicle battery thermal regulation

The Battery Thermal Management System (BTMS) plays a crucial role in ensuring the efficient and safe operation of Hybrid Electric Vehicles (HEVs) and/or Electric Vehicles (EVs). Among various heat transfer techniques, Loop Heat Pipes (LHPs) have demonstrated their potential for effective heat transfer from Li-ion batteries due to their high heat transfer rates over a longer distance. The conventional design of LHPs poses challenges for constructing robust battery pack modules, particularly in electric vehicles (EVs). This complexity arises from the separation of liquid and vapor lines at either the top or bottom of the evaporator, along with the inclusion of a compensation chamber. Furthermore, the evolving requirements of BTMS highlight the necessity for efficient heat removal from multiple locations. To address these requirements, a novel Dual Evaporator Loop Heat Pipe (DE-LHP) has been developed as a BTMS solution featuring separate vapor and two liquid lines positioned at the uppermost section of each cylindrical evaporator. The developed DE-LHP is tested as a BTMS solution with a 3S4P battery module (10Ah capacity) at different charging and discharging rates (1C, 1.5C, and 2C) in surrounding temperatures ranging from 30 °C to 45 °C. When the thermal performance of the DE-LHP-equipped BTMS was compared to a module without BTMS, a drop in maximum cell temperature was noted. At an ambient temperature of 35 °C, the DE-LHP BTMS achieved temperature reductions of 22.8 %, 22.6 %, and 18.8 % at charging rates of 1C, 1.5C, and 2C, respectively. In addition, at a threshold temperature of 60 °C, the module with DE-LHP BTMS demonstrated enhanced discharging capability from 41 % State of Charge (SOC) to 0 % SOC for a 1.5C discharging rate and from 59 % SOC to 0 % SOC for a 2C discharging rate compared to the module without BTMS. Furthermore, when the module was exposed to ambient temperatures ranging from 30 °C to 45 °C, the DE-LHP BTMS maintained a temperature difference across the battery module of less than 3.5 °C for all charging rates and 1C and 1.5C discharging rates.

Health-conscious energy management strategy for battery/fuel cell electric vehicles considering power sources dynamics

This paper presents a new multi-stage power management strategy aimed at enhancing the security and performance of hybrid electric vehicles (HEVs). The proposed strategy is designed to protect sensitive vehicle power sources, such as fuel cells (FCs), from potential damages caused by abrupt load variations. This is achieved through the implementation of a model-based coordinated switching strategy that utilizes transition functions that are tailored to the transient dynamics of power sources. The power flow in HEVs is managed using a fuzzy energy management approach that allows safe and predefined operation of FCs at multiple operating points. To ensure reliable and uninterrupted HEV operation, fault detection algorithms have been integrated into the second stage of the power management strategy to detect and correct any power source failures. Simulation results indicate that the proposed fuzzy energy management strategy provides efficient usage and precise control over FC power, while the coordinated switching strategy compensates for the sluggishness of FCs and reduces transient power ripples and fluctuations in the DC bus voltage during FC operating point variations. The efficiency of the proposed multi-stage power management strategy has been validated through simulations performed on the RT LAB platform and the obtained results were deemed satisfactory.

Multienergy Management Strategy of Fuel Cell Hybrid Vehicle Based on Distributed Parameter Model

To increase the fuel efficiency of fuel cells, lengthen their useful lives, and fulfil the demands for high energy and high power during the operation of hybrid electric vehicles. This paper's goal is to thoroughly examine the distributed parameter model-based multienergy management technique of fuel cell hybrid electric vehicles. Firstly, the simplified model of a hybrid system is constructed according to the distributed parameter model, and the fuel cell model, unidirectional DC/DC converter, and battery are described in detail. The multienergy management of hybrid electric vehicles based on improved deep Q-learning is adopted, the multienergy management strategy based on deep Q-learning is designed, so as to reduce fuel consumption and improve the working efficiency of fuel cells, optimize the energy distribution of lithium batteries and fuel cells, and adopt the experience playback mechanism of summation tree structure in the process of strategy training to complete the multienergy management of hybrid electric vehicles. The strategy described in this study can successfully increase the overall power performance of fuel cell hybrid vehicles, extend the battery's service life, and increase fuel economy, according to simulation results, which has a certain practical value.

