Plug-in Hybrid Electric Vehicle Model Research Articles

An Offline Closed-Form Optimal Predictive Power Management Strategy for Plug-In Hybrid Electric Vehicles

This article develops an optimal predictive power management strategy (OP-PMS) for plug-in hybrid electric vehicles (PHEVs) to manage the remaining battery level throughout a defined journey. Apart from using the battery before charging it again at the destination, this approach also supports transit through an Ultra Low Emission Zone (ULEZ), where a PHEV needs to operate in pure electric mode to avoid emissions. This type of PHEV power management strategy (PMS) design problem is a noncausal control problem, where the power demand of the remaining driving cycle and the final battery energy level targets influence the current power management action. Rather than solving the whole optimization problem online, this article presents a simplification that leads to a closed-form analytic solution with parameters that can be computed offline. This article makes three key contributions. First, a novel input-linearization method is developed, which converts the OP-PMS into a convex quadratic programming (QP) problem that captures the essential model nonlinearities using an input transformation and a quadratic cost function. Second, the influence of future power demands and final targets on current energy management policy is explicitly quantified from a dynamic optimal control perspective. Third, the noncausal convex QP is approximated with a closed-form analytic solution that is calculated offline. This leads to a real-time implementable OP-PMS with coefficients that can be precalculated and stored in the engine control unit. To demonstrate the viability of this approach, numerical examples are provided based on a generic PHEV model to verify the efficacy of the proposed OP-PMS.

Comprehensive CO2 emissions analysis and optimization of plug-in hybrid electric vehicles

Fuel and grid have different life-cycle carbon emission intensities, which makes plug-in hybrid electric vehicles (PHEVs) have great low-carbon potential. To reduce carbon emissions during PHEV driving, the energy allocation of PHEV based on carbon intensity is studied. The energy life cycle carbon emissions factors are collected and energy management strategies (EMS) are developed. A power-split PHEV model is established to analyze carbon dioxide (CO2) emissions. Carbon emissions factors calculate the carbon emissions of each power flow. Simulation research is carried out using Tianjin and Yunnan’s energy structure as an example. The equivalent CO2 emission minimum strategy (ECEMS) is established by optimizing the hybrid mode in the Rule-based strategy. Based on ECEMS, the improved particle swarm optimization (PSO) algorithm is used to optimize the rules of hybrid mode switching. As a comparison, the same method but a different goal is used to optimize energy consumption. The results show that the lowest energy consumption does not mean the lowest carbon emission. CO2 emissions in different scenarios are reduced by 7.1%–31.7%, but the energy consumption is increased.

Cooperative energy management and eco-driving of plug-in hybrid electric vehicle via multi-agent reinforcement learning

The advanced cruise control system has expanded the energy-saving potential of the hybrid electric vehicle (HEV). Despite this, most energy-saving researches for HEV either only optimize the energy management strategy (EMS) or integrate eco-driving through a hierarchically optimized assumption that optimizes EMS and eco-driving separately. Such kinds of approaches may lead to sub-optimal results. To fill this gap, we design a multi-agent reinforcement learning (MARL) based optimal energy-saving strategy for HEV, achieving a cooperative control on the powertrain and car-following behaviors to minimize the energy consumption and keep a safe following distance simultaneously. Specifically, a plug-in HEV model is regarded as the research object in this paper. Firstly, the HEV energy management problem in the car-following scenario is decomposed into a multi-agent cooperative task into two subtasks, each of which can conduct interactive learning through cooperative optimization. Secondly, the energy-saving strategy is designed, called the independent soft actor–critic, which consists of a car-following agent and an energy management agent. Finally, the performance of velocity tracking and energy-saving are validated under different driving cycles. In comparison to the state-of-the-art hierarchical model predictive control (MPC) strategy, the proposed MARL method can reduce fuel consumption by 15.8% while ensuring safety and comfort.

Machine Learning-Based Vehicle Model Construction and Validation—Toward Optimal Control Strategy Development for Plug-In Hybrid Electric Vehicles

Advances in machine learning inspire novel solutions for the validation of complex vehicle models and spur an easy manner to promote energy management performance of complexly configured vehicles, such as plug-in hybrid electric vehicles (PHEVs). A constructed PHEV model, based on the four-wheel-drive passenger vehicle configuration, is validated through an efficient virtual test controller (VTC) developed in this article. The VTC is designed via a novel approach based on the least-square support vector machine and random forest with the inner-interim data filtered by the ReliefF algorithm to validate the vehicle model as necessary. This article discusses the process and highlights the accuracy improvements of the PHEV model that is achieved by implementing the VTC. The validity of the VTC is addressed by examining the PHEV model to mimic the characteristics of internal combustion engine, motor, and generator behaviors observed through the benchmark test. Sufficient simulations and hardware-in-loop test are employed to demonstrate the capability of the novel VTC-based model validation method in practical applications. The major novelty of this article lies in the development of a VTC, by which the vehicle model can be efficiently developed, providing solid framework and enormous convenience for control strategy design.

