Co-optimization of speed trajectory and power management for a fuel-cell/battery electric vehicle

Kim, Youngki; Figueroa-Santos, Miriam; Prakash, Niket; Baek, Stanley; Siegel, Jason B.; Rizzo, Denise

doi:10.1016/j.apenergy.2019.114254

Cited by 73 publications

(37 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kim et al 65 investigate the eco‐driving in the literature using a sequential optimization for fast computation. They try to find the energy‐efficient speed pointing in the vehicles' dynamics.…”

Section: Methodsmentioning

confidence: 99%

“…Several types of classifications of energy management strategies have been suggested in the revised literature considering the criteria of taxonomy, advantages and disadvantages, the natural‐inspired algorithm used, performance obtained, etc. In the following sections, we built two classifications: the first is based on the type of algorithm, and the second is based on the goal they seek to optimize. Rule‐based strategies Fuzzy control strategy 42,76,80,82 State machine control strategy 84 Classical PI control strategy 37,43,47,48,52‐55,58,60,65,67,69,79 Power prediction 24,30,47,87,89,91 Unscented Kalman filter 61 Optimisation‐based strategies Pontryagin's minimum principle (PMP) 39,64 Quadratic programming (QPo) 38,56,86 Stochastic dynamic programming (SDP) 71,92 Multi‐mode predictive 68,90 Dynamic particle swarm optimization 28,83 Equivalent consumption minimization strategy (ECMS) 31,38,41,98 Dynamic programming (DP) 25,59,64 Genetic algorithm (GA) 40,43,76 Efficiency optimization strategy 27,29,36,57,85,93,96 Learning‐based strategies Reinforcement learning 33,62,74 Hybrid 32,46,49,63,66,70,72,73,75,77,78,81,88,95,97 …”

Section: Classification Of Strategiesmentioning

confidence: 99%

“…Compared to the problems and strategies raised in the previews 77 references [24–97, 100–102], critically commented by the authors in their work using tools as tables, diagrams, and figures, the actual problems and requests, identified from the reviewed literature from 2019 and 2020, evolved with the trends, and they are more specific and harder to archive.…”

Section: Novelty Of the Subjectmentioning

confidence: 99%

See 2 more Smart Citations

Optimal energy management strategies for the electric vehicles compiling bibliometric maps

2021

View full text Add to dashboard Cite

Summary The object of this research came from the continuing need to analyze and identify effective methods of reducing pollution and optimizing the functions of electric vehicles. So, a classic analysis of the management strategies proposed in the literature was performed to identify the main research directions that can be used to design an optimal strategy. The performed analysis was validated using different counting and representation methods in VOSviewer and Excel. VOSviewer is a program that visually presents the best research approaches for understanding and developing applications. Thus, analyzing the results obtained, it is possible to identify where the research of energy management strategies for electric vehicles is focused. For example, the most important factors for increasing the performance of a strategy are identified as related to optimization, efficiency, degradation, fuel economy, and systemic development. We identified 127 energy management strategies in the studied articles, which were analyzed and classified by multiple criteria. For example, 21.59% of the strategies have as main goal to increase the vehicles efficiency, followed by the 10% on fuel economy, 7.8% hydrogen consumption strategy, 6.29% work for performance optimization and emission reduction, and so on. So, using this tool, researchers can explore their field of research to identify challenging topics to address in the coming years.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Classification Of Strategiesmentioning

confidence: 99%

Section: Novelty Of the Subjectmentioning

confidence: 99%

See 1 more Smart Citation

Optimal energy management strategies for the electric vehicles compiling bibliometric maps

2021

View full text Add to dashboard Cite

show abstract

“…Consider the point-mass longitudinal dynamics of the vehicle. We assume that the effective mass of the vehicle, which normally accounts for the vehicle static and rotational effect together, is approximately equivalent to the vehicle mass [24]. Accordingly, the continuous-time longitudinal dynamics are given byṡ…”

Section: Vehicle Longitudinal Dynamicsmentioning

confidence: 99%

Battery Energy Management of Autonomous Electric Vehicles Using Computationally Inexpensive Model Predictive Control

2020

View full text Add to dashboard Cite

With the emergence of vehicle-communication technologies, many researchers have strongly focused their interest in vehicle energy-efficiency control using this connectivity. For instance, the exploitation of preview traffic enables the vehicle to plan its speed and position trajectories given a prediction horizon so that energy consumption is minimized. To handle the strong uncertainties in the traffic model in the future, a constrained controller is generally employed in the existing researches. However, its expensive computational feature largely prevents its commercialization. This paper addresses computational burden of the constrained controller by proposing a computationally tractable model prediction control (MPC) for real-time implementation in autonomous electric vehicles. We present several remedies to achieve a computationally manageable constrained control, and analyze its real-time computation feasibility and effectiveness in various driving conditions. In particular, both warmstarting and move-blocking methods could relax the computations significantly. Through the validations, we confirm the effectiveness of the proposed approach while maintaining good performance compared to other alternative schemes.

show abstract

“…The most popular instantaneous optimization strategy is ECMS (equivalent consumption minimization strategy), wherein the equivalent consumption of hydrogen in the fuel cell is changed into that of fuel in the battery, and the strategy is used to minimize the equivalent fuel consumption at each sampling time [5,6]. Global optimization strategy, adopting the optimization algorithms such as DP (dynamic programming) [7,8], Pontryagin's minimum principle [9], and pseudospectral method [10], can obtain the global optimal control laws following the certain drive cycle. And in addition to that, Lü et al present a review of energy management system of fuel cell hybrid vehicle from the perspective of heuristic algorithm [11].…”

Section: Introductionmentioning

confidence: 99%

Energy Management of Fuel Cell Vehicles Based on Model Prediction Control Using Radial Basis Functions

Xin

Zheng

Qin³

et al. 2021

Journal of Sensors

View full text Add to dashboard Cite

Energy management strategies can improve fuel cell hybrid electric vehicles’ dynamic and fuel economy, and the strategies based on model prediction control show great advantages in optimizing the power split effect and in real time. In this paper, the influence of prediction horizon on prediction error, fuel consumption, and real time was studied in detail. The framework of energy management strategy was proposed in terms of the model prediction control theory. The radial basis function neural network was presented as the predictor to obtain the short-term velocity in the future. A dynamic programming algorithm was applied to obtain optimized control laws in the prediction horizon. Considering the onboard controller’s real-time performance, we established a simple fuel cell vehicle mathematical model for simulation. Different prediction horizons were adopted on UDDS and HWFET to test the influence on prediction and energy management strategy. Simulation results showed the strategy performed well in fuel economy and real-time performance, and the prediction horizon of around 20 s was appropriate for this strategy.

show abstract

Co-optimization of speed trajectory and power management for a fuel-cell/battery electric vehicle

Cited by 73 publications

References 33 publications

Optimal energy management strategies for the electric vehicles compiling bibliometric maps

Optimal energy management strategies for the electric vehicles compiling bibliometric maps

Battery Energy Management of Autonomous Electric Vehicles Using Computationally Inexpensive Model Predictive Control

Energy Management of Fuel Cell Vehicles Based on Model Prediction Control Using Radial Basis Functions

Contact Info

Product

Resources

About