Equivalent Consumption Minimization Strategy With Consideration of Battery Aging for Parallel Hybrid Electric Vehicles

The equivalent consumption minimization strategy (ECMS) is a well-known energy management strategy for Hybrid Electric Vehicles (HEV). ECMS is very computationally efficient since it yields an instantaneous optimal control. ECMS has been shown to minimize fuel consumption under certain conditions. But, minimizing the fuel consumption often leads to excessive battery damage. This paper introduces a new optimal control problem where the cost function includes terms for both fuel consumption and battery aging. The Ah-throughput method is used to quantify battery aging. ECMS (with the appropriate equivalence factor) is shown to also minimize the cost function that incorporates battery aging. Simulation results show that the proposed aging ECMS algorithm significantly improves battery aging with little or no fuel economy penalty compared to ordinary ECMS

Design Optimization and Optimal Control for Hybrid Vehicles

International audienceGrowing environmental and global crude oil supplies concerns are stimulating research on new vehicle technologies. Hybrid-electric vehicles appear to be one of the most promising technologies for reducing fuel consumption and pollutant emissions. Different types of hybrid-electric powertrains exist: from the mild-hybrid vehicle, equipped with a small electric motor, to the combined hybrid like the Toyota prius. This paper presents a parametric study focused on variations of the size of the powertrain components , and optimization of the power split between the engine and electric motor with respect to fuel consumption. To perform this optimization on a prescribed driving cycle (for instance, the New European Driving Cycle), a dynamic programming algorithm based on a reduced model is implemented. This simplified model allows a fast optimization with a fine parameterization of the controller: it furnishes the optimal power repartition at each time step regarding fuel consumption under constraints on the battery state of charge. The obtained results may be used to determine the best components of a given powertrain, for a prescribed vehicle cycle. The optimal split obtained thanks to dynamic programming algorithm can not be used directly on a vehicle as a real time control law, as the future can not be known in advance in normal driving conditions. To overcome this difficulty, we implement, as a real-time strategy, the Equivalent Consumption Minimization Strategy (ECMS): the battery being considered as an auxiliary reversible fuel reservoir, an instantaneous minimization of ECMS is performed. This control law is inferred from Pontryagin's Minimum Principle, where the Lagrange multiplier can be deduced from previous optimization results on given driving cycles. Offline optimization results and real-time control laws are compared for a realistic hybrid vehicle application. 2

A Component Sizing Oriented On-line Controller for Parallel Hybrid Electric Vehicle Powertrains based on the Adaptive Equivalent Consumption Minimization Strategy

In this paper is illustrated the development of a flexible and near-optimal on-line controller for parallel hybrid electric vehicle (HEV) powertrains based on the well known Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) approach. Guidelines for the developed A-ECMS are automatically extrapolated from a rapid near-optimal off-line HEV controller. Results demonstrate that the implemented version of A-ECMS can remarkably improve the fuel economy performance of the traditional ECMS converging to the off-line near-optimal control benchmark. Moreover, the successful automated application of the developed A-ECMS to two different vehicles sizes suggests its ease of implementation in HEV component sizing processes

Adaptive Equivalent Consumption Minimization Strategy with Rule-based Gear Selection for the Energy Management of Hybrid Electric Vehicles Equipped with Dual Clutch Transmissions

Based on observations of the behaviour of the optimal solution to the problem of energy management for plug-in hybrid electric vehicles, a novel real-time Energy Management Strategy (EMS) is proposed. In particular, dynamic programming results are used to derive a set of rules aiming at reproducing the optimal gearshift schedule in electric mode while the Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) is employed to decide the powertrain operating mode and the current gear when power from the internal combustion engine is needed. In terms of total fuel consumption, simulations show that the proposed approach yields results that are close to the optimal solution and also outperforms those of the A-ECMS, a well-known EMS. One of the main aspects that differentiates the strategy here proposed from previous works is the introduction of a model to use physical considerations to estimate the energy consumption during gearshifts in dual-clutch transmissions. This, together with a series of properly tuned fuel penalties allows the controller to yield results in which there is no gear hunting behaviour

Efficient Thermal Electric Skipping Strategy Applied to the Control of Series/Parallel Hybrid Powertrain