Novel Approaches for Energy Management Strategies of Hybrid Electric Vehicles and Comparison with Conventional Solutions

Well-designed energy management strategies are essential for the good operation of Hybrid Electric Vehicles (HEVs) in terms of fuel economy and pollutant emissions reduction, regardless of the specific powertrain architecture. The goal of this paper is to propose two innovative supervisory control strategies for HEVs derived from different optimization algorithms and to assess HEVs’ fuel consumption reduction (compared to conventional vehicles). These approaches are derived from the literature and modified by the authors to present novel algorithms for the optimization problem. One is based on Dynamic Programming (DP), here referred to as the Forward Approach to Dynamic Programming (FADP) and introduces a different implementation of the DP to achieve computational and accuracy benefits. The other is based on the Equivalent Consumption Minimization Strategy (ECMS) approach, and it adapts to the latest driving conditions using information gathered in a finite-length backward-looking horizon. These techniques are used to achieve the optimal power share between the thermal engine and the battery of a parallel HEV. Their performances are compared and analysed in terms of achieved fuel economy and computational time with respect to conventional DP and Pontryagin’s Minimum Principle (PMP) approaches.

Co-Optimization of Velocity and Charge-Depletion for Plug-In Hybrid Electric Vehicles: Accounting for Acceleration and Jerk Constraints

Abstract Recent advances in vehicle connectivity and automation technologies promote advanced control algorithms that co-optimize the longitudinal dynamics and powertrain operation of hybrid electric vehicles. Typically, a sequential optimization with the vehicle dynamics optimized followed by powertrain optimization is adopted to manage a number of complexities such as the inherent mixed-integer nature of the hybrid powertrain, the numerous state and control variables, the differing time scales of vehicle and powertrain subsystems, time-varying state constraints, and large horizon lengths. Instead, we solve the offline optimization problem in a centralize manner assuming exact knowledge of the lead vehicle's position over the entire trip by applying a discrete-time single shooting-based numerical approach, Discrete Mixed-Integer Shooting (DMIS), including a linearly increasing computational complexity to the problem horizon. In particular, the hierarchical problem structure is exploited to decompose the computationally intensive Hamiltonian minimization step into a set of low-dimensional optimizations. DMIS allows us to compute the direct fuel minimization problem including the vehicle and powertrain dynamics in a centralized manner to its full horizon while systematically tuning weighting factors that penalize passenger discomfort. For the first time, this study reveals that practically implemented sequential optimization exhibits similar fuel optimality as co-optimization when a certain level of passenger comfort is required.

Online energy management for intermittent operation of hybrid electric vehicles at low temperatures

Online energy management for intermittent operation of hybrid electric vehicles at low temperatures

Energy demand assessment for long term operation of hybrid electric vehicles

This publication deals with the assessment of fuel demand for long-term operation of hybrid vehicles, because only with this kind of analysis it could be possible to clearly and unequivocally assess the actual progress in achieving the intended goals. The long-term test is the opposite of short-term tests - currently WLTP and RDE or other tests developed mostly by specialized magazines. Long-term car use means mileage measured in thousands of kilometers over a period of several years. The assessment of fuel consumption during long-term operation of HEVs from the presented point of view is new, in which the theory of demand for cumulated energy (as fuel) proved to be very useful. Mathematical models of fuel consumption along with an assessment of its intensity and a description of fuel economy constitute the vehicle’s “energy footprint”. Cumulative fuel consumption increases as mileage increases, but the increase is not a linear function. The discrepancy between fuel consumption in a group of vehicles of the same make and type is significant and increases as mileage increases. The average fuel consumption of the analyzed HEVs during their long-term operation significantly exceeds what is determined by the short tests, e.g. WLTP (and earlier NEDC). The actual fuel consumption is much higher than the one from the short tests.

