Online Battery Cell State of Charge Estimation for use in Electric Vehicle Battery Management Systems
| dc.contributor.advisor | Anbarjafari, Gholamreza, supervisor | |
| dc.contributor.advisor | Avots, Egils, supervisor | |
| dc.contributor.author | Dreija, Kristaps | |
| dc.contributor.other | Tartu Ülikool. Loodus- ja täppisteaduste valdkond | et |
| dc.contributor.other | Tartu Ülikool. Tehnoloogiainstituut | et |
| dc.date.accessioned | 2018-06-14T10:30:16Z | |
| dc.date.available | 2018-06-14T10:30:16Z | |
| dc.date.issued | 2018 | |
| dc.description.abstract | 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. | en |
| dc.identifier.uri | http://hdl.handle.net/10062/60714 | |
| dc.language.iso | eng | et |
| dc.publisher | Tartu Ülikool | et |
| dc.rights | openAccess | et |
| dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Estonia | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/ee/ | * |
| dc.subject | State of charge estimator | en |
| dc.subject | Central difference sigma point Kalman filter | en |
| dc.subject | Battery management system | en |
| dc.subject | Battery cell modelling | en |
| dc.subject | Electric powertrain vehicle | en |
| dc.subject | Laengu eindaja | et |
| dc.subject | Keskmine erinevus sigma-punkt Kalmani filter | et |
| dc.subject | Aku juhtimissüsteem | et |
| dc.subject | Aku modelleerimine | et |
| dc.subject | Elektrisõiduk | et |
| dc.subject.other | magistritööd | et |
| dc.title | Online Battery Cell State of Charge Estimation for use in Electric Vehicle Battery Management Systems | en |
| dc.title.alternative | Meetod elektrisõiduki aku laetuse taseme täpsemaks hindamiseks | et |
| dc.type | Thesis | en |