Methodology and Guidelines for Designing Flexible BMS in Automotive Applications

The fragile characteristics of Li-ion batteries lead to the need of battery management system (BMS) to carefully supervise them during the operation. Since there are so many variations in battery configurations, the BMS usually must undergo many iterations of the development cycle, which take a long time to optimize and finalize the design. Previously, many works adopted the idea of modularized BMS to address these issues, but they still have some skeptical issues such as measurement approaches or difficulties in reconfiguration. This paper presents a guideline on the crucial aspects of flexible BMS designs for automotive applications, which aims to reduce time and effort for developing a new BMS for automotive battery pack. The guideline covers some crucial aspects pertaining the automotive BMS hardware implementation, SOC estimation algorithm and its computational performance based on Extended Kalman Filter (EKF) and Luenberger Observer (LO) with 3 levels of Electrochemical model (ECM). All of the tests were carried out in a small-scale microcontroller. It was found that 2-RC ECM gives the best trade-off between SOC estimation accuracy and computational time. While the 3-RC ECM provides 9.5% and 31% higher accuracy than the 2-RC and 1-RC ECM, respectively, but taking 88% and 240% higher computational time than the latter two cases. The optimal speed of the observer poles of LO algorithm are suggested to be in the range of 2-5 times faster than the system poles, which makes the convergence speed to be comparable to the EKF algorithm but is still able to keep the SOC estimation error in the range of 3-5%. These results can be used to make a trade-off between estimation accuracy and computational time, to select the optimal SOC estimation algorithm for onboard BMSs

Lithium-ion battery thermal-electrochemical model-based state estimation using orthogonal collocation and a modified extended Kalman filter

This paper investigates the state estimation of a high-fidelity spatially resolved thermal- electrochemical lithium-ion battery model commonly referred to as the pseudo two-dimensional model. The partial-differential algebraic equations (PDAEs) constituting the model are spatially discretised using Chebyshev orthogonal collocation enabling fast and accurate simulations up to high C-rates. This implementation of the pseudo-2D model is then used in combination with an extended Kalman filter algorithm for differential-algebraic equations to estimate the states of the model. The state estimation algorithm is able to rapidly recover the model states from current, voltage and temperature measurements. Results show that the error on the state estimate falls below 1 % in less than 200 s despite a 30 % error on battery initial state-of-charge and additive measurement noise with 10 mV and 0.5 K standard deviations.Comment: Submitted to the Journal of Power Source

Advances in Batteries, Battery Modeling, Battery Management System, Battery Thermal Management, SOC, SOH, and Charge/Discharge Characteristics in EV Applications

The second-generation hybrid and Electric Vehicles are currently leading the paradigm shift in the automobile industry, replacing conventional diesel and gasoline-powered vehicles. The Battery Management System is crucial in these electric vehicles and also essential for renewable energy storage systems. This review paper focuses on batteries and addresses concerns, difficulties, and solutions associated with them. It explores key technologies of Battery Management System, including battery modeling, state estimation, and battery charging. A thorough analysis of numerous battery models, including electric, thermal, and electro-thermal models, is provided in the article. Additionally, it surveys battery state estimations for a charge and health. Furthermore, the different battery charging approaches and optimization methods are discussed. The Battery Management System performs a wide range of tasks, including as monitoring voltage and current, estimating charge and discharge, equalizing and protecting the battery, managing temperature conditions, and managing battery data. It also looks at various cell balancing circuit types, current and voltage stressors, control reliability, power loss, efficiency, as well as their advantages and disadvantages. The paper also discusses research gaps in battery management systems.publishedVersio

Modeling and On-Road Testing of an Electric Two-Wheeler towards Range Prediction and BMS Integration

The automotive sector is currently shifting its focus from traditional fossil fuels to electrification. The deployment of a Battery Management System (BMS) unit is the key point to oversee the battery state of the electric vehicle (EV) to ensure safety and performances. The development and assessment of electric vehicle models in turn lays the groundwork of the BMS design as it provides a quick and cheap solution to test battery optimal control logics in a Software-in-the-Loop environment. Despite the various contribution to the literature in battery and vehicle modeling, electric scooters are mostly disregarded together with a reliable estimation of their performance and electric range. The present paper hence aims at filling the gap of knowledge through the development of a numerical model for considering a two-wheeler. The latter model relies on the conservation energy based-longitudinal dynamic approach and is coupled to a Li-Ion Battery second-order RC equivalent circuit model for the electric range prediction. More specifically, the presented work assesses the performance and electric range of a two-wheeler pure electric scooter in a real-world driving cycle. The e-powertrain system embeds an Electrical Energy Storage System (EESS) Li-Ion Battery pack. On-road tests were initially conducted to retrieve the main model parameters and to perform its validation. A global battery-to-wheels efficiency was also calibrated to account for the percentual amount of available net power for the vehicle onset. The model proved to properly match the experimental data in terms of total distance traveled over a validation driving mission

Holistic Management of Energy Storage System for Electric Vehicles

While electric vehicles (EVs) have recently gained popularity owing to their economic and environmental benefits, they have not yet dominated conventional combustion-engine vehicles in the market. This is due mainly to their short driving range, high cost and/or quick battery performance degradation. One way to mitigate these shortcomings is to optimize the driving range and the degradation rate with a more efficient battery management system (BMS). This dissertation explores how a more efficient BMS can extend EVs' driving range during their warranty periods. Without changing the battery capacity/size, the driving range and the degradation rate can be optimized by adaptively regulating main operational conditions: battery ambient temperature (T), the amount of transferred battery energy, discharge/charge current (I), and the range of operating voltage (min/max V). To this end, we build a real-time adaptive BMS from a cyber-physical system (CPS) perspective. This adaptive BMS calculates target operation conditions (T, I, min/max V) based on: (a) a battery performance model that captures the effects of operational conditions on the degradation rate and the driving range; (b) a real-time battery power predictor; and (c) a temperature and discharge/charge current scheduler to determine target battery operation conditions that guarantee the warranty period and maximize the driving range. Physical components of the CPS actuate battery control knobs to achieve the target operational conditions scheduled by the batteries cyber components of CPS. There are two subcomponents for each condition (T, I): (d) a battery thermal management system and (e) a battery discharge/charge current management system that consists of algorithms and hardware platforms for each sub-system. This dissertation demonstrates that a more efficient real-time BMS can provide EVs with necessary energy for the specified period of time while slowing down performance degradation. Our proposed BMS adjusts temperature and discharge/charge current in real time, considering battery power requirements and behavior patterns, so as to maximize the battery performance for all battery types and drivers. It offers valuable insight into both current and future energy storage systems, providing more adaptability and practicality for various mobile applications such as unmanned aerial vehicles (UAV) and cellular phones with new types of energy storages.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143920/1/kimsun_1.pd

Data driven techniques for on-board performance estimation and prediction in vehicular applications.

L'abstract è presente nell'allegato / the abstract is in the attachmen