Hybrid-electric passenger car energy utilization and emissions: Relationships for real-world driving conditions that account for road grade

Past research showed on-road emissions patterns unique to hybrid electric vehicles (HEVs), indicating the need to account for them in emissions models as projected HEV sales increase over the coming decades. This work defines and characterizes a variable that quantifies HEV operating behavior to inform future development of new HEV emissions models based on current knowledge of conventional vehicle (CV) emissions patterns. Instantaneous hybridization factor (IHF), was quantified using on-road data collected from a 2010 Toyota Camry HEV. IHF is the ratio of electric system power to total system power and accounts for energy storage in the high voltage battery (IHF ranges from −1 to +1). Relationships between IHF and vehicle specific power (VSP), road type and road grade were examined. Negative VSP resulted in regenerative braking operation (IHF = −0.01 to −1) 90% of the time. IHF identified the VSP range where HEV operation was highly variable (VSP = −1 to 8 kW/ton) when driving at speeds below the ICE-off threshold (42 mph). VSP and IHF together account for 76–86% of the variability in HEV CO2 emissions in this study. CO2 model results using VSP computed with the measured real-world road grade (R2 = 0.86) gave improved fits over the no-grade VSP model (R2 = 0.69). This study establishes one framework for calculating the instantaneous HEV power split, confirms the need to include road grade in VSP for accurate modeling of vehicle emissions, and identified the need for significant improvements in on-board diagnostic (OBD) scantool measurement requirements for HEVs in three areas: (1) temporal resolution (sub-second to capture transient events such as ICE restarts); (2) simultaneous data logging capability for multiple controller area networks (i.e., engine and HEV parameters together); and (3) improved data precision.

Optimised adaptive control methodology for mode transition of hybrid electric vehicle based on the dynamic characteristics analysis

Mode transition plays an important role in the operation of hybrid electric vehicles. The clutch is a key actuator for mode transition, but friction resulting from its use makes it difficult to achieve seamless mode transition. Moreover, as is usual in an electromechanical coupling system, the hybrid powertrain may exhibit complex dynamic behaviours as operating parameters change during mode transition. This paper considered changes in the clutch friction coefficient, and established a mathematical model of the hybrid powertrain in a parallel-series hybrid electric vehicle (PSHEV) during mode transition. The Routh-Hurwitz criterion and bifurcation theory are introduced to analyse the influence of speed difference on the dynamic characteristics of the hybrid powertrain, and the corresponding instability threshold were given. Furthermore, an optimised adaptive control methodology was proposed to control the dynamic behaviours, and tests on PSHEV were carried out. The results show that the variations of speed difference may lead to the occurrence of Fold and Hopf bifurcations, and that once the speed difference reaches the instability threshold, it may cause the system to enter a chaotic state and exhibit random oscillation. The proposed control methodology can effectively expand the security domain and suppress the oscillation to improve smoothness of mode transition.

The development and numerical verification of a compromised real time optimal control algorithm for hybrid electric vehicle

Hybrid electric vehicles (HEV) have been recognized as a viable technology to significantly improve the fuel economy of on-road vehicles operated in urban areas featuring frequent stop-and-go operation. The optimization of the HEV control strategy is a dynamic optimal problem involving a nonlinear cost function and several nonlinear constraints. To take advantage of the global optimality of dynamic programming (DP) and the real-time capability of the equivalent consumption minimization strategy (ECMS) in optimizing the HEV operation strategy, a model that combines ECMS with DP is proposed in this paper, which is defined as ECMSwDP. Using this approach, the optimal equivalent factor λ for ECMS is derived using DP under various driving conditions and the different grades of synthetic driving cycle. The fuel economy simulated using the ECMSwDP control strategy is comparable to the optimal solution derived using DP. The potential of the real-time control strategy developed using ECMSwDP to improve fuel economy is demonstrated using a transit hybrid electric bus as an example.

Research on energy optimization control strategy of the hybrid electric vehicle based on Pontryagin's minimum principle

An energy optimization control strategy for the Hybrid Electric Vehicle (HEV) is developed in this paper to reduce the fuel consumption, on the premise of the operation demand, which makes the engine and motor working in the high efficiency operation area. The energy management model of Pontryagin's Minimum Principle (PMP) for the parallel hybrid vehicle is set up, the state of charge (SOC) and the output power of the battery are chosen as the state variable and control variable, respectively. The total fuel consumption during the HEV operation is considered as the objective function, and PMP is used to obtain the optimum solution. The simulation results verify the effectiveness of the developed control strategy, with which the higher output torque of the engine and the lower output torque and higher efficiency of the motor are achieved compared with the energy management strategy based on logic rule. Moreover, the 100-kilometer fuel consumption is reduced by 40%, and the fuel economy is greatly improved.

