Thermostat Strategy Research Articles

Research on Energy Management Strategy for Extended‐Range Electric Vehicle Based on Bargaining Game

To improve the fuel economy and battery life of extended‐range electric vehicles (EREVs), an energy management strategy (EMS) based on the bargaining game (BG) is proposed. This strategy optimizes the overall economy by optimizing power allocation. First, a bargaining model for EREVs is developed. The model considers the auxiliary power unit (APU) and the battery as two parties of the game. The power allocation between the APU and the battery optimizes both the fuel consumption and the battery life. And it also improves the overall benefit of the vehicle. Second, the effect of the discount factor on the control results is analyzed to obtain the best optimization results. Finally, compared to the thermostat strategy and the dynamic programming‐based strategy, the proposed BG‐based strategy reduces fuel consumption by 27.6% and 1.4% under the World Light Vehicle Test Procedure and reduces the effective ampere‐hour fluxes by 62.7% and 14.6%. Under the China Light‐duty Vehicle Test Cycle, it reduces fuel consumption by 32.5% and 0.5% and reduces the effective ampere‐hour fluxes by 56.8% and 27.5%. The simulation results manifest the excellent performance of the proposed BG‐based EMS.

Research on energy management strategy of fuel-cell vehicles based on nonlinear model predictive control

Fuel cell hybrid electric vehicles (FCHEV) are one of the most promising new energy vehicles. The cost and lifetime of its powertrain have limited its commercial development. This paper proposed an energy management strategy based on nonlinear model predictive control (NMPC) technology to solve the economy and durability problem of FCHEVs. Based on Markov Monte Carlo(MCMC) method, a prediction model of multi-scale operating conditions is established, and dynamic programming(DP) is used to realize the optimal control in the predicted time domain. The “constant speed prediction” is innovatively adopted in the transition stage to improve the prediction accuracy and enable the model to be realized online. The ways to reduce calculating amount of NMPC are also discussed in this paper. This simplification leads to suboptimal fuel economy and durability of control system but can have obvious reduction in calculating time. The simulation results show that, compared with the thermostat strategy and the power following strategy, the degradation cost decrease of 11.1% and 23.9% and the total operation cost of NMPC decrease of 11.0% and 23.5% respectively. The NMPC strategy has better economy and durability than the rule-based energy management strategy, is close to the global optimal result obtained by dynamic programming and can meet the requirements of real-time control.

A Mode-Driven Control Strategy to Reduce Electric Drive Peak Power of Hybrid Wheel Loader Propulsion System

Powertrain electrification is an effective solution to reducing emission of mobile machines. In this paper, an electric-hydraulic hybrid drive for wheel loader propulsion system is studied, where it combines high energy density of electric drive and high power density of hydraulic drive. The braking energy is captured hydraulically and stored in an accumulator. This reduces the peak power of electric drive due to large vehicle acceleration and deceleration, making the peak electric power reduction possible. The energy management strategy is essential for hybrid powertrain since it determines how efficiently the power is transferred between different sources. The existing control strategies such as thermostat strategy and power follower strategy are largely dependent on the cycles and it is challenging to achieve satisfactory results when the cycle changes. The wheel loader loading cycle is a characterized cycle consisting of multiple predictable operating modes, giving some opportunities to develop a mode-driven strategy. In this paper a mode-driven control strategy for electric-hydraulic hybrid wheel loader is proposed to achieve peak electric power reduction over power follower strategy (baseline strategy), without sacrificing electric energy use and vehicle operation hours. In the strategy four modes are defined and the expected hydraulic SOC (system pressure) profile in each mode is scheduled so that it can provide power assist and capture regenerative braking energy as much as possible. By setting the pressure profile, the hydraulic charge sustaining is guaranteed. The system operation with mode-driven strategy is compared with power follower strategy through simulation studies. Results show that peak powers of battery and electric motor with mode-driven strategy are reduced by nearly 30% compared to power follower strategy. Results also show that the vehicle operation hour has been slightly increased by 3% by using mode-driven strategy. These results verify the effectiveness of proposed strategy for electric-hydraulic hybrid wheel loader.

