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Energy management strategies play a crucial role in enhancing the fuel efficiency of hybrid electric vehicles (HEVs) and mitigating greenhouse gas emissions. For the current commonly used time horizon optimization methods that only target the trend curve of the optimal battery state of charge (SOC) trajectory obtained offline, which are only suitable for buses with known future driving conditions, this paper proposed an energy management strategy based on an adaptive network-based fuzzy inference system (ANFIS) that optimizes the time horizon length and enhances adaptability to driving conditions by integrating historical vehicle velocity, accelerations, and battery SOC trajectory. First, the vehicle velocity prediction model based on the radial basis function (RBF) neural network is used to predict the future velocity sequence. After that, ANFIS was used to optimize and update the length of the forecast time horizon based on the historical vehicle velocity sequence. Finally, compared with the fixed time horizon energy management strategy, which is based on model predictive control (MPC), the average calculation time of the energy management strategy is reduced by about 23.5%, and the fuel consumption per 100 km is reduced by about 6.12%. [ABSTRACT FROM AUTHOR]

Combination hybrid electric vehicle (HEV) mainly includes two configurations: series-parallel and power-split. It is necessary to consider a variety of metrics to comprehensively evaluate the configuration performance of HEV and compare the two configurations. In order to fully evaluate the HEVs' potential in energy management, a practical and effective multi-objective method that can solve the global optimization-based energy management problem is needed. Based on the idea of dynamic programming (DP) and non-dominated sorting method, this paper proposes a global multi-objective optimization method of non-dominated sorting dynamic programming (NSDP) with multi-control variables. This algorithm can calculate a set of uniformly distributed Pareto solutions for the conflicting or coupling optimization objectives, and the performance of the solution set is improved due to the increase in the dimension of the control variables, which increases the strategy search space. NSDP is applied to two different configurations to fully evaluate the performance of fuel consumption and battery lifespan. The parameters of the configurations are optimized and comprehensively compared based on the implementation of NSDP. The above process can provide theoretical analysis for hybrid power system developers. [ABSTRACT FROM AUTHOR]

Copyright of Automotive Digest is the property of Automotive Digest Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the development of recent artificial intelligence technology, especially after the great success of AlphaGo, there has been a growing interest in applying reinforcement learning (RL) to solve energy management strategy (EMS) problems for hybrid electric vehicles. However, the issues of current RL algorithms including deployment inefficiency, safety constraint, and simulation-to-real gap make it inapplicable to many industrial EMS tasks. With these in mind and considering the fact that there exists many suboptimal EMS controllers which can generate plentiful amounts of interactive data containing informative behaviors, an offline RL training framework that tries to extract policies with the maximum possible utility out of the available offline data is proposed. Furthermore, with connected vehicle technology standard in many new cars, rather than bringing all the data to the storage and analytics, a scheduled training framework is put forward. This cloud-based approach not only alleviates the computational burden of edge devices, but also more importantly provides a deployment-efficient solution to EMS tasks that have to adapt to changes of driving cycle. To evaluate the effectiveness of the proposed algorithm on real controllers, a hardware-in-the-loop (HIL) test is performed and the superiority of the proposed algorithm in contrast to dynamic programming, behavior cloning, rule-based, and vanilla off-policy RL algorithms is given. [ABSTRACT FROM AUTHOR]

This paper investigates an adaptive dynamic programming (ADP)-based energy management control strategy for a series-parallel hybrid electric vehicle (HEV). This strategy can further minimize the equivalent fuel consumption while satisfying the battery level constraints and vehicle power demand. Dual heuristic dynamic programming (DHP) is one of the basic structures of ADP, combining reinforcement learning, dynamic programming (DP) optimization principle, and neural network approximation function, which has higher accuracy with a slightly more complex structure. In this regard, the DHP energy management strategy (EMS) is designed by the backpropagation neural network (BPNN) as an Action network and two Critic networks approximating the control policy and the gradient of value function concerning the state variable. By comparing with the existing results such as HDP-based and rule-based control strategies, the equivalent consumption minimum strategy (ECMS), and reinforcement learning (RL)-based strategy, simulation results verify the robustness of fuel economy and the adaptability of the power-split optimization of the proposed EMS to different driving conditions. [ABSTRACT FROM AUTHOR]

Vehicles have become an integral part of the modern era, but unfortunately conventional vehicles consume non-renewable energy resources which have associated issue of air pollution. In addition to that, global warming and the shortage of fossil fuels have provided motivation to look for alternative to conventional vehicles. In the recent era, hybrid electric vehicle (HEV) is becoming popular due to their attractive features of energy saving and less air pollution. This paper aimed at designing a hybrid electric vehicle control scheme with an effective energy management system under varying load conditions. The proposed hybrid energy storage system of the HEV in this work consists of two energy sources: (1) main source: fuel cell and (2) auxiliary source: ultra-capacitor and battery. Furthermore, a fuzzy logic-based nonlinear controller has been developed to effectively control the management of energy sources according to load demand. The proposed HEV model has been developed using MATLAB/Simulink environment and the simulation results verify that the proposed fuzzy logic-based controller provides better voltage regulation and efficient energy management for the complete drive range of the HEV. Moreover, a comparative study of the proposed controller with existing control methods affirms its better reference tracking with minimum overshoot, faster convergence, better stability, and enhanced regulation of DC bus bar voltages under varying load conditions. [ABSTRACT FROM AUTHOR]

