Driving style can significantly affect the energy consumption, battery lifespan, and driving
economy of electric vehicles. In this context, this paper proposes a novel driving style-aware …

The uncertainties and disturbances in the actual driving conditions of hybrid electric vehicles
(HEVs) complicate the design of energy management strategy (EMS). To achieve better …

Deep reinforcement learning has emerged as a promising candidate for online optimal
energy management of multi-energy storage vehicles. However, how to ensure the …

As for fuel cell hybrid electric vehicle equipped with battery (BAT) and ultracapacitor (UC), its
dynamic topology structure is complex and different characteristics of three power sources …

Deep reinforcement learning algorithms have been widely applied in the energy
management of hybrid energy storage systems. However, these deep reinforcement …

To achieve optimal real-time power allocation in electric vehicles, a two-layer adaptive
dynamic programming (DP) optimization energy management strategy (EMS) has been …

Driving pattern recognition (DPR) is widely used to improve the robustness of rule-based
energy management strategies (EMS). However, the number and quality of preset patterns …

Vehicles using a single fuel cell as a power source often have problems such as slow
response and inability to recover braking energy. Therefore, the current automobile market …

The escalating level of vehicle electrification and intelligence makes higher requirements for
the energy management strategy (EMS) of fuel cell vehicles. Environmental and road …

In order to fulfill the energy and power demand of battery electric vehicles, a hybrid battery
system with a high-energy and a high-power battery pack can be implemented as the …