Adaptive Equivalent Fuel Consumption Minimization Based Energy Management Strategy for Extended-Range Electric Vehicle

Unlike battery electric vehicles, extended-range electric vehicles have one more energy source, so a reasonable energy management strategy (EMS) is crucial to the fuel economy of the vehicles. In this paper, an adaptive equivalent fuel consumption minimization strategy (A-ECMS)-based energy management strategy is proposed for the extended-range electric vehicle. The equivalent fuel consumption minimization strategy (ECMS), which utilizes Pontryagin’s minimum principle (PMP), is introduced to design the EMS. Compared with other ECMS strategies, an adaptive equivalent factor algorithm, based on state of charge (SOC) feedback and a proportional–integral (PI) controller is designed to update the equivalent factor under different working conditions. Additionally, a start–stop penalty is added to the objective function to take the dynamic start–stop process of the range extender into account. As a result, under the WLTC driving cycle, the proposed strategy can achieve 6.78 L/100 km comprehensive fuel consumption, saving 6.2% and 3.4% fuel consumption compared with the conventional rule-based thermostat strategy and the power following strategy. Moreover, the proposed EMS achieves the lowest ampere-hour flux among the three EMSs, indicating its ability to improve battery life.

Q-Learning-Based Supervisory Control Adaptability Investigation for Hybrid Electric Vehicles

As one of adaptive optimal controls, the Q-learning based supervisory control for hybrid electric vehicle (HEV) energy management is rarely studied for its adaptability. In real-world driving scenarios, conditions such as vehicle loads, road conditions and traffic conditions may vary. If these changes occur and the vehicle supervisory control does not adapt to it, the resulting fuel economy may not be optimal. To our best knowledge, for the first time, the study investigates the adaptability of Q-learning based supervisory control for HEVs. A comprehensive analysis is presented for the adaptability interpretation with three varying factors: driving cycle, vehicle load condition, and road grade. A parallel HEV architecture is considered and Q-learning is used as the reinforcement learning algorithm to control the torque split between the engine and the electric motor. Model Predictive Control, Equivalent consumption minimization strategy and thermostatic control strategy are implemented for comparison. The Q-learning based supervisory control shows strong adaptability under different conditions, and it leads the fuel economy among four supervisory controls in all three varying conditions.

Napredni model upravljanja energijom zasnovan na pravilima za poboljšanje ekonomičnosti hibridnih električnih guseničnih vozila

All recent technological developments in the field of power distribution in hybrid electric tracked vehicles are often hard to apply and carry high computational burden which makes them impractical for real-time applications. In this paper, a novel control strategy is proposed for parallel hybrid electric tracked vehicle based on robust and easy to implement thermostat strategy with added merits of power follower control strategy (PFCS). The goal of the control strategy is enhanced fuel economy. Serbian infantry fighting vehicle BVP M80-A is chosen as the reference vehicle. For the purpose of validation, a backward-looking, high fidelity model is created in Simulink environment. Investigation of the results indicates that the proposed control strategy offers 12.8% better fuel economy while effectively maintaining battery state of charge (SOC). Even better results (23.2%) were achieved applying the proposed strategy to a model with an additional generator. It is concluded that further improvements can be made with combined sizing and control optimization.

Ensemble Reinforcement Learning-Based Supervisory Control of Hybrid Electric Vehicle for Fuel Economy Improvement

This study proposes an ensemble reinforcement learning (RL) strategy to improve the fuel economy. A parallel hybrid electric vehicle model is first presented, followed by an introduction of ensemble RL strategy. The base RL algorithm is $Q$ -learning, which is used to form multiple agents with different state combinations. Two common energy management strategies, namely, thermostatic strategy and equivalent consumption minimization strategy, are used as two single agents in the proposed ensemble agents. During the learning process, multiple RL agents make an action decision jointly by taking a weighted average. After each driving cycle iteration, $Q$ -learning agents update their state-action values. A single RL agent is used as a reference for the proposed strategy. The results show that the fuel economy of the proposed ensemble strategy is 3.2% higher than that of the best single agent.