Recent developments in emissions regulations are pushing Non-Road Mobile Machineries manufacturers towards the adoption of more efficient solutions to reduce the amount of pollutants per unit of work performed. Electrification can be a reasonable alternative to traditional powertrain to achieve this goal. The higher complexity of working machines architectures requires, now more than ever, better design and testing methodologies to better integrate electric systems into mechanical and hydraulic layouts. In this work, the attention focused on the use of a Hardware in the Loop (HIL) approach to test performance of an energy management strategy (called load observer) developed specifically for an orchard tractor starting from field characterization. The HIL bench was designed to replicate a scaled architecture of a parallel hybrid electric tractor at mechanical and electrical level. The vehicle behavior was simulated with a personal computer connected on the CAN BUS network designed for the HIL system. Several tasks were simulated starting from data gathered during field measurements of a daily use of the machine. Results showed good performance in terms of load split between the two power sources and stability of the speed control although the variability of the applied load.

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents predictive energy management of hybrid electric vehicles (HEVs) via computationally efficient multi-layer control. First, we formulate an optimization problem by considering driveability and a penalty for using service brakes in the objective function to optimize gear, engine on/off, engine clutch state, and power-split decisions subject to constraints on the battery state of charge (SOC) and charge sustenance. Then, we split it into two control layers, including a supervisory control in a higher layer and a local power-split control in a lower layer. In the supervisory layer, a gear and powertrain mode manager (PM) is designed, and optimal gear, engine on/off and clutch states are obtained by using a combination of dynamic programming (DP) and Pontryagin's minimum principle (PMP). Moreover, a real-time iteration Secant method is proposed to calculate optimal battery costate such that the constraint on charge sustenance is satisfied. In the local controller layer, a linear quadratic tracking method (LQT) is used to optimally split power between the engine and the electric machine and keep battery SOC within its bounds. [ABSTRACT FROM AUTHOR]

Hybrid and electric vehicle (HEV/EV) technology is reasonably mature at this time, with a few million vehicles around in the world, and there is a significant amount of literature in the public domain on this subject. However, there is not enough literature on the application of this technology for off-road vehicles, including construction equipment, other industrial utility vehicles, and nonautomotive applications, such as a locomotive, ships, or airborne vehicles. With this in mind, the author presents here the topic and its current status. In addition, the author discusses the issue related to the decision-making process before the above technology is introduced for any HEV/EV application so that one is assured that the technology will bring benefit if applied for a particular purpose. [ABSTRACT FROM AUTHOR]

Analytical optimal solution to the energy management of hybrid electric vehicles is of interest from theoretical and practical perspectives. Particularly, effort has been made to derive analytical solution to the energy management problem for series hybrid electric vehicles using Pontryagin's minimum principle (PMP). However, admissibility of the system input was not fully explored in determining the optimal input candidates. In this paper, the analytical solution for the same problem is found by partitioning the positive power demand set into four subsets, where the solution is derived for each case separately according to the corresponding admissible input set. The analytical solution is verified through comparison with numerical solution for a series hybrid electric wheel loader, and two different drive cycles are considered for this purpose. From the proposed solution, effective equivalence factor bounds are found and used to construct an adaptive equivalent consumption minimization strategy. The proposed strategy and the analytical solution are implemented together for the same vehicle to demonstrate their effectiveness in dealing with real-world applications. Simulations are performed for 12 drive cycles, and the results are compared to the ones achieved by PMP-based optimal control where the optimization is done numerically. Simulation results suggest that the proposed methodology is relatively fast and has satisfactory performance in presence of drive cycle uncertainty. It is observed that the proposed method fulfills charge sustenance, and the achieved fuel consumption figures are very close to the optimal benchmarks found by the non-causal method. [ABSTRACT FROM AUTHOR]

A Dynamic Programming (DP) formulation is developed to find the global optimal solution to the energy management of a parallel Plug-in Hybrid Electric Vehicle (PHEV) equipped with a Dual-Clutch Transmission (DCT). The effects of integrating in the DP formulation the losses accounting for gearshifts and engine starts are studied in terms of the overall fuel consumption; the optimal control solutions obtained depends on the occurrence of these transient events. These sources of dissipation are modeled through physical considerations thus enabling the DP algorithm to decide when it is more convenient, in terms of minimizing the total energy consumption, to perform either a gearshift or an engine start. This capability differentiates the DP formulation here presented from those presented in previous studies. [ABSTRACT FROM AUTHOR]