An Optimal Control Strategy for Plug-In Hybrid Electric Vehicles Based on Enhanced Model Predictive Control With Efficient Numerical Method

Advances in machine learning inspire novel solutions for the validation of complex vehicle models and spur an easy manner to promote energy management performance of complexly configured vehicles, such as plug-in hybrid electric vehicles (PHEVs). A constructed PHEV model, based on the four-wheel-drive passenger vehicle configuration, is validated through an efficient virtual test controller (VTC) developed in this article. The VTC is designed via a novel approach based on the least-squares support vector machine and random forest with the inner-interim data filtered by the ReliefF algorithm to validate the vehicle model as necessary. This article discusses the process and highlights the accuracy improvements of the PHEV model that is achieved by implementing the VTC. The validity of the VTC is addressed by examining the PHEV model to mimic the characteristics of the internal combustion engine, motor, and generator behaviors observed through the benchmark test. Sufficient simulations and hardware-in-the-loop tests are employed to demonstrate the capability of the novel VTC-based model validation method in practical applications. The major novelty of this article lies in the development of a VTC, by which the vehicle model can be efficiently developed, providing a solid framework and enormous convenience for control strategy design.

Risk Analysis of Voltage Violation With PHEV Inter-Area Mobile Charging Strategy Under Gas Station Networks Attacked

To reduce the risk of voltage violation after gas station networks (GSNs) are attacked, this study investigates an inter-area mobile charging strategy of plug-in hybrid electric vehicles (PHEVs) to decrease the charging load by taking full advantage of charging resources. First, considering the location of the charging station, the waiting time, and the charging fee, an inter-area mobile charging strategy of PHEVs is proposed, and a mobile charging model of PHEVs among regions is established to relieve the charging pressure. Second, the risk index is developed to analyze the risk of voltage violation in terms of the results of probabilistic load flow (PLF). Finally, the proposed strategy is tested on a modified coastal active power distribution network, and simulation results show that the charging load of PHEVs is dispersed among regions and the risk of voltage over-limit can be reduced.

Extended Horizon ECMS Control of PHEVs With 2D Electricity Price Adaptation Policy

This article proposes new improvements to the ECMS-based control of plug-in hybrid electric vehicles (PHEVs) using macro-level trip forecast information. First, the dynamic programming (DP) optimization is applied to a power-split PHEV model to obtain the optimal powertrain trajectories for benchmarking the energy economy of the proposed controllers. An instantaneous energy cost minimization process is then formulated to compute the optimal mix of fuel and electricity, subject to the state and input constraints. To improve the speed and accuracy of the energy management system, the Nelder-Mead simplex search algorithm is used for the instantaneous optimizations. To account for the battery capacity limitation, an adaptive electricity pricing policy is introduced to control battery discharge rate based on the remaining trip distance, average speed, and elevation forecasts. The parameters of the proposed policy are optimized for a group of 5 urban and highway drive cycles to obtain a robust controller which can serve a wide spectrum of driving patterns. To further improve the energy economy, an extended-horizon optimization algorithm is proposed to account for control input transitions between consecutive time steps. Finally, a revised speed forecast formula is implemented during the low-SOC intervals to lift battery SOC ahead of high-demand periods. The combination of the proposed schemes enables reaching over 99% average optimality compared to a finely discretized DP optimization for the adopted drive cycles, and above 98% optimality for a test PHEV drive cycle.

Investigation of impacts of plug-in hybrid electric vehicles’ stochastic characteristics modeling on smart grid reliability under different charging scenarios