Adverse effects of rising interest rates on sustainable energy transitions

Increasing the use of renewable energy (RE) is a key enabler of sustainable energy transitions. While the costs of RE have substantially declined in the past, here we show that rising interest rates (IRs) can reverse the trend of decreasing RE costs, particularly in Europe with its historically low IRs. In Germany, IRs recovering to pre-financial crisis levels in 5 years could add 11% and 25% to the levelized cost of electricity for solar photovoltaics and onshore wind, respectively, with financing costs accounting for about one-third of total levelized cost of electricity. As fossil-fuel-based electricity costs are much less and potentially even negatively affected by rising IRs, the viability of RE investments would be markedly deteriorated. On the basis of these findings, we argue that rising IRs could jeopardize the sustainable energy transition and we propose a self-adjusting thermostatic policy strategy to safeguard against rising IRs. Renewable energy costs have declined in the past but things could change with increasing interest rates. This study shows that in Germany, if interest rates increased to pre-financial crisis levels in 5 years, the levelized cost of solar photovoltaics and onshore wind could rise by 11 and 25%, respectively.

Threshold-changing control strategy for series hybrid electric vehicles

This paper proposes a new set of design principles to classify and design rule-based control strategies for the powertrain energy management of series hybrid electric vehicles. The design principles proposed consider the two most established rule-based control strategies for series hybrid electric vehicles, the Thermostat and the Power follower control strategies, and also an optimization-based control strategy, the Equivalent consumption minimization strategy, in terms of the mechanisms they employ to ensure charge sustaining operation and fuel efficient driving. Thus, the work then reflects upon the most effective design principles and derives a novel and superior rule-based control strategy for series hybrid electric vehicles that is claimed to outperform all the existing rule-based schemes in terms of fuel economy: the optimal primary source strategy (OPSS). The OPSS is implemented and then compared on a high fidelity hybrid electric vehicle model to Thermostat, Power follower and Equivalent consumption minimization strategies, as well as to a recently developed rule-based control strategy, the Exclusive operation strategy. As compared to conventional rule-based control strategies, the OPSS is found to deliver significantly improved fuel economy and which is remarkably close to that achieved by the optimization-based Equivalent consumption minimization strategy, while the design of the OPSS is simple and robust as compared to optimization-based strategies. The impressive performance is partly attributed to the recent improvements in engine start stop system technology. It is also shown that the battery is operated in a more steady manner, with a lower depth of discharge, consequently reducing battery degradation.

Multi-mode energy management strategy for fuel cell electric vehicles based on driving pattern identification using learning vector quantization neural network algorithm

The development of fuel cell electric vehicles can to a certain extent alleviate worldwide energy and environmental issues. While a single energy management strategy cannot meet the complex road conditions of an actual vehicle, this article proposes a multi-mode energy management strategy for electric vehicles with a fuel cell range extender based on driving condition recognition technology, which contains a patterns recognizer and a multi-mode energy management controller. This paper introduces a learning vector quantization (LVQ) neural network to design the driving patterns recognizer according to a vehicle's driving information. This multi-mode strategy can automatically switch to the genetic algorithm optimized thermostat strategy under specific driving conditions in the light of the differences in condition recognition results. Simulation experiments were carried out based on the model's validity verification using a dynamometer test bench. Simulation results show that the proposed strategy can obtain better economic performance than the single-mode thermostat strategy under dynamic driving conditions.

Real-time energy management controller design for a hybrid excavator using reinforcement learning

Real-time energy management of a hybrid excavator is addressed using reinforcement learning (RL). Due to the computational complexity and need for a priori knowledge of the load cycles, a traditional optimal control method, like dynamic programming (DP), is not feasible for real-time control. Real-time controllers derived from traditional optimal control methods compute the solutions either in a cycle-dependent manner or far away from the optimal. An RL-based energy management controller is proposed to solve this problem. The simulation and experimental results demonstrate that the RL controller has a better performance than the widely used thermostat and equivalent consumption minimization strategy (ECMS) controllers. It also shows that the RL controller is cycle-independent. Pontryagin’s minimum principle (PMP) is used to obtain the analytical solution of the energy management problem, and this can help to reduce the iteration time in the design process.

