A Dual-Mode Energy Management Strategy Considering Fuel Cell Degradation for Energy Consumption and Fuel Cell Efficiency Comprehensive Optimization of Hybrid Vehicle

Optimal energy management strategies for the electric vehicles compiling bibliometric maps

“…The new directions of EVs on actual research studies from 2019 to 2020 are the following: “battery electric vehicles with zero emission, improving lithium‐ion battery energy storage density, safety, and renewable energy conversion efficiency,” 103 “traffic congestion and the waiting time at charging stations, achieved Nash equilibrium substantially improves the load balance across the grid,” 104 “large companies whose supply chain may involve hundreds of commercial vehicles, costs,” 105 “vehicle's acceleration process by controlling the driving behavior, pedal control strategy, updated algorithms,” 106 “bidirectional charging, facilitating islanding and cost‐effective management of main grid use,” 107 “energy‐efficient powertrain requires tackling several conflicting control objectives such as the drivability, fuel economy, reduced emissions, and battery state of charge preservation,” 97 “comprehensive technical review for pure electric vehicles,” 108 “increase the autonomy of the vehicle, as a good self‐ dispatch energy system,” 109 “increase the autonomy of the vehicle, a good self‐dispatch energy system,” 110 “effective approach of DSM based on predetermined hourly generation and time‐varying tariffs to enhance the reliability and quality of a stand‐alone energy system,” 111 “reduce the battery life degradation, battery degradation cost and the electric cost, reduce the energy losses, and handle the system constraints,” 25 “reduce charging waiting time and efficiently design driving behaviors from spots to charging stations, bi‐functional charging management strategy,” 112 “fuel consumption to noise emissions up to battery aging and engine start‐up costs,” 113 “bi‐level online energy management for a battery‐based fuel cell electric vehicle based on operational mode control,” 114 “DPR ‐ wavelet transform‐fuzzy logic control energy management strategy based on driving pattern recognition,” 115 “wavelet transform, neural network and fuzzy logic,” 116 “system constraints, cost function of the model predictive control,” 117 “Improved fuel economy and SOC charge sustainability,” 118 “supervisory control strategy, control framework implementing Model‐based Q‐learning,” 119 “minimize the energy consumption in unknown driving cycles,” 120 “mixed‐integer nonlinear optimal control problem, hierarchical supervisory control architecture,” 121 “DRL's advantages of requiring no future driving information in derivation and good generalization in solving energy management problem,” 122 “fixed models of power sources energy consumption and efficiency,” 123 “multimode power‐split, increased flexibility, predicted fuel consumption and computational cost,” 124 “state‐of‐charge and state‐of‐power capability joint estimator, quantifiable battery degradation model,” 125 “fast rolling optimization for plug‐in hy...…”

Power Allocation Strategy Based on Decentralized Convex Optimization in Modular Fuel Cell Systems for Vehicular Applications

“…In order to solve the modified sub-problems, a regional decomposition framework based on APP approach is suggested in [37]. For the sake of relaxing the coupling between 1 and 2 , − = 0, d, = 1,2 ≠f, and instead of applying standard Lagrangian technique, linearized augmented Lagrangian technique is applied to (14) to aid the convergence speed [38]. The new quadratic function does not change the optimal result although the decomposition of the coupled C-PAS considerably improves the convergence speed [39].…”

“…To minimize this cost, it is required to define a well-developed multi-objective power allocation strategy (PAS). A variety of PASs, such as rule-based [6][7][8], equivalent consumption minimization [9,10], model predictive control [11], adaptive [12,13], dual-mode [14], and heuristic [15,16], have been suggested in the past few decades for the FCVs. Some of these papers have also highlighted the possibility of integrating the prognostic and health management techniques into the design of PASs [17].…”

Implementation and design of an interleaved Cuk converter with selective input current ripple elimination capability

“…To overcome this problem, an auxiliary DC-DC buck-boost converter will be necessary to control and stabilize the output voltage of the sources. [5][6][7][8][9][10][11] However, the converter should have continuous and low ripple input current, which is necessary to connect to renewable energy sources. In a traditional Cuk converter, the energy transfer is capacitive.…”