The optimal control of hybrid powertrains represents one of the most challenging tasks for the compliance with the legislation concerning CO2 and pollutant emission of vehicles. Most common off-line optimization strategies (Pontryagin minimum principle-PMP-or dynamic programming) allow to identify the optimal control along a predefined driving mission at the expense of a quite relevant computational effort. On-line strategies, suitable for on-vehicle implementation, involve a certain performance degradation depending on their degree of simplification and computational effort. In this work, a simplified control strategy is presented, where the conventional power-split logics, typical of the above-mentioned strategies, is here replaced with an alternative utilization of the thermal and electric units for the vehicle driving (Efficient Thermal Electric Skipping Strategy-ETESS). The choice between the units is realized at each time and is based on the comparison between the effective fuel rate of the thermal engine and an equivalent fuel rate related to the electrical power consumption. The equivalent fuel rate in a pure electric driving is associated to a combination of brake specific fuel consumption of the thermal engine, and electro-mechanical efficiencies along the driveline. The ETESS is applied for the simulation of segment C hybrid vehicle, equipped with a thermal engine and two electric units (motor and generator). The methodology is tested along regulatory driving cycles (WLTP, Artemis) and RDE, with different powertrain variants. Numerical results underline that the proposed approach performs very close to most common control strategies (consumed fuel per kilometer higher than PMP of about 1% on average). The main advantage is a reduced computational effort (decrease of 99% on average). The ETESS is straightforwardly adapted for an on-line implementation, through the introduction of an adaptative factor, preserving the computational effort and the fuel economy

Analytical Optimal Solution to the Energy Management Problem in Series Hybrid Electric Vehicles

[EN] An optimal solution to the energy management problem in hybrid electric vehicles has been extensively addressed in the literature during the last decade, especially with the application of dynamic programming, the Pontryagin minimum principle, or equivalent consumption minimization strategy. However, most of the works consist in finding cycle-specific optimal trajectories, which are far from being general control strategies. The aim of this work is to derive an analytical expression, general and not cycle specific, for the energy management problem that summarizes the optimal controls for a series hybrid electric vehicle. Starting from a simple definition of the powertrain, an explicit formulation is deducted to minimize fuel consumption based on an analytic analysis of the Pontryagin minimum principle. Explicit expressions for control variables and costates are provided. The result is a general control strategy that specifies the optimal generator set usage for a given probability distribution of expected traction demands. This methodology is benchmarked with common methods in the literature (dynamic programming and numerical Pontryagin minimum principle) showing near identical results but with strongly reduced computational time. The general form of this control strategy can also be used to analyze the optimal operation range of the engine, which could be useful for designing purposes.This work was supported by the Ministerio de Economia y Competitividad through Project TRA2016-78717-R.Luján, JM.; Guardiola, C.; Pla Moreno, B.; Reig, A. (2018). Analytical Optimal Solution to the Energy Management Problem in Series Hybrid Electric Vehicles. IEEE Transactions on Vehicular Technology. 67(8):6803-6813. https://doi.org/10.1109/TVT.2018.2821265S6803681367

Design optimization and optimal control for hybrid vehicles

International audienceIn the context of growing environmental concerns, hybrid-electric vehicles appear to be one of the most promising technologies for reducing fuel consumption and pollutant emissions. This paper presents a parametric study focused on variations of the size of the powertrain components, and optimization of the power split between the engine and electric motor with respect to fuel consumption. To take into account the ability of the engine to be turned off, and the energy consumed to start the engine, we consider a second state to represent the engine: this state permits to obtain a more realistic engine model than it is usually done. Results are obtained for a prescribed vehicle cycle thanks to a dynamic programming algorithm based on a reduced model, and furnish the optimal power repartition at each time step regarding fuel consumption under constraints on the battery state of charge, and may then be used to determine the best components of a given powertrain. To control the energy sources in real driving conditions, when the future is unknown , a real-time control strategy is used: the Equivalent Consumption Minimization Strategy (ECMS). In this strategy, the battery is being considered as an auxiliary reversible fuel reservoir, using a scaling parameter which can be deduced from dynamic programming results. Offline optimization results and ECMS are compared for a realistic hybrid vehicle application

Implementation Of Fuzzy Logic Control Into An Equivalent Minimization Strategy For Adaptive Energy Management Of A Parallel Hybrid Electric Vehicle

