19 research outputs found
A Model-Based Approach for Voltage and State-of- Charge Estimation of Lithium-ion Batteries
Electric vehicles are equipped with a large number of lithium-ion battery cells. To achieve superior performance and guarantee safety and longevity, there is a fundamental requirement for a Battery Management System (BMS). In the BMS, accurate prediction of the State-of-Charge (SOC) is a crucial task. The SOC information is needed for monitoring, controlling, and protecting the battery, e.g. to avoid hazardous over-charging or over-discharging. Nonetheless, the SOC is an internal cell variable and cannot be straightforwardly obtained. This paper presents a Kalman Filter (KF) approach based on an optimized second-order Rc equivalent circuit model to carefully account for model parameter changes. An effective machine learning technique based on Proximal Policy Optimization (PPO) is applied to train the algorithm. The results confirm the high robustness of the proposed method to varying operating conditions
Meetod elektrisõiduki aku laetuse taseme täpsemaks hindamiseks
The electric vehicle (EV) is a complex, safety-critical system, which must ensure the
safety of the operator and the reliability and longevity of the device. The battery management
system (BMS) of an EV is an embedded system, whose main responsibility
is the protection and safety of the high-voltage battery pack. The BMS must ensure
that the requirements for health, status and deliverable power are met by maintaining
the battery pack within the defined operational conditions for the defined lifetime of the
battery. The state of charge (SOC) of a cell describes the ratio of its current capacity
(amount of charge stored) to the nominal capacity as defined by the manufacturer. SOC
estimation is a crucial, but not trivial BMS task as it can not be measured directly, but
must be estimated from known and measured parameters, such as the cell terminal voltage,
current, temperature, and the static and dynamic behavior of the cell in different
conditions. Many different SOC estimation methods exist, out of which (currently) the
most practical methods for implementing on a real-time embedded system are adaptive
methods, which adapt to different internal and external conditions. This thesis proposes
the use of the sigma point Kalman filter (SPKF) for non-linear systems as an equivalentcircuit
model-based state estimator to be used in one of the current series production EV
projects developed by Rimac Automobili. The estimator has been implemented and validated
to yield better results than the currently used SOC estimation method, and will
be deployed on the BMS of a high-performance hybrid-electric vehicle
Review on Battery State Estimation and Management Solutions for Next-Generation Connected Vehicles
The transport sector is tackling the challenge of reducing vehicle pollutant emissions and carbon footprints by means of a shift to electrified powertrains, i.e., battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs). However, electrified vehicles pose new issues associated with the design and energy management for the efficient use of onboard energy storage systems (ESSs). Thus, strong attention should be devoted to ensuring the safety and efficient operation of the ESSs. In this framework, a dedicated battery management system (BMS) is required to contemporaneously optimize the battery’s state of charge (SoC) and to increase the battery’s lifespan through tight control of its state of health (SoH). Despite the advancements in the modern onboard BMS, more detailed data-driven algorithms for SoC, SoH, and fault diagnosis cannot be implemented due to limited computing capabilities. To overcome such limitations, the conceptualization and/or implementation of BMS in-cloud applications are under investigation. The present study hence aims to produce a new and comprehensive review of the advancements in battery management solutions in terms of functionality, usability, and drawbacks, with specific attention to cloud-based BMS solutions as well as SoC and SoH prediction and estimation. Current gaps and challenges are addressed considering V2X connectivity to fully exploit the latest cloud-based solutions
State of charge estimation for lithium-ion batteries connected in series using two sigma point Kalman filters
This paper proposes a method to estimate state of charge (SoC) for Lithium-ion battery pack (LIB) with series-connected cells. The cell’s model is represented by a second-order equivalent circuit model taking into account the measurement disturbances and the current sensor bias. By using two sigma point Kalman filters (SPKF), the SoC of cells in the pack is calculated by the sum of the pack’s average SoC estimated by the first SPKF and SoC differences estimated by the second SPKF. The advantage of this method is the SoC estimation algorithm performed only two times instead of times in each sampling time interval, so the computational burden is reduced. The test of the proposed SoC estimation algorithm for 7 samsung ICR18650 Lithium-ion battery cells connected in series is implemented in the continuous charge and discharge scenario in one hour time. The estimated SoCs of the cells in the pack are quite accurate, the 3-sigma criterion of estimated SoC error distributions is 0.5%
Modelling and estimation in lithium-ion batteries: a literature review
Lithium-ion batteries are widely recognised as the leading technology for electrochemical energy storage. Their applications in the automotive industry and integration with renewable energy grids highlight their current significance and anticipate their substantial future impact. However, battery management systems, which are in charge of the monitoring and control of batteries, need to consider several states, like the state of charge and the state of health, which cannot be directly measured. To estimate these indicators, algorithms utilising mathematical models of the battery and basic measurements like voltage, current or temperature are employed. This review focuses on a comprehensive examination of various models, from complex but close to the physicochemical phenomena to computationally simpler but ignorant of the physics; the estimation problem and a formal basis for the development of algorithms; and algorithms used in Li-ion battery monitoring. The objective is to provide a practical guide that elucidates the different models and helps to navigate the different existing estimation techniques, simplifying the process for the development of new Li-ion battery applications.This research received support from the Spanish Ministry of Science and Innovation under projects MAFALDA (PID2021-126001OB-C31 funded by MCIN/AEI/10.13039/501100011033/ ERDF,EU) and MASHED (TED2021-129927B-I00), and by FI Joan Oró grant (code 2023 FI-1 00827), cofinanced by the European Union.Peer ReviewedPostprint (published version