Estimation of energy consumption of electric vehicles using Deep Convolutional Neural Network to reduce driver’s range anxiety

Modi, Shatrughan; Bhattacharya, Jhilik; Basak, Prasenjit

doi:10.1016/j.isatra.2019.08.055

Cited by 65 publications

(29 citation statements)

References 28 publications

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“…The NNCs are employed successfully for optimisation of HES energy recovery. For instance, in [20] a convolutional NN estimates EV energy consumption. In [21], a regenerative EB scheme is offered to transfer braking energy to the HES devices.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Control of Green Energy Vehicles with Blended Braking Systems

Vodovozov¹,

Petlenkov²,

Aksjonov³

et al. 2024

RE&PQJ

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A neural network-based control system is offered, which ensures high quality blended braking of the green energy vehicles in both the intensive and the gradual deceleration scenarios, with energy recovery at the changing road pavement. In this study, a neural network controller provides the torque gradient control without a tire model resulting in returning maximal energy to the hybrid energy source during the braking process. To meet the conflicting requirements of different braking modes and road surfaces, an allocation algorithm determines how to distribute the driver’s torque request between the friction and electrical brakes. Simulation demonstrates effectiveness of the proposed braking system. Model states and inputs are used as a guidance to learn a coupled two-layer neural network capable to capture various dynamic behaviours that could not be included in the simplified physics-based model. An experimental part of the research proves the model and simulation validity.

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Section: Introductionmentioning

confidence: 99%

Neural Network Control of Green Energy Vehicles with Blended Braking Systems

Vodovozov¹,

Petlenkov²,

Aksjonov³

et al. 2024

RE&PQJ

View full text Add to dashboard Cite

show abstract

“…Based on the state-of-the-art research, the key factors of EV energy consumption estimation includes both internal and external factors. Thus, in more recent work such as [13], research proposed real-time energy estimation methods based on hybrid factors e.g., vehicle speed, tractive effort and road elevation.…”

Section: Introductionmentioning

confidence: 99%

Fine-Grained RNN With Transfer Learning for Energy Consumption Estimation on EVs

Hua

Sevegnani

et al. 2022

IEEE Trans. Ind. Inf.

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“…The NNCs are applied successfully in HES management to optimize energy recovery. For instance, a convolutional NN has been used in [42] for estimation of energy/power consumption of EV. In [43], a regenerative EB scheme is offered aiming to transfers braking energy to the HES devices.…”

Section: Introductionmentioning

confidence: 99%

Neural Network-Based Model Reference Control of Braking Electric Vehicles

et al. 2021

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The problem of energy recovery in braking of an electric vehicle is solved here, which ensures high quality blended deceleration using electrical and friction brakes. A model reference controller is offered, capable to meet the conflicting requirements of intensive and gradual braking scenarios at changing road surfaces. In this study, the neural network controller provides torque gradient control without a tire model, resulting in the return of maximal energy to the hybrid energy storage during braking. The torque allocation algorithm determines how to share the driver’s request between the friction and electrical brakes in such a way as to enable regeneration for all braking modes, except when the battery state of charge and voltage levels are saturated, and a solo friction brake has to be used. The simulation demonstrates the effectiveness of the proposed coupled two-layer neural network capable of capturing various dynamic behaviors that could not be included in the simplified physics-based model. A comparison of the simulation and experimental results demonstrates that the velocity, slip, and torque responses confirm the proper car performance, while the system successfully copes with the strong nonlinearity and instability of the vehicle dynamics.

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Estimation of energy consumption of electric vehicles using Deep Convolutional Neural Network to reduce driver’s range anxiety

Cited by 65 publications

References 28 publications

Neural Network Control of Green Energy Vehicles with Blended Braking Systems

Neural Network Control of Green Energy Vehicles with Blended Braking Systems

Fine-Grained RNN With Transfer Learning for Energy Consumption Estimation on EVs

Neural Network-Based Model Reference Control of Braking Electric Vehicles

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