Energy management strategy based on driving pattern recognition for a dual‐motor battery electric vehicle

Hu, Jianjun; Niu, Xiyuan; Jiang, Xingyue; Zu, Guoqiang

doi:10.1002/er.4474

Cited by 45 publications

(21 citation statements)

References 29 publications

(43 reference statements)

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“…With a prediction accuracy of 84% for driving recognition, a fuel consumption reduction between (9 -17)% was obtained. Subsequently, the context-aware driver behavior system is implemented in a control strategy for an electric vehicle with dual-motor coupled drive-train [111]. This work aims to achieve reasonable mode switching and optimal power allocation between the two motors of the vehicle, under a known driving behavior.…”

Section: Urban Planningmentioning

confidence: 99%

Context Aware Control Systems: An Engineering Applications Perspective

et al. 2020

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Cyber-physical systems revolve around context awareness, empowering objective-oriented services, products and operations based on real data. Self-aware and self-control systems are core elements in the Industry 4.0 framework towards self-sustainable adaptive manufacturing and personalized services. This development is witnessed by the context-aware pervasive assistance to users and machines in decisions making process for optimizing product performance and economic yield. While integration of the virtual and the physical world entails smart sensors communication and complex data analytics, it relies on artificial intelligence tools to manage process operations. The objective of the article is to create awareness that systems & control community must address theoretical and practical aspects from a larger perspective. Context aware control is emerging as a natural solution to maximize the use of available sensing instrumentation and the relatively low cost data logging, i.e. an important source for extracting information, interpreting and using context information and adapt its functionality to the current context of use. This article presents a concise overview of applications where context aware systems and control methodologies are relevant in the seven societal challenges acknowledged by European policy-makers: Digital Society;

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Section: Urban Planningmentioning

confidence: 99%

Context Aware Control Systems: An Engineering Applications Perspective

et al. 2020

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“…12 As an example, the dual motor coupled drivetrains allow the motors to operate in efficient areas by reasonably synthesizing and decomposing the output power. 13 By utilizing two propulsion machines instead of a large central one, the powertrain efficiency could be improved up to 10%. 14 This approach is adopted by the industry and many carmakers such as Audi, BYD, Jaguar, Mercedes-Benz, Tesla, GM, Porsche and Genovation Cars.…”

Section: Introductionmentioning

confidence: 99%

“…As an example, the dual motor coupled drivetrains allow the motors to operate in efficient areas by reasonably synthesizing and decomposing the output power 13 . By utilizing two propulsion machines instead of a large central one, the powertrain efficiency could be improved up to 10% 14 .…”

Section: Introductionmentioning

confidence: 99%

Neural network‐based energy management of multi‐source (battery/UC/FC) powered electric vehicle

2020

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Due to increased environmental pollution and global warming concerns, the use of energy storage units that can be supported by renewable energy resources in transportation becomes more of an issue and plays a vital role in terms of clean energy solutions. However, utilization of multiple energy storage units together in an electric vehicle makes the powertrain system more complex and difficult to control. For this reason, the present study proposes an advanced energy management strategy (EMS) for range extended battery electric vehicles (BEVs) with complex powertrain structure. Hybrid energy storage system (HESS) consists of battery, ultra-capacitor (UC), fuel cell (FC) and the vehicle is propelled with two complementary propulsion machines. To increase powertrain efficiency, traction power is simultaneously shared at different rates by propulsion machines. Propulsion powers are shared by HESS units according to following objectives: extending battery lifetime, utilizing UC and FC effectively. Primarily, to optimize the power split in HESS, a convex optimization problem is formulated to meet given objectives that results 5 years prolonged battery lifetime. However, convex optimization of complex systems can be arduous due to the excessive number of parameters that has to be taken into consideration and not all systems are suitable for linearization. Therefore, a neural network (NN)-based machine learning (ML) algorithm is proposed to solve multi-objective energy management problem. Proposed NN model is trained with convex optimization outputs and according to the simulation results the trained NN model solves the optimization problem within 92.5% of the convex optimization one.

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“…The main purpose of energy management is to realize optimal, adaptive, and real‐time control in the real‐world . The rule‐based strategies can realize real‐time control, while the threshold values should be carefully designed to improve the fuel economy . For example, Reference proposed a rule‐based strategy, where the real‐time control performance was demonstrated by a hardware‐in‐loop verification, and the fuel economy was improved by adjusting the threshold values.…”

Section: Introductionmentioning

confidence: 99%

Self‐learning energy management for plug‐in hybrid electric bus considering expert experience and generalization performance

Guo¹,

Zhao²,

Guo³

et al. 2020

Int J Energy Res

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A self-learning energy management is proposed for plug-in hybrid electric bus, by combining Q-Learning (QL) and Pontryagin's minimum principle algorithms. Different from the existing strategies, the expert experience and generalization performance are focused in the proposed strategy. The expert experience is designed as the approximately optimal reference state-of-charge (SOC) trajectories, and the generalization performance is enhanced by a multiply driving cycle training method. In specific, an efficient zone of SOC is firstly designed based on the approximately optimal reference SOC trajectories. Then, the agent of the QL is trained off-line by taking the expert experience as reference SOC trajectories.Finally, an adaptive strategy is proposed based on the well-trained agent. Specially, two different reward functions are defined. That is, the reward function in the off-line training mainly considers the tracking performance between the expert experience and the SOC, while mainly considering the punishment in the adaptive strategy. Simulation results show that the proposed strategy has good generalization performance and can improve the fuel economy by 22.49%, compared to a charge depleting-charge sustaining (CDCS) strategy. K E Y W O R D Senergy management, expert experience, generalization performance, plug-in hybrid electric bus, Q-learning

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Energy management strategy based on driving pattern recognition for a dual‐motor battery electric vehicle

Cited by 45 publications

References 29 publications

Context Aware Control Systems: An Engineering Applications Perspective

Context Aware Control Systems: An Engineering Applications Perspective

Neural network‐based energy management of multi‐source (battery/UC/FC) powered electric vehicle

Self‐learning energy management for plug‐in hybrid electric bus considering expert experience and generalization performance

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