The energy management strategy (EMS) or power management strategy (PMS) unit is the core of power sharing control in the hybridization of automotive drivetrains in hybrid electric vehicles (HEVs). Once a new topology and its corresponding EMS are virtually designed, they require undertaking different stages of experimental verifications toward guaranteeing their real-world applicability. The present paper focuses on a new and less-extensively studied topology of such vehicles, HEVs equipped with an electrical variable transmission (EVT) and assessed the controllability validation through hardware-in-the-loop (HiL) implementations versus model-in-the-loop (MiL) simulations. To this end, first, the corresponding modeling of the vehicle components in the presence of optimized control strategies were performed to obtain the MiL simulation results. Subsequently, an innovative versatile HiL test bench including real prototyped components of the topology was introduced and the corresponding experimental implementations were performed. The results obtained from the MiL and HiL examinations were analyzed and statistically compared for a full input driving cycle. The verification results indicate robust and accurate actuation of the components using the applied EMSs under real-time test conditions. [ABSTRACT FROM AUTHOR]

Dynamic programming (DP) is currently the reference optimal energy management approach for hybrid electric vehicles (HEVs). However, several research concerns arise regarding the effective application of DP for optimal HEV control problems which involve a significant number of control variables, state variables and optimization constraints. This paper deals with an optimal control problem for a full parallel P2 HEV with constraints on battery state-of-charge (SOC), battery lifetime in terms of state-of-health (SOH), and smooth driving in terms of the frequencies of internal combustion engine (ICE) activations and gear shifts over time. The DP formulation for the considered HEV control problem is outlined, yet its practical application is demonstrated as unfeasible due to a lack of computational power and memory in current desktop computers. To overcome this drawback, a computationally efficient version of DP is proposed which is named Slope-weighted Rapid Dynamic Programming (SRDP). Computational advantage is achieved by SRDP in considering only the most efficient HEV powertrain operating points rather than the full set of control variable values at each time instant of the drive cycle. A benchmark study simulating various drive cycles demonstrates that the introduced SRDP can achieve compliance with imposed control constraints on battery SOC, battery SOH and smooth driving. At the same time, SRDP can achieve up to 78% computational time saving compared with a baseline DP approach considering the Worldwide Harmonized Light Vehicle Test Procedure (WLTP). On the other hand, the increase in the fuel consumption estimated by SRDP is limited within 3.3% compared with the baseline DP approach if the US06 Supplemental Federal Test Procedure is considered. SRDP could thus be exploited to efficiently explore the large design space associated to HEV powertrains.

Energy management is an enabling technology for increasing the economy of fuel cell/battery hybrid electric vehicles. Existing efforts mostly focus on optimization of a certain control objective (e.g., hydrogen consumption), without sufficiently considering the implications for on-board power sources degradation. To address this deficiency, this article proposes a cost-optimal, predictive energy management strategy, with an explicit consciousness of degradation of both fuel cell and battery systems. Specifically, we contribute two main points to the relevant literature, with the purpose of distinguishing our study from existing ones. First, a model predictive control framework, for the first time, is established to minimize the total running cost of a fuel cell/battery hybrid electric bus, inclusive of hydrogen cost and costs caused by fuel cell and battery degradation. The efficacy of this framework is evaluated, accounting for various sizes of prediction horizon and prediction uncertainties. Second, the effects of driving and pricing scenarios on the optimized vehicular economy are explored. [ABSTRACT FROM AUTHOR]

A novel rule-based control strategy is proposed for the energy management of parallel hybrid electric vehicles (HEVs): the torque-leveling threshold-changing strategy (TTS). In contrast to the most commonly used heuristic electric assist control strategy (EACS) that is designed based on the load following approach, the TTS proposes and applies the new fundamental concept of torque leveling. This mechanism operates the engine with a constant torque when the engine is active, thus ensuring the engine works at an efficient operating point. The TTS additionally extends and uses a design concept that has previously been proposed in the context of series HEVs, the threshold-changing mechanism, to operate the HEV in a charge-sustaining manner. By exploiting this new set of design principles for parallel HEVs, the TTS realizes energy source control sharing behavior that is reminiscent to optimization-based methods. To show its effectiveness, the TTS is implemented to a through-the-road (TTR) HEV and benchmarked against two conventional control strategies: dynamic programming (DP) and the EACS. The results show that the TTS, despite its simplicity, is able to deliver comparable fuel economy as the global optimization approach DP and thus achieve significant improvement compared to the EACS. In addition, to facilitate real-time application, a simplified version of the TTS (STTS) is also developed, which is able to deliver similar performance as the TTS but is more simple to implement in practice. [ABSTRACT FROM AUTHOR]

This paper investigates the utilization of two low-voltage hybrid mechanisms on fuel economy improvement of a vehicle with a downsized engine. The first mild hybrid system, configured with a motor, a planetary gear set, and a supercharger, permits switching between parallel hybrid operation and boosting but not both. This constrained flexibility offers additional degrees of freedom, yet it requires a highly optimized decision of which functionality, torque assist, or boost assist, in addition to how much energy to be used by each. The second mild hybrid system also supports supercharging and torque assist but with two separate motors, hence is more flexible. Both systems enable the use of a downsized engine as a competitive counterpart to widely used turbocharged and full hybrid electric powertrains. The optimal control problem for energy management of both configurations on a vehicle with an automatic transmission is formulated and solved using dynamic programming (DP) over standard drive cycles. The results indicate around 25% fuel consumption improvement compared to a baseline turbo-charged engine over the FTP75 cycle, which is around 80% of the full hybridization benefit. The supercharging versus torque assist share and its sensitivity to the drive cycle, battery size, and compressor efficiency, which influence the availability of electric energy and the losses of electrical and mechanical paths, is also investigated. Few causally implementable rules for selecting the single motor system operating mode are found by analysis of the DP solution, and their effectiveness is shown by incorporating into the DP formulation and comparing the resulting fuel consumption. [ABSTRACT FROM AUTHOR]

