Energy Management Strategy in Consideration of Battery Health for PHEV via Stochastic Control and Particle Swarm Optimization Algorithm

Wang, Yuying; Jiao, Xiaohong; Sun, Zitao; Li, Ping

doi:10.3390/en10111894

Cited by 31 publications

(19 citation statements)

References 28 publications

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“…Moreover, NPC increased from $107,637 to $114,274. However, researchers have recommended that the SOC min not be set to an extremely low value to avoid damaging the storage bank by excessive discharge [62][63][64][65].…”

Section: Battery Minimum Socmentioning

confidence: 99%

Energy Management and Optimization of a PV/Diesel/Battery Hybrid Energy System Using a Combined Dispatch Strategy

et al. 2019

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In recent years, the concept of hybrid energy systems (HESs) is drawing more attention for electrification of isolated or energy-deficient areas. When optimally designed, HESs prove to be more reliable and economical than single energy source systems. This study examines the feasibility of a combined dispatch (CD) control strategy for a photovoltaic (PV)/diesel/battery HES by combining the load following (LF) strategy and cycle charging (CC) strategy. HOMER software is used as a tool for optimization analysis by investigating the techno-economic and environmental performance of the proposed system under the LF strategy, CC strategy, and combined dispatch CD strategy. The simulation results reveal that the CD strategy has a net present cost (NPC) and cost of energy (COE) values of $110,191 and $0.21/kWh, which are 20.6% and 4.8% lower than those of systems utilizing the LF and CC strategies, respectively. From an environmental point of view, the CD strategy also offers the best performance, with CO 2 emissions of 27,678 kg/year. Moreover, the results show that variations in critical parameters, such as battery minimum state of charge, time step, solar radiation, diesel price, and load growth, exert considerable effects on the performance of the proposed system. Sustainability 2019, 11, 683 2 of 26 different energy sources to produce a hybrid energy system (HES), which can solve reliability problems and provide an environmentally friendly solution [7][8][9]. Various power sources and storages, such as diesel generators, batteries, and supercapacitors, should be integrated into HESs to improve system stability and smooth out fluctuations [10,11].Sustainability 2018, 10, x FOR PEER REVIEW 2 of 29 problems and provide an environmentally friendly solution [7][8][9]. Various power sources and storages, such as diesel generators, batteries, and supercapacitors, should be integrated into HESs to improve system stability and smooth out fluctuations [10,11]. Figure 2. Commonly used approaches for energy management strategies [14]. Sustainability 2019, 11, 683 3 of 26Although the non-dispatchable RESs necessitate complex systems modeling, they are themselves simple to model without the need for control logic, in which the power is merely produced in direct response to the available renewable energy resources. On the other hand, the dispatchable components, such as the generator and battery, are more difficult to model because they must be controlled to achieve matching of demand and supply properly, and to compensate for the intermittency of the non-dispatchable RESs [16,17]. "Should a generator charge the battery or should excess solar production be responsible for charging the battery only and the generator used only to satisfy the load?" This question has led to some rules that manage the operation of generator and battery energy storage. The load following (LF) and cycle charging (CC) dispatch strategies are used to control generator operation and battery energy storage. Under the LF strategy, a generator produces only e...

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Section: Battery Minimum Socmentioning

confidence: 99%

Energy Management and Optimization of a PV/Diesel/Battery Hybrid Energy System Using a Combined Dispatch Strategy

et al. 2019

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“…To avoid the frequent charging and discharging of electric vehicles and to maximize regenerative braking energy, the authors of [39] proposed a new hybrid energy storage system. The multi-objective optimization problem for PHEV considering energy consumption and battery health at the same time is solved by using stochastic dynamic programming and particle swarm optimization [40]. At this time, a semi-empirical model considering the effects of battery temperature and SOC was used as the battery lifetime model.…”

Section: Nomenclature V Ocmentioning

confidence: 99%

Economic Nonlinear Predictive Control for Real-Time Optimal Energy Management of Parallel Hybrid Electric Vehicles