Review on alternative propulsion in automotives -hybrid vehicles

The dynamic diminishing in overall oil stores and proximity of stringent outflows runs the world over, have made a desperate prerequisite for the making of automobiles with upgraded effectiveness. This is the change time frame to move with elective powertrains as an Electric driven, hybrid, fuel cell models are being produced. Energy Management System (EMS) are given significance for capacity and improving the effectiveness of machines. The operation of Hybrid Electric Vehicles (HEVs) in different landscape with their fuel utilization is accounted. Hybrid powertrain like series, parallel and mixed are clarified. Testing undertaking is the appropriation of charging station situation for India and compelling use of hybrid vehicles. Battery management is the key part in HEV which require search for various methodologies are taken into for creating. The correlation of the customary motors finished with hybrid vehicles.

Experimental analysis of Dynamic Charge Acceptance test conditions for lead-acid and lithium iron phosphate cells

This paper presents the results of a series of tests to determine the Dynamic Charge Acceptance (DCA) performance of small form-factor carbon-enhanced VRLA cells designed for use in Hybrid Electric Vehicle (HEV) applications, together with standard lead-acid and lithium iron phosphate (LFP) cells. The results demonstrate how varying the conditions and parameters of the standard DCA test regime can provide a superior evaluation of DCA performance and lead to a better understanding of cell behaviour under real-world conditions. A modified test procedure is proposed, based on the DCA Short Test profile (EN50342-6). Results are presented for a batch of carbon-enhanced cells, tested at various temperatures, rest periods and States of Charge (SoC) for the cell. These conditions having been chosen to mimic a range of real-life scenarios which could potentially be encountered during HEV operation. The resulting analysis demonstrates clear variations and trends in DCA performance which may be used to inform conditions for future testing regimes. The modified test procedure is then applied to standard lead-acid and LFP 26650-type cells and the results compared.

A Multiobjective Optimization Framework for Online Stochastic Optimal Control in Hybrid Electric Vehicles

The increasing urgency to extract additional efficiency from hybrid propulsion systems has led to the development of advanced power management control algorithms. In this paper, we address the problem of online optimization of the supervisory power management control in parallel hybrid electric vehicles (HEVs). We model HEV operation as a controlled Markov chain and show that the control policy yielding the Pareto optimal solution minimizes online the long-run expected average cost per unit time criterion. The effectiveness of the proposed solution is validated through simulation and compared with the solution derived with dynamic programming using the average cost criterion. Both solutions achieved the same cumulative fuel consumption demonstrating that the online Pareto control policy is an optimal control policy.

Characterization of Real-World Particle Number Emissions during Reignition Events from a 2010 Light-Duty Hybrid Electric Vehicle

Despite the increasing popularity of hybrid electric vehicles (HEVs), few studies have compared the real-world particle emissions of HEVs from internal combustion engine (ICE) reignition events with that of a conventional vehicle (CV) during real-world driving. Reignition events occur under unstable combustion conditions and frequently result in particle number (PN) emission rates (PNERs) that exceed those for stabilized engine operation. Tailpipe PNERs from a CV and an HEV 2010 Toyota Camry were quantified on a 32-mi route over rural, urban, and freeway roadways in Chittenden County, Vermont, with the total onboard tailpipe emissions measurement system. This study directly compared the CV and HEV PNERs and characterized the operation of the HEV in a new HEV ICE operating mode framework. Mean PNER for reignition events (7.19 pM 11.8 × 1010 particles/s) were on average four times greater than for stabilized HEV operation (1.79 ± 3.99 × 1010 particles/s). Under urban, rural, and freeway driving, HEV reignition event operation accounted for 58.7%, 44.6%, and 5.0%, respectively, of the total PN inventory. Mean HEV PNER was 1.8 times greater than that of the CV in urban driving, while under freeway driving, where the two vehicles operated similarly, average CV PNER was 2.4 times greater than that of the HEV. The data show that the typical fuel consumption benefits of HEVs in urban driving are associated with a trade-off in PN emissions. The HEV ICE operating behavior has implications for the spatial distribution of PN hot spots as well as the associated microscale modeling of alternative vehicle technology emissions.