Application of a modified thermostatic control strategy to parallel mild HEV for improving fuel economy in urban driving conditions

A modified thermostatic control strategy is applied to the powertrain control of a parallel mild hybrid electric vehicle (HEV) to improve fuel economy. This strategy can improve the fuel economy of a parallel mild HEV by operating internal combustion engine (ICE) in a high-efficiency region. Thus, in this study, experiments of a parallel mild HEV were conducted to analyze the characteristics of the hybrid electric powertrain and a numerical model is developed for the vehicle. Based on the results, the thermostatic control strategy was modified and applied to the vehicle model. Also, battery protection logic by using electrochemical battery model is applied because the active usage of battery by thermostatic control strategy can damage the battery. The simulation results of the vehicle under urban driving conditions show that the thermostatic control strategy can improve the vehicle’s fuel economy by 3.7 % compared with that of the conventional strategy. The results also suggest that the trade-off between the fuel economy improvement by efficient ICE operation and the battery life reduction by active battery usage should be carefully investigated when a thermostatic control strategy is applied to a parallel mild HEV.

A Supervisory Control Algorithm for a Series Hybrid Vehicle With Multiple Energy Sources

This paper presents a new power distribution strategy (PDS) for a series electric hybrid vehicle (SHEV) in the sense of minimizing fuel usage. The target vehicle has three energy sources, i.e., an engine and generator set (EGS), a battery, and an ultracapacitor, and two traction motors for the front and rear wheels. A two-step PDS based on the real-time optimization technique is proposed to distribute the demand power among three energy sources. Several existing PDSs, such as the thermostat strategy and the power-follower strategy, are modified and applied to be compared with the proposed strategy. An optimal torque distribution strategy between front and rear traction motors is also proposed to minimize the electric energy consumption and to further improve fuel economy. Simulation results demonstrate the feasibility and effectiveness of the proposed strategies.

Hybrid Thermostat Strategy for Enhancing Fuel Economy of Series Hybrid Intracity Bus

The recently developed power distribution strategies in hybrid electric vehicles (HEVs) are often hard to apply, or the mathematical backgrounds are burdened by very complicated calculations. In this paper, a new control strategy, referred to as a hybrid thermostat strategy, is proposed for a series-type HEV (SHEV). The proposed strategy has its foundation in the thermostat strategy and incorporates merits from the power follower strategy such that it overcomes those strategy weaknesses and accomplishes increased efficiency with a practical concept. The development of the proposed strategy is based on AMESim and Simulink cosimulation with Simplex method optimization.

Thermostats and thermostat strategies for molecular dynamics simulations of nanofluidics

The thermostats in molecular dynamics (MD) simulations of highly confined channel flow may have significant influences on the fidelity of transport phenomena. In this study, we exploit non-equilibrium MD simulations to generate Couette flows with different combinations of thermostat algorithms and strategies. We provide a comprehensive analysis on the effectiveness of three thermostat algorithms Nosé-Hoover chain (NHC), Langevin (LGV) and dissipative particle dynamics (DPD) when applied in three thermostat strategies, thermostating either walls (TW) or fluid (TF), and thermostating both the wall and fluid (TWTF). Our results of thermal and mechanical properties show that the TW strategy more closely resembles experimental conditions. The TF and TWTF systems also produce considerably similar behaviors in weakly sheared systems, but deviate the dynamics in strongly sheared systems due to the isothermal condition. The LGV and DPD thermostats used in the TF and TWTF systems provide vital ways to yield correct dynamics in coarse-grained systems by tuning the fluid transport coefficients. Using conventional NHC thermostat to thermostat fluid only produces correct thermal behaviors in weakly sheared systems, and breaks down due to significant thermal inhomogeneity in strongly sheared systems.

Slip in nanoscale shear flow: mechanisms of interfacial friction

The atomistic mechanism of fluid–solid interfacial friction as the basis of slip is still not fully understood. This study explores the interfacial friction mechanisms and their interplay with the nanoscale slip behavior using non-equilibrium molecular dynamics simulations. Our results show that there is an abrupt jump of slip length at a critical shear rate, corresponding to the transition from “defect slip” at low shear rates to “collective slip” at high shear rates. Here, we identified two mechanisms of interfacial friction: surface potential and collision mechanisms. Their impacts on slip are elaborated through a quantitative scaling estimation and our results show that both mechanisms contribute to the defect slip at low shear rates, while the collision mechanism dominates the collective slip at high shear rates. We also verify the importance of the bulk viscous heating via a comparison among different thermostat strategies.