Although it is essential to investigate the impacts of the analytical modeling of plug-in hybrid electric vehicles (PHEVs) for reliability and adequacy evaluation of smart grids, this issue has received less attention. This article tries to develop a new analytical reliability and adequacy model of smart grids, considering the precise model of PHEVs. Until now, no solution has been reported to determine the appropriate number of PHEVs’ states in the analytical reliability evaluation methods. In this paper, a novel framework is developed to determine the suitable number of PHEVs’ states that simultaneously guarantees the speed and accuracy of the reliability calculations. The Monte Carlo simulation (MCS) is used to validate the proposed reliability evaluation method of smart grids using the developed analytical model of PHEVs. In addition, the impacts of different stochastic parameters on the adequacy evaluation of PHEVs, such as distance driven, departure time, and arrival time are studied by performing a variety of sensitivity analyses under different charging scenarios. The proposed method is applied to an actual electric distribution network of Kashan, which is located in the Isfahan province distribution company. The comparative test results illustrate the advantages of the introduced analytical reliability evaluation framework. One point that claims attention is the importance of distance driven’s impact and its state reduction in comparison to the impact of arrival time or departure time. Test results imply that reliability calculations of smart grids could be accelerated significantly by investigating the suitable number of discretized states of PHEVs’ characteristics.

Phase unbalance and PAR constrained optimal active and reactive power scheduling of Virtual Power Plants (VPPs)

In near future, the integration of unknown and unpredictable quantity of Electric Vehicles (EVs) can violate reliable and quality power service of distribution system. In spite of that, ability of simultaneously regulate active and reactive power by Plug-in-Hybrid Electric Vehicles (PHEVs) in quick response time, without affecting the batteries can help to ensure reliable and quality power service in distribution system. This paper investigates the optimal active and reactive power scheduling of PHEVs and Distributed Generations (DGs) in Virtual Power Plants (VPPs), considering unbalance and Peak-to-Average Ratio (PAR) constraints. To compute stochastic model of PHEVs, we considers dynamic nature of driving pattern based on NHTS 2017 data. The proposed approach is implemented on IEEE-25 bus unbalanced distribution system. The developed planning and operational investigation of VPPs also presents dependencies of cost and losses in terms of Unbalance factor and PAR.

Mean Field Optimal Energy Management of Plug-In Hybrid Electric Vehicles

This paper addresses the problem of energy consumption management of a large population of plug-in hybrid electric vehicles (PHEVs). A PHEV can be derived either on its gasoline or the battery modes while traveling, and can be either charged from or discharged to the grid while parking at home, respectively. This feature is the main novel consideration in our system model, besides taking into account the constraints like traveling time and distance, and the bounds on the charging power and state of charge of the battery. On the basis of the proposed model of PHEV, the optimal battery-gasoline energy management strategy is obtained in order to minimize the cost of energy and battery degradation for each user. Such a strategy can also compensate the peak load. Since the strategy of each PHEV affects other users’ cost through the electricity price, we model such an interaction as a game problem and we prove that there exist a unique Nash equilibrium point of the game. Then, due to the fact that the effect of a single PHEV of a large population on the other users’ costs is negligible, the theory of mean filed (MF) game is utilized to develop a decentralized control strategy for PHEVs. The lightweight information exchange of the proposed MF algorithm ensures a low computational cost and communication overhead. The proof of convergence for the proposed algorithm is given and it is shown that as the number of PHEVs goes to infinity the MF optimal strategy of the players converges to the unique Nash equilibrium point of the game. In simulation results, the effectiveness of the proposed method in terms of demand side management, peak shaving, convergence rate, and robustness is studied.

Effect of an electric vehicle mode in a plug-in hybrid electric vehicle with a post-transmission electric motor

The additional design consideration of a plug-in hybrid electric vehicle (PHEV) caused by its recharging capability requires an optimal hybrid powertrain control strategy to fully exploit the vehicle's capability. Amongst many aspects of control algorithm development and design optimisation, the advantage of including a pure electric vehicle (EV) mode in the vehicle driving modes of a PHEV with a post-transmission electric motor was studied in this research. For this purpose, architecture of a series-parallel PHEV was determined and its simulation model was developed. Then, various simulations were conducted to evaluate technical merits of the pure EV mode in terms of fuel economy and emissions. The simulation results favourably supports inclusion of the pure EV mode as one of the driving modes in the powertrain control strategy of the studied PHEV model with a post-transmission electric motor.

Integration of Plug-in Hybrid Electric Vehicles into Residential Distribution Grid Based on Two-Layer Intelligent Optimization

