A New Energy Management Strategy for Multimode Power-Split Hybrid Electric Vehicles

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...…”

MPC-Based Energy Management Strategy for an Autonomous Hybrid Electric Vehicle

“…The equivalent consumption minimization strategy (ECMS) converts the electrical energy consumption to an equivalent amount in actual fuel consumption using the average efficiency of the components [9]. This equivalent factor, however, should be meticulously tuned to achieve near-optimal performance [10], introducing the possibility of using an adaptive approach [11]. Fig.…”

Multi-objective Optimization of Digital Management for Renewable Energies in Smart Cities

“…In 2019, the cities accounted for more than 75% of energy consumed across China. This proportion is expected to surpass 80% in 2029 [1][2][3][4][5][6]. To balance urban development with environment protection and energy consumption, it is necessary to complete the shift from the traditional model of urban construction to the planned construction of a composite system between smartness and energy, along with the rise of smart cities.…”