시뮬레이션을 이용한 군용 직렬형 HEV 의 주행 전략에 따른 연비 성능 비교에 관한 연구

군용 차량은 일반 차량에 비하여 높은 기동 성능 및 정숙성을 요구한다. 또한 최소의 연료 보급으로 최대의 작전 수행 능력을 보유하고 있어야 한다. 시리즈 하이브리드 자동차의 경우 모터만을 이용하여 차량을 구동하므로, 정숙성이 뛰어나고 초기 기동 토크가 커 구동 성능이 뛰어나다. 또한 하이브리드화를 통하여 연비 향상 효과를 얻을 수 있다. 이와 같은 시리즈 하이브리드 차량의 경우 배터리 SOC 와 차량의 주행 상태에 따라 엔진과 발전기로 이루어진 발전 시스템에서 전기를 생산하여 차량 구동에 이용하거나 배터리를 충전한다. 발전 시스템의 작동 여부와 작동 영역을 결정하는 것이 시리즈 하이브리드 차량의 주행 전략이며 이 주행전략에 따라 연비 성능에 차이가 난다. 본 연구에서는 Thermostat, Power-Follower, Combined 주행 전략을 비교/평가 하였으며 새롭게 제안한 Combined 주행 전략을 통하여 기존 차량 대비 37%의 연비 향상 효과를 얻을 수 있었다.

Engine-in-the-Loop Validation of a Frequency Domain Power Distribution Strategy for Series Hybrid Powertrains

This paper presents an engine-in-the-loop validation of a power management strategy for hybrid electric powertrains that splits the power demanded by driver between the engine and battery depending on the frequency content. In particular, a series hybrid electric powertrain is considered where the engine is a real 6.4L diesel engine and the rest of the vehicle, including the battery, generator, motors, vehicle dynamics, and driver, is modeled in computer. A networked engine-in-the-loop experiment is considered where the engine and generator constitute one site, and the rest of the system constitutes another site. This networked setup is used to compare the abovementioned power management strategy to a thermostatic strategy as the baseline. For the specific drive cycle considered, the proposed strategy yields about 12% increase in fuel economy, a performance that exceeds the previously reported purely model-based simulation results. In addition, an improvement in battery life can also be expected.

Thermostat strategies impact on energy consumption in residential buildings

This paper identifies the impact of thermostat strategies on heating and cooling energy consumptions in buildings, thus suggesting the importance of a database for understanding the effect of thermostat settings and for deciding energy-conscious strategies. Using computer simulation, diverse thermostat strategies – change of setback period, change of set-point temperature, and change of setback temperature – were tested parametrically in a typical USA single-family homes, located in two climate zones, namely cold (Detroit, Michigan) and hot-humid (Miami, Florida). Analysis revealed that heating and cooling systems were significant energy-consuming components in each climate zone. Accordingly, heating energy in cold climate zones and cooling energy in hot-humid climate zones have potential for matchingly significant saving. Diverse thermostat strategies (changes of setback period, of set-point, of setback temperature) demonstrated their obvious impacts on such saving. In the cold climate in particular, the heating system showed the most significant energy-saving effect via proper thermostat strategies. Proper setback period, set-point and setback temperature need to be established to achieve energy efficiency in residential buildings.

Minimization of power losses in hybrid electric vehicles in view of the prolonging of battery life

Hybrid Electric Vehicles (HEVs) are becoming more popular than pure electric ones, nowadays. This is because of their better performance, economic advantages and higher operating range. However, their potential advantages extremely depend on their system design, most importantly their battery system design. Batteries’ life requirements as well as the cost of replacing them at the end of their life period, currently limit manufacturers to bring HEVs into play, even though their fuel economy reduces their everyday cost considerably. Generally, inappropriate discharge/charge patterns would result in loss in batteries’ life. In the present study, an optimization based control strategy has been proposed for the series HEVs in order to maximize the efficiency of the power-train while minimizing the loss. A genetic algorithm is implemented to optimally evaluate the control algorithm's parameters. The approach is then compared to two main control strategies, namely thermostatic control strategy and power follower control strategy. The computational procedure of the genetic algorithm is discussed, and a simulation study based on a model of a series hybrid electric vehicle is given to validate the genetic algorithm results.