Design and fabrication of coulomb counter for estimation of SOC of battery

Kotamraju, Sarat K.; Sastry, V.V.; Sekhar, O. Chandra; Mohanta, Dasumanta Kumar; Rajesh, D.; Varma, Mohit

doi:10.1109/pedes.2016.7914473

Cited by 16 publications

(7 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1 to 9 are the coefficients based on the model of the UAV used in the simulation. For the energy calculation model, we use the columb counter technique as described in prior work (Kumar et al, 2016). The simulator computes the total number of columb that has passed over the battery over every cycle.…”

Section: Energy Model In Airsim Pluginmentioning

confidence: 99%

Air Learning: a deep reinforcement learning gym for autonomous aerial robot visual navigation

Boroujerdian

Faust

et al. 2021

Mach Learn

View full text Add to dashboard Cite

We introduce Air Learning, an open-source simulator, and a gym environment for deep reinforcement learning research on resource-constrained aerial robots. Equipped with domain randomization, Air Learning exposes a UAV agent to a diverse set of challenging scenarios. We seed the toolset with point-to-point obstacle avoidance tasks in three different environments and Deep Q Networks (DQN) and Proximal Policy Optimization (PPO) trainers. Air Learning assesses the policies’ performance under various quality-of-flight (QoF) metrics, such as the energy consumed, endurance, and the average trajectory length, on resource-constrained embedded platforms like a Raspberry Pi. We find that the trajectories on an embedded Ras-Pi are vastly different from those predicted on a high-end desktop system, resulting in up to $$40\%$$ 40 % longer trajectories in one of the environments. To understand the source of such discrepancies, we use Air Learning to artificially degrade high-end desktop performance to mimic what happens on a low-end embedded system. We then propose a mitigation technique that uses the hardware-in-the-loop to determine the latency distribution of running the policy on the target platform (onboard compute on aerial robot). A randomly sampled latency from the latency distribution is then added as an artificial delay within the training loop. Training the policy with artificial delays allows us to minimize the hardware gap (discrepancy in the flight time metric reduced from 37.73% to 0.5%). Thus, Air Learning with hardware-in-the-loop characterizes those differences and exposes how the onboard compute’s choice affects the aerial robot’s performance. We also conduct reliability studies to assess the effect of sensor failures on the learned policies. All put together, Air Learning enables a broad class of deep RL research on UAVs. The source code is available at: https://github.com/harvard-edge/AirLearning.

show abstract

Section: Energy Model In Airsim Pluginmentioning

confidence: 99%

Air Learning: a deep reinforcement learning gym for autonomous aerial robot visual navigation

Boroujerdian

Faust

et al. 2021

Mach Learn

View full text Add to dashboard Cite

show abstract

“…We have a battery model that implements a coulomb counter approach [31]. The simulator calculates how many coulombs (product of current and time) have passed through the drone's battery over every cycle.…”

Section: Energy Simulation and Battery Modelmentioning

confidence: 99%

MAVBench: Micro Aerial Vehicle Benchmarking

Boroujerdian

Genç

Krishnan

et al. 2018

2018 51st Annual IEEE/ACM International Symposium on Microarchitecture (MICRO)

View full text Add to dashboard Cite

Unmanned Aerial Vehicles (UAVs) are getting closer to becoming ubiquitous in everyday life. Among them, Micro Aerial Vehicles (MAVs) have seen an outburst of attention recently, specifically in the area with a demand for autonomy. A key challenge standing in the way of making MAVs autonomous is that researchers lack the comprehensive understanding of how performance, power, and computational bottlenecks affect MAV applications. MAVs must operate under a stringent power budget, which severely limits their flight endurance time. As such, there is a need for new tools, benchmarks, and methodologies to foster the systematic development of autonomous MAVs. In this paper, we introduce the "MAVBench" framework which consists of a closed-loop simulator and an end-to-end application benchmark suite. A closed-loop simulation platform is needed to probe and understand the intra-system (application data flow) and inter-system (system and environment) interactions in MAV applications to pinpoint bottlenecks and identify opportunities for hardware and software co-design and optimization. In addition to the simulator, MAVBench provides a benchmark suite, the first of its kind, consisting of a variety of MAV applications designed to enable computer architects to perform characterization and develop future aerial computing systems. Using our open source, end-to-end experimental platform, we uncover a hidden, and thus far unexpected compute to total system energy relationship in MAVs. Furthermore, we explore the role of compute by presenting three case studies targeting performance, energy and reliability. These studies confirm that an efficient system design can improve MAV's battery consumption by up to 1.8X.

show abstract

“…For the batteries to maintain the same voltage at coupling capacitors, the battery Simulink block voltage parameter was set to 582.5 V. Demand of the utility grid was 100 kW. Minimum voltage to be maintained at coupling capacitor had to match the voltage generated by the PV array, i.e., 500 V. Many battery models are available in the literature [27][28][29][30][31][32][33][34][35][36][37][38][39] but not all of them can be used in simulation because of algebraic loop problem. A generic battery model from the MATLAB SimPower Systems library with appropriate parameters was used for analyzing battery charging and discharging performance [40].…”

Section: Battery Bank Descriptionmentioning

confidence: 99%

Chopper-Based Control Circuit for BESS Integration in Solar PV Grids

Rajanna

Kumar

2021

Energies

View full text Add to dashboard Cite

The power delivered by photovoltaic (PV) arrays is dependent on environmental factors, and hence the availability and quality of power delivered by the PV array is low. These issues can be mitigated by integrating a battery energy storage system (BESS) with PV arrays. The integration of the BESS with PV arrays requires controller circuits to regulate power flow between the BESS, PV array, and the load. In this paper, a boost converter-based controller is proposed. The proposed controller has higher reliability and efficiency, and lower operational complexity. It improves the power quality and availability by adjusting the power flow to/from the BESS while delivering the required load power. A simulation study was performed to validate the proposed controller under varying irradiance and temperature of the PV array. The controller was validated against both lithium-ion and lead-acid BESSs.

show abstract

Design and fabrication of coulomb counter for estimation of SOC of battery

Cited by 16 publications

References 5 publications

Air Learning: a deep reinforcement learning gym for autonomous aerial robot visual navigation

Air Learning: a deep reinforcement learning gym for autonomous aerial robot visual navigation

MAVBench: Micro Aerial Vehicle Benchmarking

Chopper-Based Control Circuit for BESS Integration in Solar PV Grids

Contact Info

Product

Resources

About