Optimal Operation of Automotive Electrical System with Photovoltaic Generation and Three-level Battery Management Scheme

For many years, the electrical power requirements in Automotive Electrical System (AES) have been quickly increasing and are predicted to continue to climb. This trend is being pushed by the introduction of a slew of new vehicle features. The constant growth in power needs is stretching the limitations of current automotive power generation and control technologies, stimulating the development of higher-power and higher-voltage electrical systems and components. Electrical power on a vehicle is not free. It comes as a direct result of consuming fuel within the engine to drive the alternator. With a typical engine efficiency of 44% and with present fuel costs this leads to onboard electrical power costs 4 times more than a typical household utility rate. Global oil and gas resource depletion, as well as environmental concerns, have prompted the automobile industry to build more efficient and eco-friendly cars in order to minimize fuel use and safeguard the environment. In our proposed Automotive Electrical system configuration, we have an AES system which is powered by an automotive alternator and battery combination where the alternator is driven by an IC engine and we have a hybrid energy system using a Rooftop PV array with a battery management system (BMS). We discovered that during the off state, the whole load of the automobile is dependent on the 12Vlead acid battery for power, which causes the SOC to drop dramatically. As a result, the suggested model will include a flexible thin-film solar PV module positioned on the rooftop, which will be supported by a Maximum Power Point (MPPT) Tracking charge controller and will deliver energy to recharge the extra battery and meet the electrical requirements when the vehicle is stationary. When the vehicle is in motion, the existing alternator in the car's electrical system takes over the battery charging requirements, by this way, we can meet the electrical requirements of AES without running the engine for a long time by consuming fuel. The proposed model specialty is investigated using MATLAB/Simulink and compared with existing methods. Keywords: Automotive Electrical System (AES), Internal Combustion Engine (ICE), Hybrid Energy System, Rooftop PV array, Maximum Power Point Tracking (MPPT).

Aerodock (a smart, autonomous charging and docking station for unmanned aerial vehicles)

The COVID-19 pandemic has laid bare the need for contactless operations. While unmanned aerial vehicles (UAVs) are being developed to aid humans in countless domains, the need for effective battery management and performance optimization remains a huge task. The proposed solution, the “AeroDock”, aims to tackle these challenges by using wireless power transfer (WPT) technology coupled with smart monitoring of the drone’s health. The performance and hardware checks are assessed at the user end via cloud computing and IoT technology. This system is contact-less, safe, reliable and its usage is not affected by external factors. Thus, the AeroDock is a smart docking station for UAVs which eliminates the need for human intervention in effective charging and maintenance.

Microcontroller-Based Lead-Acid Battery Balancing System for Electric Vehicle Applications

In application of lead-acid batteries for electrical vehicle applications, 48 V of four 12 V batteries in a series configuration are required. However, the battery stack is repeatedly charged and discharged during operation. Hence, differences in charging and discharging speeds may result in a different state-of-charge of battery cells. Without proper protection, it may cause an excessive discharge that leads to premature degradation of the battery. Therefore, a lead-acid battery requires a battery management system to extend the battery lifetime. Following the LTC3305 balancing scheme, the battery balancing circuit with auxiliary storage can employ an imbalance detection algorithm for sequential battery. It happens by comparing the voltage of a battery on the stack and the auxiliary storage. In this paper, we have replaced the function of LTC3305 by a NUCLEO F767ZI microcontroller, so that the balancing process, the battery voltage, the drawn current to or from the auxiliary battery, and the surrounding temperature can be fully monitored. The prototype of a microcontroller-based lead-acid battery balancing system for electrical vehicle application has been fabricated successfully in this work. The batteries voltage monitoring, the auxiliary battery drawn current monitoring, the overcurrent and overheat protection system of this device has also successfully built. Based on the experimental results, the largest voltage imbalance is between battery 1 and battery 2 with a voltage imbalance of 180 mV. This value is still higher than the target of voltage imbalance that must be lower than 12.5 mV. The balancing process for the timer mode operation is faster 1.5 times compared to the continuous mode operation. However, there were no overcurrent or overtemperature occurred during the balancing process for both timer mode and continuous mode operation. Furthermore, refinement of this device prototype is required in the future to improve the performance significantly.

Adaptive Smooth Variable Structure Filter Strategy for State Estimation of Electric Vehicle Batteries

Battery Management Systems (BMSs) are used to manage the utilization of batteries and their operation in Electric and Hybrid Vehicles. It is imperative for efficient and safe operation of batteries to be able to accurately estimate the State of Charge (SoC), State of Health (SoH) and State of Power (SoP). The SoC and SoH estimation must remain robust and accurate despite aging and in presence of noise, uncertainties and sensor biases. This paper introduces a robust adaptive filter referred to as the Adaptive Smooth Variable Structure Filter with a time-varying Boundary Layer (ASVSF-VBL) for the estimation of the SoC and SoH in electrified vehicles. The internal model of the filter is a third-order equivalent circuit model (ECM) and its state vector is augmented to enable estimation of the internal resistance and current bias. It is shown that system and measurement noise covariance adaptation for the SVSF-VBL approach improves the performance in state estimation of a battery. The estimated internal resistance is then utilized to improve determination of the battery’s SoH. The effectiveness of the proposed method is validated using experimental data from tests on Lithium Polymer automotive batteries. The results indicate that the SoC estimation error can remain within less than 2% over the full operating range of SoC along with an accurate estimation of SoH.

A Data-Driven Framework to Predict Lithium-Ion Battery Cell Imbalance for Real-Time Battery Management Systems

Models that can predict battery cells’ thermal and electrical behaviors are necessary for real-time battery management systems to regulate the imbalance within battery cells. This work introduces a Gaussian Process Regression (GPR)-based data-driven framework that succeeds the Multi-Scale Multi-Dimensional (MSMD) modeling structure. The framework can make highly accurate predictions at the same level as full-order full-distribution simulations based on MSMD. A pseudo-2D model is used to generate training data and is combined with a process that shifts computation burdens from real-time battery management systems to lab data preparation. The testing results highlight the reliability of the GPR-based data-driven framework in terms of accuracy and stability under various operational conditions.