Battery Monitoring For State-of-Charge And Power Optimization Using LabIVIEW

Battery Monitoring For State-of-Charge And Power Optimization Using LabIVIEW

Mrs. Sarita Chauhan1, Ashish Sharma2 and Pratiti Sharma3

ABSTRACT:

As the transportation industry strives to electrify their vehicles, the onboard power source remains a weak link. Fuel cells and secondary batteries are often considered major candidates for providing the primary motive power or serving as load- leveling devices. Due to the relative maturity of the secondary batteries, much effort by academia and industry is devoted to making batteries re- liable and affordable for the electrification of vehicles. In addition to the development of new batteries with better capacity and power capabil- ity, an advanced battery management system is also required to better utilize the capacity of the batteries and to provide diagnostic information for the benefit of the driver. Unfortunately, the internal battery states such as energy remaining are not readily available for direct monitoring. The development of a battery monitoring system that accurately estimates the internal states from available external measurements such as voltage and current is thus important. Therefore, here we present a project dealing with the cause aforesaid. In this we shall implement a method to determine the battery state of charge. Battery state of health and state of Function will also be determined as pre-requisites for the purpose. This project uses system identification techniques to implement a monitoring system for lead-acid batteries in an electric vehicle. Specifically, the information that the proposed methodology provides can help estimate the energy remained in the battery bank (State of-Charge (SOC)) and the power capability of the battery bank (State-of-Function (SOF)).Software requirements will be LabVIEW for the Graphical User Interface.

LabVIEW

1. INTRODUCTION

   With the development of new batteries with better capac- ity and power capability, an advanced battery manage- ment system is also required to better utilize the capacity of the batteries and to provide diagnostic information for the benefit of the driver. Unfortunately, the internal bat- tery states such as energy remaining are not readily avail- able for direct monitoring. The development of a battery

   monitoring system that accurately estimates the internal states from available external measurements such as volt- age and current is thus important. Most secondary bat- teries have thin, cylindrical strips for their electrodes. The cylindrical strips are rolled with a separator between the electrode strips and then placed in a cylindrical can. This design tends to achieve a higher electrode surface area that increases the battery power density while lowering the en- ergy capacity due to the increased size of current collector needed to support the electrode . The lead-acid battery technology generally suffers little or no memory effect . Memory effect refers to the restricted capacity that some batteries exhibit when they have been subjected to a par- ticular limited range of capacity use. The lack of memory effect makes this technology a strong candidate for back- up power applications. Lead-acid batteries, however, suf- fer from a relatively low energy density and irreversible capacity loss during deep discharge.

2. Background Work

   As the transportation industry strives to electrify their vehicles, the onboard power source remains a weak link. Fuel cells and secondary batteries are often considered ma- jor candidates for providing the primary motive power or serving as load-leveling devices. Due to the relative matu- rity of the secondary batteries, much effort by academia and industry is devoted to making batteries reliable and affordable for the electrification of vehicles. In addition to the development of new batteries with better capacity and power capability, an advanced battery management system is also required to better utilize the capacity of the batteries and to provide diagnostic information for the benefit of the driver. Unfortunately, the internal battery states such as energy remaining are not readily available for direct monitoring. The development of a battery moni- toring system that accurately estimates the internal states from available external measurements such as voltage and current is thus important.

3. The State-of-the-Art Review

   On studying this chapter we can say SOF online es- timation is based on the information obtained from recent voltage and current measurements. Means if the impedance can be known and the OCV can be treated as constant for the short span of time period then the power capability of the battery can be predicted. The Peukert modification approach attempts to estimate useful energy, thus taking into account SOF, for static

   operating conditions. The emphasis of this work is to establish an adaptive methodology for electric vehicle battery monitoring system. This work proposes to implement the system identification based method on the EV driving cycle, investigate the implementation issues of the estimation system, and present the results of the system in the context of the EV cycle.

4. INTERFACING CARD: NI 6008

   The NI USB-6008 provides connection to eight single- ended analog input (AI) channels, two analog output (AO) channels, 12 digital input/output (DIO) channels, and a 32-bit counter with a full-speed USB interface. The firmware on the NI USB-6008 refreshes whenever the de- vice is connected to a computer with NI-DAQmx. NI- DAQmx automatically uploads the compatible firmware version to the device. When you use a DAC to generate a waveform, you may observe glitches in the output signal. These glitches are normal; when a DAC switches from one voltage to another, it produces glitches due to released charges. The largest glitches occur when the most signifi- cant bit of the DAC code changes. You can build a lowpass deglitching filter to remove some of these glitches, depend- ing on the frequency and nature of the output signal. For more information about minimizing glitches. refer to the

5. State-of-Charge Estimation Methodology

   Now developing the equivalent circuit model based on the chemical processes of lead-acid batteries. The fig. 1 shown below shows the Randles equivalent circuit model. Here two variants of the equivalent circuit, with diffusion and without diffusion, are compared in performance.