This paper presents a methodology for modeling the load demand of plug-in hybrid electric vehicles (PHEVs). Due to the stochastic nature of vehicle arrival time, departure time and daily mileage, probabilistic methods are chosen to model the driving pattern. However, these three elements of driving pattern are correlated with each other, which makes the probability density functions (pdfs)-based probabilistic methods inaccurate. Here a fuzzy logic based stochastic model is built to study the relationship between the three elements of driving pattern. Moreover, a load profile modeling framework (LPMF) for PHEVs is proposed to synthesize both the characteristics of driving pattern and vehicle parameters into a load profile prediction system. Based on this stochastic model of PHEV, a two-layer evolution strategy particle swarm optimization (ESPSO) algorithm is proposed to integrate PHEVs into a residential distribution grid. A novel business model is developed for PHEVs to provide ancillary service and participate in peak load shaving. A virtual time-of-use rate is used to reflect the load deviation of the system. Then, an objective function is developed to aggregate the peak load shaving, power quality improvement, charging cost, battery degradation cost and frequency regulation earnings into one cost function. The ESPSO approach can benefit the system in four major aspects by: 1) improving the power quality; 2) reducing the peak load; 3) providing frequency regulation service; and 4) minimizing the total virtual cost. Finally, simulations are carried out based on different control strategies and the results have demonstrated the effectiveness of the proposed algorithm.

Intelligent power management of plug-in hybrid electric vehicles, part I: real-time optimum SOC trajectory builder

Offering better fuel economy and lower emissions than conventional vehicles, plug–in hybrid electric vehicles (PHEVs) are promising near–term options for high efficiency, 'sustainable' transportation. It has recently been found that these efficiency benefits can be further improved with access to upcoming trip and driving conditions. This study is organised into two parts: in part I, upcoming trip data is used to find the optimal SOC trajectory of our PHEV model that will help minimise the total cost of electricity and fossil fuel. In part II, the optimum SOC trajectory is applied within the real–time controller to optimally distribute propulsion power between two energy sources. Autonomie was used to develop and implement a high fidelity PHEV model. The optimal SOC trajectory which has been found by real–time optimisation technique is in close agreement with the global optimum solution of Dynamic Programming. Moreover, the real–time technique is much less computationally expensive.

Investigating the Impacts of Plug-in Hybrid Electric Vehicles on Power Distribution Systems

Despite the economic and environmental advantages of plug-in hybrid electric vehicles (PHEVs), the increased utilization of PHEVs brings up new concerns for power distribution system decision makers. Impacts of PHEVs on distribution networks, although have been proven to be noticeable, have not been thoroughly investigated for future years. In this paper, a comprehensive model is proposed to study the PHEV impacts on residential distribution systems. In so doing, PHEV fundamental characteristics, i.e., PHEV battery capacity, PHEV state of charge (SOC), and PHEV energy consumption in daily trips, are accurately modeled. As some of these effective characteristics depend on vehicle owner's behavior, their behavior and interests are considered in the proposed model. Also, to get a more practical model of PHEVs, the number of vehicles in a residential distribution network, the PHEV penetration level for upcoming years, distribution of PHEVs in the network, and estimation of household load growth for upcoming years are extracted from related published reports. The proposed model is applied to the IEEE 34-node test feeder, and PHEV impacts on residential distribution network are studied in different time horizons. A sensitivity analysis is also performed to demonstrate the effects of PHEV operation modes on the network load profile.

A combinatorial artificial intelligence real-time solution to the unit commitment problem incorporating V2G

Plug-in hybrid electric vehicles (PHEVs) have been the center of attention in recent years as they can be utilized to set up a bidirectional connection to a power grid for ancillary services procurement. By incorporating Vehicle to Grid (V2G), this paper proposes a real-time solution to a non-convex constrained unit commitment (UC) optimization problem considering V2G parking lots as dispersed generation units. V2G parking lots can be considered as virtual power plants that my decrease dependency to small expensive units in a UC problem. In this paper, firstly a probabilistic attendance model of PHEVs in a parking lot is investigated, while expected number of PHEVs as well as the equivalent generation capacity of the parking lot is obtained using a radial basis neural network. Secondly, a particular UC problem considering V2G parking lot is solved using GA-ANN as a hybrid heuristic method. A real-time estimation of PHEVs number in the V2G parking lot and real-time solution to UC–V2G problem associated with load variation makes this work distinguished, while the proposed method is applied to a standard IEEE 10-unit test system with promising results.

Impact Analysis of Electric Vehicles Charging/Discharging

The penetration of a large number of plug-in hybrid electric vehicles (PHEVs) has introduced severe challenges to power grid operation. In this paper, the grid-connected PHEV model is first proposed to reflect the charge/discharge property of PHEV. Then, a simulation approach is developed to study the probability distributions of the aggregate load and available vehicle to grid (V2G). capacity of PHEVs. Simulation results show that the charging and discharging capacities have the characteristic of normal distribution．

Optimizing and Diversifying the Electric Range of Plug-in Hybrid Electric Vehicles for U.S. Drivers