Abstract Considering the overwhelming pressure on worldwide demand of fossil fuels and the climate change caused by air pollution, hybrid electric vehicles have seen a promising future thanks to the development of renewable energy sources. Among various kinds of energy sources that have been used in hybrid electric vehicles, lithium-ion battery and proton exchange membrane (PEM) fuel cell exist to be the most favorable ones owing to their high energy density and power density. However, the degradation issues of the energy sources tend to be neglected when designing the energy management strategies for the hybrid electric vehicles. Concerning existing literature, degradation modelling methods of lithium-ion batteries and PEM fuel cells are reviewed and the possibility of integrating them into health-conscious energy management is discussed. Besides, a variety of energy management strategies that have taken the influence of degradations into consideration are reviewed and classified. The contribution of this paper is to investigate the possibility of developing a health-conscious energy management strategy based on accurate estimation of degradation to improve the durability of the system. Highlights • Degradation modelling and estimation methods for lithium-ion batteries and PEM fuel cells are summarized and compared. • A review of the progress made for involving health management into energy management strategies is provided. • The challenges and unresolved issues for developing health-conscious EMSs are discussed. [ABSTRACT FROM AUTHOR]

Improving fuel economy and lowering emissions are key societal goals. Standard driving cycles, pre-designed by the US Environmental Protection Agency (EPA), have long been used to estimate vehicle fuel economy in laboratory-controlled conditions. They have also been used to test and tune different energy management strategies for hybrid electric vehicles (HEVs). This paper aims to estimate fuel consumption for a conventional vehicle and a HEV using personalized driving cycles extracted from real-world data to study the effects of different driving styles and vehicle types on fuel consumption when compared to the estimates based on standard driving cycles. To do this, we extracted driving cycles for conventional vehicles and HEVs from a large-scale U.S. survey that contains real-world GPS-based driving records. Next, the driving cycles were assigned to one of three categories: volatile, normal, or calm. Then, the driving cycles were used along with a driver-vehicle simulation that captures driver decisions (vehicle speed during a trip), powertrain, and vehicle dynamics to estimate fuel consumption for conventional vehicles and HEVs with power-split powertrain. To further optimize fuel consumption for HEVs, the Equivalent Consumption Minimization Strategy (ECMS) is applied. The results show that depending on the driving style and the driving scenario, conventional vehicle fuel consumption can vary widely compared with standard EPA driving cycles. Specifically, conventional vehicle fuel consumption was 13% lower in calm urban driving, but almost 34% higher for volatile highway driving compared with standard EPA driving cycles. Interestingly, when a driving cycle is predicted based on the application of case-based reasoning and used to tune the power distribution in a hybrid electric vehicle, its fuel consumption can be reduced by up to 12% in urban driving. Implications and limitations of the findings are discussed. [ABSTRACT FROM AUTHOR]

Improving fuel economy and lowering emissions are key societal goals. Standard driving cycles, pre-designed by the US Environmental Protection Agency (EPA), have long been used to estimate vehicle fuel economy in laboratory-controlled conditions. They have also been used to test and tune different energy management strategies for hybrid electric vehicles (HEVs). This paper aims to estimate fuel consumption for a conventional vehicle and a HEV using personalized driving cycles extracted from real-world data to study the effects of different driving styles and vehicle types on fuel consumption when compared to the estimates based on standard driving cycles. To do this, we extracted driving cycles for conventional vehicles and HEVs from a large-scale U.S. survey that contains real-world GPS-based driving records. Next, the driving cycles were assigned to one of three categories: volatile, normal, or calm. Then, the driving cycles were used along with a driver-vehicle simulation that captures driver decisions (vehicle speed during a trip), powertrain, and vehicle dynamics to estimate fuel consumption for conventional vehicles and HEVs with power-split powertrain. To further optimize fuel consumption for HEVs, the Equivalent Consumption Minimization Strategy (ECMS) is applied. The results show that depending on the driving style and the driving scenario, conventional vehicle fuel consumption can vary widely compared with standard EPA driving cycles. Specifically, conventional vehicle fuel consumption was 13% lower in calm urban driving, but almost 34% higher for volatile highway driving compared with standard EPA driving cycles. Interestingly, when a driving cycle is predicted based on the application of case-based reasoning and used to tune the power distribution in a hybrid electric vehicle, its fuel consumption can be reduced by up to 12% in urban driving. Implications and limitations of the findings are discussed. [ABSTRACT FROM AUTHOR]