   Figure 1: Randles Equivalent Circuit

   The following equation estimates the state-of-charge.

   Qt

   Qt

   SOC = QtQr × 100

   here:

   Qt = Total Charge in Battery

   Qr = Remaining Charge in Battery

6. Model-Bases Battery Power Capability Predic- tion

   In this chapter, the use of the developed model for

   short-term power capability prediction will be discussed. In the discussion on the developed models suitability for short-term power capability, another modeling ap- proach based on frequency spectral separation will be compared with the developed model. The focus will then shift to the performance of the developed model in terms of short-term power capability prediction. The prospect of using the developed model for long-term power

   Capability prediction is also considered in the chapter.

7. Battery Monitoring System

   The system control software is written in LabVIEW. Two main user interfaces exist for programming the desired battery current profile, controller and automation pro- gram interface. The controller interface provides an en- vironment to manually set up the battery activities while providing real-time system monitorng information on a visual panel.

   Figure 2: System Controller Interface

   in this window we estimate the battery voltage and current and provides diffrent controls for battery monitoring.

   The diagram shown below is a snapshot of the controller interface.

   Figure 3: Battery Paramerter Window

   The automation program interface can load a text file written in a certain format and interpret it for commands requested by the user.

   Command mode	Description
   Standby	The relays are off ,includ- ing the supply or load sen- sor relays to prevent unin- tended current flow. the cuurent sensor is zeroed to
   Constant Current	The battery bank supplies or absorbs a fixed amount of current, as long as the voltage limits and other protection constraints are satisfied.
   Constant Voltage	The power supply pro- vides a curret at a fixed voltage , so long as the cr- rent limits and other ppro- tection constraints
   Constant Power	The load has a CP mode while the supply relise on a proportional power
   Current waveform	The waveform genera- tor can produce sine, square,triangle and saw- tooth wave. in addition , the user can uplaod a waveform pattern for any

   Table 1: T1 Command Modes Available in System Con- troller Interface

   Figure 4: Battery Charge Discharge Window

   The command modes available for the battery current are listed in Table T1.

   To ensure safety, the automated battery testing system actively monitors the battery bank and terminates the operation if any of the pre set conditions are met. Specifically, the following table lists the constraints that the system monitors.

8. Results

   A thorough state-of-the-art review of BMS technologies to provide SOC, SOH, and SOF information for the user has been conducted. The primary contributions of this project are summarized as follows:

   Following window is the estimation voltage and current

   curve of lead acid battery.

   Figure 5: Battery Voltage and Current Curves

   Below window is the estimation of remaing power of lead acid battery.

   Figure 6: Estimated Remaining Power

   The simulation results are presented . The voltage track- ing performances are first compared in Fig. 7 and Fig. 8.

   Therefore, further determining the remaining SOC, SOH and SOF for a battery.

   Figure 7: SOC, SOH and SOF tester

   Connection profile and formula SOC, SOH and SOF for a battery.

   Figure 8: Connection With NI 6008

   In the above window interfacing with NI 6008 and basic terms equations which determine the SOC,SOH and SOF is shown.

9. Conclusions

State-of-charge, state-of-health and state-of-function is es- timated. Also the approximate time for which the battery can work under a load would be determined. This idea has a great deal of potency and can be used to determine the batteries with higher potential beforehand. This can be further enhanced to provide online battery monitoring in industrial applications. It can be extended to be used in industries where simultaneous discharging of batteries is done before charging again.

9. Future Aspects :

Many other possible improvements to the proposed method were considered.In which first one consider tem- perature effect.By varying temperature battery perfor- mance also change.

The lead acid battery is an electrochemical device. Heat accelerates chemical activity; cold slows it down. Nor- mal battery operating temperature is considered to be 77F (25C). Higher-than-normal temperature has the following effects on a lead acid battery: Increases performance, In- creases internal discharge or local action losses, Lowers cell voltage for a given charge current , Raises charging current for a given charge voltage Shortens life, Increases water usage, Increases maintenance requirements.

Lower than normal temperatures have the opposite effects. In general, at recommended float voltage, a battery in a cool location will last longer and require less maintenance than one in a warm location.

If the operating temperature is something other than 77F (25C), it is desirable to modify the float voltage as follows: For temperatures other than 77F (25C), correct float volt- age by 2.8 mV/F (5.0 mV/C). Add 2.8 mV (0.0028 Volt) per F (5.0 mV/C) below 77F (25C).

Figure 9: Performance of battery with temperature

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