To provide useful information for automakers to design successful plug-in hybrid electric vehicle (PHEV) products and for energy and environmental analysts to understand the social impact of PHEVs, this paper addresses the question of how many of the U.S. consumers, if buying a PHEV, would prefer what electric ranges. The Market-oriented Optimal Range for PHEV (MOR-PHEV) model is developed to optimize the PHEV electric range for each of 36,664 sampled individuals representing U.S. new vehicle drivers. The optimization objective is the minimization of the sum of costs on battery, gasoline, electricity and refueling hassle. Assuming no battery subsidy, the empirical results suggest that: 1) the optimal PHEV electric range approximates two thirds of one s typical daily driving distance in the near term, defined as $450/kWh battery delivered price and $4/gallon gasoline price. 2) PHEVs are not ready to directly compete with HEVs at today s situation, defined by the $600/kWh battery delivered price and the $3-$4/gallon gasoline price, but can do so in the near term. 3) PHEV10s will be favored by the market over longer-range PHEVs in the near term, but longer-range PHEVs can dominate the PHEV market if gasoline prices reach as high as $5-$6 per gallonmore » and/or battery delivered prices reach as low as $150-$300/kWh. 4) PHEVs can become much more attractive against HEVs in the near term if the electric range can be extended by only 10% with multiple charges per day, possible with improved charging infrastructure or adapted charging behavior. 5) the impact of a $100/kWh decrease in battery delivered prices on the competiveness of PHEVs against HEVs can be offset by about $1.25/gallon decrease in gasoline prices, or about 7/kWh increase in electricity prices. This also means that the impact of a $1/gallon decrease in gasoline prices can be offset by about 5/kWh decrease in electricity prices.« less

Forward power-train energy management modeling for assessing benefits of integrating predictive traffic data into plug-in-hybrid electric vehicles

In this paper, a forward power-train plug-in hybrid electric vehicle model with an energy management system and a cycle optimization algorithm is evaluated for energy efficiency. Using wirelessly communicated predictive traffic data for vehicles in a roadway network, as envisioned in intelligent transportation systems, traffic prediction cycles are optimized using a cycle optimization strategy. This resulted in a 56–86% fuel efficiency improvements for conventional vehicles. When combined with the plug-in hybrid electric vehicle power management system, about 115% energy efficiency improvements were achieved. Further improvements in the overall energy efficiency of the network were achieved with increased penetration rates of the intelligent transportation assisted enabled plug-in hybrid electric vehicles.

An energy optimization strategy for power-split drivetrain plug-in hybrid electric vehicles

To demonstrate the greater capabilities and benefits achievable with a plug-in hybrid electric vehicle (PHEV), an energy optimization strategy for a power-split drivetrain PHEV, which utilizes a predicted speed profile, is presented. In addition, the paper reports an analysis and evaluation of issues related to real time control implementation for the modeled PHEV system, which include the optimization window sizes and the impact of prediction errors on the energy optimization strategy performance. The optimization time window sizes were identified and validated for different driving cycles under different operating modes and total length of travel. With the identified optimization windows size, improvements in fuel consumption were realized; the highest improvement was for Urban Dynamometer Driving Schedule (UDDS), with a range of improvement of 14–31%, followed by a 1–15% range of improvement for Highway Fuel Economy Driving Schedule (known as HWFET) and a 1–8% range of improvement for US06 (also known as Supplemental Federal Test Procedure). While no correlation was observed between the error rate and the rate of increased fuel consumption, this PHEV system still yielded energy savings with errors in the speed prediction, which is an indication of robustness of this PHEV model.

On Conversion of Hybrid Electric Vehicles to Plug-In

The retrofit conversion of currently available hybrid electric vehicles (HEVs) to plug-in HEVs (PHEVs) is studied in this paper through experiments and simulations using the powertrain system analysis toolkit (PSAT). First, a rule-based fuzzy controller of the battery energy-management unit is developed to simulate different energy-management policies. Second, by modifying the energy-control strategy, the model of the conversion PHEV (C-PHEV) is verified with experiments. Finally, the C-PHEV model is used to simulate different battery energy-management control strategies. The results show improvement in fuel economy, whereas the energy-management controller discharges the power through the plug-in battery pack only when the state of charge of the base vehicle battery is close to its minimum value. This method keeps the advantage of driving in electric mode using a combination of two batteries and optimizing the use of regenerative braking capabilities, which is the main advantage of HEVs. It is also found that increasing the power threshold of the internal combustion engine (ICE) improves the performance of C-PHEV. Increasing the ICE power threshold increases the engine efficiency by running the engine in its efficient points. It also drives the vehicle in electric mode in higher power demands.