Summary: To achieve best fuel economy control for a parallel hybrid electric vehicle, optimized control based on dynamic programming (DP) is proposed. The main effort in this paper includes the following. First, the global optimal control mathematic model of the best fuel economy is constructed and its corresponding recursive equation is established. Second, 2 additional augmented cost functions are proposed to prevent frequent gear shifting and frequent engine stopping and starting. Third, an algorithm of restricting the exploring region is proposed to overcome the curse of dimensionality of the numerical DP, which can greatly reduce the computational complexity. Both this DP method and a simple threshold control in corporation of a standby optimization are applied to a vehicle manufactured by the Changan company on the Federal Test Program driving cycle. The results show that the proposed method is effective in reducing fuel consumption by 15%. Although the proposed DP method is implemented on a priori known driving cycle, it can provide a criterion to design and evaluate a control strategy to achieve better fuel consumption. [ABSTRACT FROM AUTHOR]

Motivated by the fact that the real driving NOx emissions (RDE) of conventional diesel vehicles can exceed the legislation norms by far, a concept for the control of RDE with a diesel parallel hybrid electric vehicle (HEV) is proposed. By extending the well-known equivalent consumption minimization strategy (ECMS), the power split degree of freedom is used to control the NOx emissions and the battery state of charge (SOC) simultaneously. Through an appropriate formulation of the problem, the feedback control is shown to be separable into two dependent PI controllers. By hardware-in-the-loop (HIL) experiments, as well as by simulations, the proposed method is shown to minimize the fuel consumption while tracking a given reference trajectory for both the NOx emissions and the battery SOC.

In this paper, the calculation of eco-driving cycles for a Hybrid Electric Vehicle (HEV), using Dynamic Programming (DP), is investigated from the complexity-solving method viewpoint. The study is based on a comparative analysis of four optimal control problems formulated using distinct levels of modeling. Starting with three state dynamics (vehicle position and speed, battery state-of-charge) and three control variables (engine and electric machine torque, gear-box ratio), the number of state variables is reduced to two in a first simplification. The other two simplifications are based on decoupling the optimization of the control variables into two steps: an eco-driving cycle is calculated assuming that the vehicle is propelled only by the engine. Then, knowing that the vehicle follows the eco-driving cycle calculated in the first step, an off-line energy management strategy (torque split) for an HEV is calculated to split the requested power at the wheels between the electric source and the engine. As is shown, the decreased complexity and the decoupling optimization lead to a sub-optimality in fuel economy while the computation time is noticeably reduced. Quantitative results are provided to assess these observations. [ABSTRACT FROM AUTHOR]

This paper presents a predictive energy management strategy for a parallel hybrid electric vehicle (HEV) based on velocity prediction and reinforcement learning (RL). The design procedure starts with modeling the parallel HEV as a systematic control-oriented model and defining a cost function. Fuzzy encoding and nearest neighbor approaches are proposed to achieve velocity prediction, and a finite-state Markov chain is exploited to learn transition probabilities of power demand. To determine the optimal control behaviors and power distribution between two energy sources, a novel RL-based energy management strategy is introduced. For comparison purposes, the two velocity prediction processes are examined by RL using the same realistic driving cycle. The look-ahead energy management strategy is contrasted with shortsighted and dynamic programming based counterparts, and further validated by hardware-in-the-loop test. The results demonstrate that the RL-optimized control is able to significantly reduce fuel consumption and computational time. [ABSTRACT FROM PUBLISHER]

This paper aims at proposing an efficient and versatile application of Petri nets (PNs) either alone without global positioning system (GPS) as in (GPS-free) system or together with the navigation system (GPS-registered) to conveniently provide a proper energy management strategy for hybrid electric vehicles (HEVs) of high hybridization level and serial architecture. A comparison between the PN strategy and two fuzzy logic strategies is performed in terms of fuel consumption and convergence time. In this paper, short and long trip types of 30 km mainly urban and 240 km mostly highway are considered with an initial state of charge (SoC) of 50% and different daily driving cycles or various standard the New York City Cycle, the New European Driving Cycle, US06 driving cycles. Both kinds of battery management strategies, GPS-free and GPS registered, are demonstrated and compared through simulation studies using the MTCsim software. Dealing with both types of trips, the simulation results significantly illustrate the superiority of the novel GPS-registered methodology’s efficiency toward improving the HEV’s energy management and reducing its fuel consumption besides the relative economic feasibility and structural simplicity features. Over one week duration, the GPS allows reaching the desired final SoC with acceptable errors and reducing the fuel consumption for both daily short and weekend long trips. The originality of this paper is proposing a hybrid GPS/rule-based approach to reduce the fuel consumption during daily driving trips that present about half of the professional travels in 2008 according to the French Sustainable Development Division. This novel strategy is developed on the basis of the recorded GPS data from past trips and the batteries’ final recharging capacities. [ABSTRACT FROM AUTHOR]

Supervisory control systems (SCSs) are used to manage the powertrain of hybrid electric vehicles (HEVs). This brief presents a novel SCS called exclusive operation strategy (XOS) that applies simple rules based on the idea that batteries are efficient at lower loads while engines and generators are efficient at higher loads. The XOS is developed based on insights gained from three conventional SCSs for series HEVs: 1) thermostat control strategy (TCS); 2) power follower control strategy (PFCS); and 3) global equivalent consumption minimization strategy. Also, recent technological developments have been considered to make the XOS more suited to modern HEVs than conventional SCSs. The resulting control decisions are shown to emulate the operation of approximate global optimal solutions and thus achieve significant improvement in fuel economy compared with TCS and PFCS. In addition, the generally linear relationship between required power and engine power for the XOS provides auditory cues to the driver that are comparable to conventional vehicles, thus reducing barriers to adopting HEVs. The simplicity and the effectiveness of the XOS make it a practical SCS. [ABSTRACT FROM AUTHOR]

