This research proposes a method for estimating the residual capacity of Li-ion batteries. The charging and discharging characteristics of Li-ion batteries are investigated and analyzed by a battery test system. The measurement of the initial capacity is based on the improved open-circuit voltage measurement, which compensates the effects of battery aging and self-discharging. The measurement of the used capacity is based on the improved coulomb counting measurement, which compensates the effects of output current and environmental temperature. The designed system provides various functions for battery charging and discharging, battery voltage measuring and recording, battery capacity estimation and calculation, and the log files can be used for further battery characteristics analysis.

The topic of this study is to develop a battery capacity management system. The main purpose is to monitor the state of battery during charging and discharging. Form this, user can know the battery status and to avoid loss of data before sudden system power down caused by a spent battery. Different states of battery were collected in different conditions by a battery measurement system, after which characteristics were analyzed. A fast-charge and residual capacity estimation system was developed according to the battery characteristics. The fast-charge system is a technique that emphasizes not only fastness charging but also safety. In this study a fast-charge end method was adopted to terminate the fast charging state of the battery and the initial state had been estimated before charging. Furthermore, the battery was charged with the optimum method according to the battery initial state. That can recover the capacity of the battery within a short period without causing any side effects from repeated usage. The residual capacity estimation system works by first estimating the initial capacity of the batteries, and then recording the current of batteries continuously using the coulomb counting method to make compensation for the effects of battery aging, environmental temperature, self-discharging, and output current.

In this paper, a state of charge (SOC) and a state of health (SOH) estimation method for lead-acid batteries are presented. In the algorithm the measurements of battery’s terminal voltage, current and temperature are used in the process of SOC calculation. The thesis was written in cooperation with Micropower AB. The algorithm was designed to fulfill the specific requirements of the electric vehicles application: an error below 5% of SOC, computational simplicity and the possibility of being implemented in a basic programming languages. The current used method at Micropower, Coulomb counting, is compared with a method presented by Chiasson and Vairamohan 2005 based on modified Thevein circuit during charging and discharging of the battery. The Thevenin based method gave better result compared to Coulomb counting but seems not to fulfill Micropowers requirements. A correction method based on periods of no charging or discharging, possible to be used together with Coulomb counting as well as with the Thevenin method was developed. The evaluation method indicates that when using also the correction method the Micropowers requirements are fulfilled. / I detta papper, är ett laddningstillstånd (SOC) och hälsotillstånd (SOH) skattningsmetod för blybatterier presenteras. I algoritmen mätningarna av batteriets polspänning, ström och temperatur används i processen för SOC beräkning. Avhandlingen är skriven i samarbete med Micropower AB. Algoritmen har utformats för att uppfylla de särskilda kraven för elektriska fordon: ett fel under 5% av SOC, computational enkelhet och möjligheten att genomföras i ett grundläggande programmeringsspråk. Den nuvarande metoden vid Micropower, Coulomb räkning, jämförs med en metod som presenteras av Chiasson och Vairamohan 2005 baserad på modifierad Thevein kretsen under laddning och urladdning av batteriet.

In this paper, an algorithm for estimating lead-acid battery state of charge (SOC) is implemented. The algorithm, named “Improved Coulomb Counting Algorithm”, was developed within a master thesis project (M. M. Samolyk & J. Sobczak, “Development of an algorithm for estimating Lead-acid Battery State of Charge and State of Health”, M.S. thesis, Dept. Signal Processing, Blekinge Institute of Technology, Karlskrona, Sweden, 2013) with cooperation of a Swedish company – Micropower – Research and Development department.  Currently used method at Micropower is Coulomb Counting; implemented algorithm compares coulomb counting method with open circuit voltage method and uses current, terminal voltage and temperature measurements to finally produce improvement for the very same coulomb counting method and get a better estimation of SOC.  The algorithm was implemented on Micropower Access Battery Monitoring Unit (BMU) using C programming language, so that it can be tested in real time application of the regular battery operation. In the end specific gravity measurements were also presented to comparing the methods.