Cell state-of-charge inconsistency estimation for LiFePO4 battery pack in hybrid electric vehicles using mean-difference model

Identification of cell SOC (state-of-charge) inconsistency for LiFePO4 battery packs is challenging due to the demanding conditions in hybrid electric vehicles (HEVs) and the relatively flat SOC–OCV (open circuit voltage) curve of LiFePO4 cells compared to others. We experimentally investigate cell voltages in a small battery pack and propose a Mean-plus-Difference Model (M+D Model). The M+D Model uses a cell mean model (CMM) representing the overall performance of the pack in high frequency. Meanwhile cell voltage differences (CVDs) between cells and the “mean cell” are studied by a cell difference model (CDM) in low frequency. A CDM considering SOC and internal resistance differences is subsequently presented and OCV differences are estimated. We further propose an SOC strategy to accurately identify cell SOC inconsistency by intermittently lowering pack SOC to 30% during HEV operation. Finally we discover that SOC differences can be determined with estimated OCV differences using SOC-difference/OCV-difference curve. The proposed method is verified by simulation and experiment. With the proposed method, LiFePO4 cell SOC inconsistency can be precisely estimated with existing measuring technology during HEV operating and cell equalization can be ultimately implemented.

One Electric Motor System for Steering Hydraulic Pump and Braking Air Pump in HEV BuS

The pure electric vehicles and hybrid electric vehicles are being developed and used wildly to save energy and reduce air pollution. In pure electric vehicles and hybrid electric vehicles, most of the steering hydraulic pump and braking sir pump have one electric motor to drive its pump. A new system with one electric motor for these two bumps was developed for application in pure electric vehicles（EVs） and hybrid electric vehicle ( HEVs). Comparison with conventional configuration, this new method can reduce cost and mounting space with compact size and higher energy efficiency. And the test results show that the new system can meet the requirements of HEV operation quite well.

Different architectures and modes of operation of HEV based on permanent magnet-electric variable transmission with rule-based and fuzzy logic global control strategy

In this paper, permanent magnet–dual mechanical ports (PM–DMP) machine is used as an electrical variable transmission (EVT), instead of the Toyota hybrid system (THS) transmission, in hybrid electric vehicle (HEV). In this paper, a new tactic to strategise the power management and torque distribution in the studied HEV system is developed. This strategy is based on the combination of rule–based and fuzzy logic control (RB–FLC). According to this strategy, different architectures with all modes of operation of the series/parallel HEV based on PM–EVT have been achieved. Using the vehicle specifications, engine, battery, and other control parameters of the Prius HEV, the appealing features of PM–EVT unit and the proposed RB–FLC power management strategy are explored in comparison to the SPHEV obtained from ADVISOR 2002. The obtained results give a specific conclusion that the Prius SPHEV can be successfully replaced by the HEV based on the PM–EVT with the proposed control strategy.

Position Error Compensation via a Variable Reluctance Sensor Applied to a Hybrid Vehicle Electric Machine

In the automotive industry, electromagnetic variable reluctance (VR) sensors have been extensively used to measure engine position and speed through a toothed wheel mounted on the crankshaft. In this work, an application that already uses the VR sensing unit for engine and/or transmission has been chosen to infer, this time, the indirect position of the electric machine in a parallel Hybrid Electric Vehicle (HEV) system. A VR sensor has been chosen to correct the position of the electric machine, mainly because it may still become critical in the operation of HEVs to avoid possible vehicle failures during the start-up and on-the-road, especially when the machine is used with an internal combustion engine. The proposed method uses Chi-square test and is adaptive in a sense that it derives the compensation factors during the shaft operation and updates them in a timely fashion.

Sliding mode controller for internal combustion engine for energy-efficient operation of hybrid electric vehicles

This paper presents a new concept with different control strategies. The developed intelligent speed controller utilises a gear box which includes a gear train with two inputs, one coupled with the engine shaft and another with the shaft of a variable speed controlled motor. The net output speed is a combination of the two input speeds and is governed by the transmission ratio of the planetary gear train. This new approach eliminates the use of a torque converter, which reduces the power loss that occurs in the box during fluid coupling. By gradually varying the speed of the induction motor a stepless transmission is achieved. The developed controller also scores in climbing a step gradient and reversing the vehicle, by intelligent mixing of the induction motor and engine speeds. This approach eliminates the traditional braking system in the entire vehicle design. The use of two power sources, IC engine and battery-driven induction motor, utilises the modern idea of hybrid vehicles. The new speed controller is capable of driving a vehicle even in an extreme case of IC engine failure, during gas depletion and for plausibility errors.