This paper considers a parallel hybrid electric truck where the tractive force delivered to the wheels can be provided by a conventional internal combustion engine (ICE) and/or a motor generator (MG). This hybrid truck is equipped with a clutch system that is capable of decoupling the ICE and the MG from the drivetrain to improve the fuel reduction performance, compared with a hybrid truck without a separate clutch for the MG. In the considered hybrid truck, the energy management strategy utilizes the battery to reduce the fuel consumption and the associated emissions. As the battery life is limited, the vehicle owner will suffer from extra costs for battery replacement. Henceforth, it is necessary to guarantee sufficient battery life. This paper develops a quasi-static battery cycle-life model and formulates a model-based integrated energy management (IEM) strategy. This IEM strategy optimizes the power split (between the ICE and the MG) and the operation of the clutch system to minimize the hybrid truck fuel consumption while guaranteeing the requested battery life. The problem of fuel minimization with battery life guarantee is analytically solved using optimal control theory. Mathematical and physical arguments are used to explain the compromise between fuel reduction and battery life preservation. Simulation results demonstrate that the IEM strategy will guarantee the requested battery life for different driving cycles while allowing appropriate hybrid powertrain operations for fuel minimization. Moreover, the results also show the fuel reduction improvement from decoupling the MG from the drivetrain using the clutch system. [ABSTRACT FROM PUBLISHER]

The energy management strategy (EMS) of a hybrid electric vehicle is a supervisory control meant to steer power flows among the different energy sources of the powertrain. The EMS is designed for the purpose of reaching the maximum potential of a given powertrain architecture. Fuel consumption is not the only criterion to be considered for evaluating the global performance of an EMS, since the EMS also impacts other features such as drivability, through engine starts/stops and gear shifts. This paper proposes a general approach to include drivability constraints in the EMS of powertrains provided with discrete automated transmissions. As a first step, determinist dynamic programming is used to compute the optimal solution of the resulting optimization problem. This optimal solution is then used to assess the performance of the proposed online strategy, which is based on the equivalent consumption minimization strategy. Finally, simulation results are completed by a discussion of experimental results, as the proposed online strategy has been implemented in a prototype vehicle designed at Renault and tested on a dynamometric bench. [ABSTRACT FROM AUTHOR]

In order to improve the driving safety and fuel economy of hybrid electric vehicles (HEVs) in car-following scenarios, this paper proposes a hierarchical energy management strategy (EMS) based on adaptive dynamic programming (ADP). In the upper-layer speed planning, the speed prediction model of the preceding vehicle is established utilizing a back-propagation neural network (BPNN), and the host vehicle speed is planned based on heuristic dynamic programming (HDP) real-time through the predicted preceding vehicle speed and the current state of the host vehicle. In the lower-layer energy management control, the EMS based on dual heuristic dynamic programming (DHP) is used to realize the power allocation of each power source according to the planned speed to reduce fuel consumption. In both HDP and DHP algorithms, BPNN is used to implement the critic network (CN) and action network (AN) to obtain a more accurate control variable and faster convergence speed. By comparing the results with various existing EMSs in the different driving cycles, the simulation results show that the proposed EMS ensures real-time planning speed and good following performance while reducing energy consumption. • A hierarchical ADP-based EMS is proposed for HEVs to ensure safety and fuel economy. • The layer HDP plans the host vehicle acceleration based on the PV speed prediction. • The DHP achieves the power allocation optimization of EMS based on the HDP planning. • BPNN constructs the action-critic network of the ADP for fast weights convergence. [ABSTRACT FROM AUTHOR]

Faced with environmental issues caused by fossil fuel burning in the industrial and transportation sectors, innovations towards cleaner solutions to replace the ever diminishing fossil fuels have been the focus of not only researchers but governments all around the world. The hybrid electric vehicle (HEV) technology is the result of the desire to have vehicles with a better fuel economy and lower tailpipe emissions to meet the requirements of environmental policies as well as to absorb the impact of rising fuel prices. The objectives are met by combining a conventional internal combustion engine (ICE) with one or more electric motors powered by a battery pack that can be charged using an on-board generator and the regenerative braking technology to power the transmission. The challenge is to develop an efficient energy management strategy (EMS) to satisfy the objectives while not having a reduced vehicle performance. In this paper, EMSs that are proposed and developed in the recent years are revisited and reviewed. Additionally, the Plug-in HEV is discussed in a new perspective from the EMS point of view. The through-the-road (TtR) HEV with in-wheel motors (IWM) is a fairly new concept in the HEV design that features less complicated configuration with reduced hardware requirements and lower cost. Recent research findings are evaluated throughout this paper leading to a hypothetical TtR HEV materialization. A thorough discussion is made encompassing the advantages and disadvantages of the concept, its performance compared to conventional HEVs and the way forward. [ABSTRACT FROM AUTHOR]