As government agencies continue to tighten emissions regulations due to the continued increase in greenhouse gas production, automotive industries are seeking to produce increasingly efficient vehicle technology. Electric vehicles have been introduced by the industry, showing promising signs of reducing emissions production in the automotive sector. However, many consumers may be hesitant to purchase fully electric vehicles due to several uncertainty variables including available charging stations. Hybrid electric vehicles (HEVs) have been introduced to reduce problems while improving fuel economy. HEVs have led to the demand of creating more advanced controls software to consider multiple components for propulsive power in a vehicle. A large section in the software development process is the implementation of an optimal energy management strategy meant to improve the overall fuel efficiency of the vehicle. Optimal strategies can be implemented when driving conditions are known a prior. The Equivalent Consumption Minimization Strategy (ECMS) is an optimal control strategy that uses an equivalence factor to equate electrical to mechanical power when performing torque split determination between the internal combustion engine and electric motor for propulsive and regenerative torque. This equivalence factor is determined from offline vehicle simulations using a sensitivity analysis to provide optimal fuel economy results while maintaining predetermined high voltage battery state of charge (SOC) constraints. When the control hierarchy is modified or different driving styles are applied, the analysis must be redone to update the equivalence factor. The goal of this work is to implement a fuzzy logic controller that dynamically updates the equivalence factor to improve fuel economy, maintain a strict charge sustaining window of operation for the high voltage battery, and reduce computational time required during algorithm development. The adaptive algorithm is validated against global optimum fuel economy and charge sustaining results from a sensitivity analysis performed for multiple drive cycles. Results show a maximum fuel economy improvement of 9.82% when using a mild driving style and a 95% success rate when maintaining an ending SOC within 5% regardless of starting SOC. Recommendations for modification of the fuzzy logic controller are made to produce additional fuel economy and charge sustaining benefits from the parallel hybrid vehicle model

Exclusive Operation Strategy for the Supervisory Control of Series Hybrid Electric Vehicles

Supervisory control systems (SCSs) are used to manage the powertrain of hybrid electric vehicles (HEV). This paper 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: Thermostat control strategy (TCS), Power follower control strategy (PFCS) and Global equivalent consumption minimization strategy (GECMS). 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 as compared to 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 effectiveness of the XOS makes it a practical SCS

An Optimization Approach for Energy Efficient Coordination Control of Vehicles in Merging Highways

Environmental concerns along with stronger governmental regulations regarding automotive fuel-economy and greenhouse-gas emissions are contributing to the push for development of more sustainable transportation technologies. Furthermore, the widespread use of the automobile gives rise to other issues such as traffic congestion and increasing traffic accidents. Consequently, two main goals of new technologies are the reduction of vehicle fuel consumption and emissions and the reduction of traffic congestion. While an extensive list of published work addresses the problem of fuel consumption reduction by optimizing the vehicle powertrain operations, particularly in the case of hybrid electric vehicles (HEV), approaches like eco-driving and traffic coordination have been studied more recently as alternative methods that can, in addition, address the problem of traffic congestion and traffic accidents reduction. This dissertation builds on some of those approaches, with particular emphasis on autonomous vehicle coordination control. In this direction, the objective is to derive an optimization approach for energy efficient and safe coordination control of vehicles in merging highways. Most of the current optimization-based centralized approaches to this problem are solved numerically, at the expense of a high computational load which limits their potential for real-time implementation. In addition, closed-form solutions, which are desired to facilitate traffic analysis and the development of approaches to address interconnected merging/intersection points and achieve further traffic improvements at the road-network level, are very limited in the literature. In this dissertation, through the application of the Pontryagin’s minimum principle, a closed-form solution is obtained which allows the implementation of a real-time centralized optimal control for fleets of vehicles. The results of applying the proposed framework show that the system can reduce the fuel consumption by up to 50% and the travel time by an average of 6.9% with respect to a scenario with not coordination strategy. By integrating the traffic coordination scheme with in-vehicle energy management, a two level optimization system is achieved which allows assessing the benefits of integrating hybrid electric vehicles into the road network. Regarding in-vehicle energy optimization, four methods are developed to improve the tuning process of the equivalent consumption optimization strategy (ECMS). First, two model predictive control (MPC)-based strategies are implemented and the results show improvements in the efficiency obtained with the standard ECMS implementation. On the other hand, the research efforts focus in performing analysis of the engine and electric motor operating points which can lead to the optimal tuning of the ECMS with reduced iterations. Two approaches are evaluated and even though the results in fuel economy are slightly worse than those for the standard ECMS, they show potential to significantly reduce the tuning time of the ECMS. Additionally, the benefits of having less aggressive driving profiles on different powertrain technologies such as conventional, plug-in hybrid and electric vehicles are studied