**7. Monitoring state of health (SOH)**

State of charge and SOH define the most important amounts of charge and rated capacity loss of a battery, respectively. To determine these two parameters instantaneously, VOC Reused Lithium-Ion Battery Applied in Water Treatment Plants http://dx.doi.org/10.5772/intechopen.76303 171

**Figure 9.** A generalized ECM for lithium batteries.

**Figure 7.** RLIB in parallel connection with auxiliary physical battery (ultracapacitor) controlled by EMS.

**6. Real-time simulator for optimizing the sharing ratio between** 

**Figure 8.** Architecture of EMS (symbol B is a safety device for estimating RLIB pack's insulation resistance).

the dynamic response of load, multi-battery pack, and EMS.

**7. Monitoring state of health (SOH)**

Real-time simulators have been widely used in developing and verifying control strategies for power systems. Such devices are a powerful platform before on-board tests. Total analytical modules including EMS module is employed in the simulator. Detailed topology can be found in [41, 42]. In the system level, the control strategy from the vehicle side for the powertrain relating to the area electric range is validated [15]. Through the vehicle side, commands of torque and speed are sent out to the demand side of the motor simultaneously. Likewise, commands for gear shifting commands, the auxiliary system, and protection signals are passed from the vehicle side to other control units. It is originally developed in the environment of OPAL-RT®. An imaginary vehicle module is linked with the simulator via an analog/digital I/O interface, CAN bus, and RS-232. The off-line environment connected to real-time simulator provides sufficient capability for the development of EMS to select the optimized current sharing ratio between LIB and UC. The environment and interface model

State of charge and SOH define the most important amounts of charge and rated capacity loss of a battery, respectively. To determine these two parameters instantaneously, VOC

**RLIB and UC**

170 Energy Systems and Environment

**Figure 10.** A flowchart describes how SoH functions.

**Figure 11.** Comparison of estimated and measured internal resistances (1st, R<sup>s</sup> ; 2nd, R<sup>t</sup> ) and VoC (voltage of open circuit) in test case.

and internal resistance (IR) of the battery are indispensable. To guarantee the safety of RLIB, besides the insulation monitoring function shown in **Figure 8**, a simple, training-free, and easily implemented scheme in EMS is applied. This scheme is capable of estimating VOC and IR, particularly here for RLIB pack [53]. On the basis of an equivalent circuit model (ECM) shown in **Figure 9**, the electrical performance of the battery can be formulated into state-space representation. An underdetermined model's parameters can be arranged linearly so that an adaptive control approach can be applied. An algorithm of adaptive control is developed by exploiting the Lyapunov stability criteria as briefly illustrated in **Figure 10**. VOC and IR can be extracted precisely without limitations of input signals in the system, such as persistent excitation (PE). It enhances the application of this method for power systems. **Figure 11** shows one example for examining the algorithm by using adaptive control observer to estimate VOC and IR through the adaptive control approach. Estimation of SOH-sensitized IR can converge into a stable measured value in about 600(s).