The energy efficiency of parallel hybrid electric vehicles (HEVs) can degrade significantly when the battery state-of-charge (SoC) reaches its boundaries. The road grade has a great influence on the HEV battery charging and discharging processes, and therefore the HEV energy management can be benefited from the road grade preview. In real-world driving, the road grade ahead can be considered as a random variable because the future route is not always available to the HEV controller. This brief proposes a stochastic model predictive control-based energy management strategy using the vehicle location, traveling direction, and terrain information of the area for HEVs running in hilly regions with light traffic. The strategy does not require a determined route being known in advance. The road grade is modeled as a Markov chain and stochastic HEV fuel consumption and battery SoC models are developed. The HEV energy management problem is formulated as a finite-horizon Markov decision process and solved using stochastic dynamic programming. The proposed method is evaluated in simulation and compared with an equivalent consumption minimization strategy and the dynamic programming results. It is shown that the developed method can help maintaining the battery SoC within its boundaries and achieve good energy consumption performance. [ABSTRACT FROM AUTHOR]

The performance and practicality of predictive energy management in hybrid electric vehicles (HEVs) are highly dependent on the forecast of future vehicular velocities, both in terms of accuracy and computational efficiency. In this brief, we provide a comprehensive comparative analysis of three velocity prediction strategies, applied within a model predictive control framework. The prediction process is performed over each receding horizon, and the predicted velocities are utilized for fuel economy optimization of a power-split HEV. We assume that no telemetry or on-board sensor information is available for the controller, and the actual future driving profile is completely unknown. Basic principles of exponentially varying, stochastic Markov chain, and neural network-based velocity prediction approaches are described. Their sensitivity to tuning parameters is analyzed, and the prediction precision, computational cost, and resultant vehicular fuel economy are compared. [ABSTRACT FROM PUBLISHER]

Motivated by the fact that the real driving NOx emissions (RDE) of conventional diesel vehicles can exceed the legislation norms by far, a concept for the control of RDE with a diesel parallel hybrid electric vehicle (HEV) is proposed. By extending the well-known equivalent consumption minimization strategy (ECMS), the power split degree of freedom is used to control the NOx emissions and the battery state of charge (SOC) simultaneously. Through an appropriate formulation of the problem, the feedback control is shown to be separable into two dependent PI controllers. By hardware-in-the-loop (HIL) experiments, as well as by simulations, the proposed method is shown to minimize the fuel consumption while tracking a given reference trajectory for both the NOx emissions and the battery SOC. [ABSTRACT FROM AUTHOR]

• EMS discriminating between discrete and continuous actions is proposed for HEVs. • A battery aging model reflecting the nonlinear decline of battery SOH is established. • 99.5% performance of DP is achieved by this parameterized DQN in the simulation. • The real-time capacity of the proposed method is validated by the HIL experiment. Energy management strategy (EMS) is an essential technique to ensure the long- term driving economy of hybrid electric vehicles (HEVs). The complicated discrete–continuous hybrid action space lying in HEV's driving system presents a challenge to achieve high-performance EMSs. Thus, this paper proposes a novel improved deep Q-network (DQN)-based EMS to reduce the HEV's driving costs, with lithium-ion battery (LIB) life and energy economy considered. Firstly, a data-driven battery life map reflecting the non-linear decaying trajectory of battery state of health (SOH) is proposed to quantify the real-time battery aging. Secondly, in the proposed EMS incorporating the battery aging model, an enhanced parameterized DQN (PDQN) algorithm is applied to particularly provide a hybrid solution discriminating between discrete and continuous actions. Finally, with the dynamic programming (DP) method employed as the benchmark, the effectiveness and optimality of the proposed EMS are validated. Without the prior knowledge of testing driving conditions, the proposed EMS effectively achieves 99.5% performance of the DP method, reducing the vehicle's driving costs by 3.1% and extending battery life effectively. The EMS converges quickly during training and a hardware-in-loop test validates its real application potential. [ABSTRACT FROM AUTHOR]

Abstract: In this paper‎, ‎a formulation is developed for sizing of a Hybrid Energy Storage System (HESS) in different applications‎. ‎Here‎, ‎the HESS is a combination of Lithium battery and Ultra-Capacitor (UC)‎, ‎which is useful for many high energy and high power applications such as Hybrid Electric Vehicles (HEVs) and renewable energy‎. ‎The sizing formulas are based on initial cost and 10-years battery replacement cost which is arranged as an optimization problem‎. ‎For battery replacement cost‎, ‎the Lithium battery capacity depletion formulas are studied for a LiFePO4 battery‎. ‎As a case study‎, ‎application of HESS in a Series Hybrid Electric Bus (SHEB) is considered‎. ‎The results show by the addition of UC‎, ‎the Lithium battery life is improved significantly‎. ‎Furthermore‎, ‎the optimum sizing of the HESS is dependent to the SHEB driving cycle‎. ‎Therefore‎, ‎considering the power profile of the HESS in its sizing process may reduce HESS cost‎. ‎This effect is studied in three different cycles of the SHEB‎. ‎In addition‎, ‎the formulation is applied to cycle-based optimization of the Power Distribution Control Strategy (PDCS) of the HESS by dynamic programming‎. ‎The results show the optimum PDCS has better LiFePO4 battery life in comparison with the conventional PDCS‎. [Copyright &y& Elsevier]