Kim

Bae

et al. 2020

IEEE Access

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This paper presents an economic nonlinear hybrid model predictive control strategy for optimal energy management of parallel hybrid electric vehicles. Hybrid electric vehicles are controlled for operation in various driveline modes and the associated optimal control problem involves both continuous and discrete control variables. To solve the resultant mixed-integer nonlinear optimal control problem, we propose a hierarchical supervisory control architecture that consists of demand prediction, driveline mode determination, and real-time optimization. These three modules are designed independently and connected in series to perform computer-aided control. The demand prediction module uses a times series model to forecast the mechanical traction power requests of the driver over a prediction horizon based on vehicle speed, road grade, acceleration pedal scale, brake pedal scale, and past and current power demands. For a given forecasted power demand profile, the mode determination module decides a sequence of driveline modes that are presumed to be operated over the prediction horizon. The model-based real-time optimization corresponding to nonlinear model predictive control computes the optimal motor power over a prediction horizon, and the receding horizon scheme as feedback control is applied to repeat the processes of the three control modules. A dedicated case study with real driving data obtained from Hyundai IONIQ PHEV 2018 is presented to demonstrate the effectiveness in fuel economy and emission reduction offered by the proposed optimal energy management strategy. The proposed hierarchical real-time predictive optimizationbased strategy is competitive with any exiting power management strategies such as dynamic programming and equivalent consumption minimization strategy in fuel economy and emission reduction while showing better charge-sustaining capability. This trade-off between fuel economy and charge-sustainability can be further improved by tuning the hyper-parameters in the proposed optimal control problem.

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“…However, traditional DP can only be used to attain the optimal solutions under the designated driving cycles rather than the unpredictable driving patterns in reality. An evolved approach from DP called stochastic dynamic programming (SDP) is capable of dealing with uncertain situations during actual driving and, thus, may further extend the application to real-time control [24]. However, the undesirably high computing load and sophisticated derivation of mathematics limit its application to the practical control of vehicles.…”

Section: Introductionmentioning

confidence: 99%

The Optimal Control of Fuel Consumption for a Heavy-Duty Motorcycle with Three Power Sources Using Hardware-in-the-Loop Simulation

2019

Energies

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This study presents a simulation platform for a hybrid electric motorcycle with an engine, a driving motor, and an integrated starter generator (ISG) as three power sources. This platform also consists of the driving cycle, driver, lithium-ion battery, continuously variable transmission (CVT), motorcycle dynamics, and energy management system models. Two Arduino DUE microcontrollers integrated with the required circuit to process analog-to-digital signal conversion for input and output are utilized to carry out a hardware-in-the-loop (HIL) simulation. A driving cycle called worldwide motorcycle test cycle (WMTC) is used for evaluating the performance characteristics and response relationship among subsystems. Control strategies called rule-based control (RBC) and equivalent consumption minimization strategy (ECMS) are simulated and compared with the purely engine-driven operation. The results show that the improvement percentages for equivalent fuel consumption and energy consumption for RBC and ECMS using the pure software simulation were 17.74%/18.50% and 42.77%/44.22% respectively, while those with HIL were 18.16%/18.82% and 42.73%/44.10%, respectively.

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Energy Management Strategy in Consideration of Battery Health for PHEV via Stochastic Control and Particle Swarm Optimization Algorithm

Cited by 31 publications

References 28 publications

Energy Management and Optimization of a PV/Diesel/Battery Hybrid Energy System Using a Combined Dispatch Strategy

Energy Management and Optimization of a PV/Diesel/Battery Hybrid Energy System Using a Combined Dispatch Strategy

Economic Nonlinear Predictive Control for Real-Time Optimal Energy Management of Parallel Hybrid Electric Vehicles

The Optimal Control of Fuel Consumption for a Heavy-Duty Motorcycle with Three Power Sources Using Hardware-in-the-Loop Simulation

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