Intelligent Energy Management Systems for Electrified Vehicles: Current Status, Challenges, and Emerging Trends

Ostadian, Reihaneh; Ramoul, John; Biswas, Atriya; Emadi, Ali

doi:10.1109/ojvt.2020.3018146

Cited by 30 publications

(12 citation statements)

References 112 publications

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“…Due to its simplicity, rule-based EMSs are easily applied in practice [12,13]. However, rule-based EMSs are formulated empirically so that they cannot efficiently cope with all kinds of driving cycles, therefore cannot always ensure efficient control [14,15]. To further enhance fuel economy, the expert knowledge and optimization algorithms are applied to the rule-based EMSs, such as the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm [16].…”

Section: Literature Reviewmentioning

confidence: 99%

Soft Actor-Critic Algorithm-Based Energy Management Strategy for Plug-In Hybrid Electric Vehicle

Cui

2022

WEVJ

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Plug-in hybrid electric vehicles (PHEVs) are equipped with more than one power source, providing additional degrees of freedom to meet the driver’s power demand. Therefore, the reasonable allocation of the power demand of each power source by the energy management strategy (EMS) to keep each power source operating in the efficiency zone is essential for improving fuel economy. This paper proposes a novel model-free EMS based on the soft actor-critic (SAC) algorithm with automatic entropy tuning to balance the optimization of energy efficiency with the adaptability of driving cycles. The maximum entropy framework is introduced into deep reinforcement learning-based energy management to improve the performance of exploring the internal combustion engine (ICE) as well as the electric motor (EM) efficiency interval. Specifically, the automatic entropy adjustment framework improves the adaptability to driving cycles. In addition, the simulation is verified by the data collected from the real vehicle. The results show that the introduction of automatic entropy adjustment can effectively improve vehicle equivalent fuel economy. Compared with traditional EMS, the proposed EMS can save energy by 4.37%. Moreover, it is able to adapt to different driving cycles and can keep the state of charge to the reference value.

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Section: Literature Reviewmentioning

confidence: 99%

Soft Actor-Critic Algorithm-Based Energy Management Strategy for Plug-In Hybrid Electric Vehicle

Cui

2022

WEVJ

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“…48V constant dc link voltage is created by 24 V battery pack and 24.3V UC pack. Parameters of the converters are designed based on the equations [10][11][12][13][14] Modelling equations for battery and UC are represented through [15][16][17] and [18][19][20] respectively. These modelling equations are based on (20).…”

Section: Hess Topology Overviewmentioning

confidence: 99%

“…For controlling the power flow by taking maximum control indicators, shared bus topology is best suited. In (9)(10)(11)(12) one or two control indicators are used along with semiactive topology or multiport topology. In this paper, power filtration (low pass filter-LPF) based conventional rule-based EMS is simulated in MATLAB.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic Controller Based Modified Energy Management Strategy for Battery and UC with Improved Battery Performance

Ranjan¹,

Bodkhe²

2022

ECS Trans.

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The ultracapacitor (UC) acts as a stress reducer for the battery. It is also considered as a fruitful companion for a battery in battery-UC hybrid energy storage system (HESS). Management of power between battery, UC, and drive motor, along with a suitable topology, decides the performance of the battery and drive system. Rule based energy management strategy (EMS) is simple, real-time implementable, ease in control, and based on engineer’s experience. But it lacks in controlling the state-of-charge (SOC) of battery and UC, battery current rate, and its magnitude, dc link voltage control, and UC voltage regulation. In this paper, low pass filter based conventional rule-based EMS is studied and implemented in MATLAB. Then fuzzy logic based modified rule-based EMS is proposed and simulated for the same load profile. Comparative study between conventional and modified EMS shows the proposed EMS is capable to provide constant dc link voltage, stable UC SOC operation, fast response, and reduces the large variation in speed. Proposed EMS saves the 10% of the battery energy for 20 sec load profiles as compared with battery only system. So, by use of UC, shared bus topology, and effective EMS, driving range of the vehicle and battery life can be improved and range anxiety can be reduced.

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“…An electrical energy management system (EEMS) coordinates the energy flows in a vehicle s 12-volt electrical system. Here, the battery and the alternator energy act as energy sources [8]. A widespread strategy is to use predefined rules to briefly deactivate or switch down comfort consumers, such as seat heaters or the air-conditioning fan, in the event of low energy availability.…”

Section: Introductionmentioning

confidence: 99%

Energy Management Strategy in 12-Volt Electrical System Based on Deep Reinforcement Learning

et al. 2022

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The increasing electrification in motor vehicles in recent decades can be attributed to higher comfort and safety demands. Strong steering and braking maneuvers reduce the vehicle’s electrical system voltage, which causes the vehicle electrical system voltage to drop below a critical voltage level. A sophisticated electrical energy management system (EEMS) is needed to coordinate the power flows within a 12-volt electrical system. To prevent the voltage supply from being insufficient for safety-critical consumers in such a case, the power consumption of several comfort consumers can be reduced or switched off completely. Rule-based (RB) energy management strategies are often used for this purpose, as they are easy to implement. However, this approach is subject to the limitation that it is vehicle-model-specific. For this reason, deep reinforcement learning (DRL) is used in the present work, which can intervene in a 12-volt electrical system, regardless of the type of vehicle, to ensure safety functions. A simulation-based study with a comprehensive model of a vehicle electric power system is conducted to show that the DRL-based strategy satisfies the main requirements of an actual vehicle. This method is tested in a simulation environment during driving scenarios that are critical for the system’s voltage stability. Finally, this is compared with the rule-based energy management system using actual vehicle measurements. Concluding measurements reveal that this method is able to increase the voltage at the most critical position of the 12-volt electrical system by approximately 0.6 V.

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Intelligent Energy Management Systems for Electrified Vehicles: Current Status, Challenges, and Emerging Trends

Cited by 30 publications

References 112 publications

Soft Actor-Critic Algorithm-Based Energy Management Strategy for Plug-In Hybrid Electric Vehicle

Soft Actor-Critic Algorithm-Based Energy Management Strategy for Plug-In Hybrid Electric Vehicle

Fuzzy Logic Controller Based Modified Energy Management Strategy for Battery and UC with Improved Battery Performance

Energy Management Strategy in 12-Volt Electrical System Based on Deep Reinforcement Learning

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