This paper deals with the control design of a military hybrid electric vehicle (HEV). The studied vehicle presents a highly redundant architecture to ensure high system reliability. As a consequence, the control scheme is complex to design. Energetic macroscopic representation (EMR) is used to describe the interaction between all subsystems of the powertrain. A control structure is deduced by an inversion of the EMR of the vehicle powertrain. Simulation results demonstrate the ability of this inversion-based control to achieve military missions. [ABSTRACT FROM PUBLISHER]

A power-split hybrid electric vehicle (HEV) combines the advantages of both series and parallel hybrid vehicle architectures by utilizing a planetary gear set to split and combine the power produced by electric machines and a combustion engine. Because of the different modes of operation, devising a near optimal energy management strategy is quite challenging and essential for these vehicles. To improve the fuel economy of a power-split HEV, we first formulate the energy management problem as a nonlinear and constrained optimal control problem. Then two different cost functions are defined and model predictive control (MPC) strategies are utilized to obtain the power split between the combustion engine and electrical machines and the system operating points at each sample time. Simulation results on a closed-loop high-fidelity model of a power-split HEV over multiple standard drive cycles and with different controllers are presented. The results of a nonlinear MPC strategy show a noticeable improvement in fuel economy with respect to those of an available controller in the commercial Powertrain System Analysis Toolkit (PSAT) software and the other proposed methodology by the authors based on a linear time-varying MPC. [ABSTRACT FROM PUBLISHER]

Real-time energy management of hybrid electric vehicles (HEVs) is a key point for performing effective fuel economy optimization. Offline methods have been developed for energy management optimization when the drive cycle is known. Online real-time methods can provide good results but can only ensure suboptimal management. In this paper, it is assumed that information about the route is available in advance. Using this knowledge, global optimization methods can be used in real-time control to approach optimal fuel consumption while keeping the state of charge (Soc) of the batteries at a desired level. Such a method is presented in this paper. The developed strategy is implemented in a real-time experiment using the power-hardware-in-the-loop (PHIL) principle. The measured fuel consumption and the obtained battery Soc trajectory demonstrate good performance of the proposed control. [ABSTRACT FROM PUBLISHER]

Abstract: The studies for hybrid electrical vehicle (HEV) have attracted considerable attention because of the necessity of developing alternative methods to generate energy for vehicles due to limited fuel based energy, global warming and exhaust emission limits in the last century. HEV incorporates internal composition engine, electric machines and power electronic equipments. In this study, overview of HEVs with a focus on hybrid configurations, energy management strategies and electronic control units are presented. Advantages and disadvantages of each configuration are clearly emphasized. The existing powertrain control techniques for HEVs are classified and comprehensively described. Electronic control units used in HEV configuration are also elaborated. The latest trends and technological challenges in the near future for HEVs are discussed. [ABSTRACT FROM AUTHOR]

With the advent of more stringent regulations related to emissions, fuel economy, and global warming, as well as energy resource constraints, electric, hybrid, and fuel-cell vehicles have attracted increasing attention from vehicle constructors, governments, and consumers. Research and development efforts have focused on developing advanced powertrains and efficient energy systems. This paper reviews the state of the art for electric, hybrid, and fuel-cell vehicles, with a focus on architectures and modeling for energy management. Although classic modeling approaches have often been used, new systemic approaches that allow better understanding of the interaction between the numerous subsystems have recently been introduced. [ABSTRACT FROM AUTHOR]

Dynamic programming (DP) is currently the reference optimal energy management approach for hybrid electric vehicles (HEVs). However, several research concerns arise regarding the effective application of DP for optimal HEV control problems which involve a significant number of control variables, state variables and optimization constraints. This paper deals with an optimal control problem for a full parallel P2 HEV with constraints on battery state-of-charge (SOC), battery lifetime in terms of state-of-health (SOH), and smooth driving in terms of the frequencies of internal combustion engine (ICE) activations and gear shifts over time. The DP formulation for the considered HEV control problem is outlined, yet its practical application is demonstrated as unfeasible due to a lack of computational power and memory in current desktop computers. To overcome this drawback, a computationally efficient version of DP is proposed which is named Slope-weighted Rapid Dynamic Programming (SRDP). Computational advantage is achieved by SRDP in considering only the most efficient HEV powertrain operating points rather than the full set of control variable values at each time instant of the drive cycle. A benchmark study simulating various drive cycles demonstrates that the introduced SRDP can achieve compliance with imposed control constraints on battery SOC, battery SOH and smooth driving. At the same time, SRDP can achieve up to 78% computational time saving compared with a baseline DP approach considering the Worldwide Harmonized Light Vehicle Test Procedure (WLTP). On the other hand, the increase in the fuel consumption estimated by SRDP is limited within 3.3% compared with the baseline DP approach if the US06 Supplemental Federal Test Procedure is considered. SRDP could thus be exploited to efficiently explore the large design space associated to HEV powertrains. [ABSTRACT FROM AUTHOR]