Implementation of a P&E management system for a dual-source EV powered by different batteries

ABSTRACT


INTRODUCTION
According to the electric vehicles (EV) policy in India, car density is growing exponentially [1].The application of EV has set a new era in the transportation field the recent innovations in the technologies applied in the storage system and motors [2].Lower battery E density and driving ranges restricted to only hundreds of kilometres currently mesh a widespread use of electric cars.Many scientists and automotive industries are working on battery energy management to increase battery life, rapid recharging, price, and weight reduction [3].Therefore, in battery-powered pure EV, the study focuses primarily on the concept of efficient energy storage [4].The research was carried out to validate the novel energy storage system (ESS) topology using a multisource inverter.Work focusing on the intention of limiting battery peak current and weight, extending battery life, and increasing the driving range for plug-in EV, with the active-controlled parallel operation of a battery and an ultra-capacitor (UC) as energy devices [5]- [7].
A single-stage inverter connects the battery and UC to a three-phase load in the proposed architecture.Driving cycle torque demands reduce battery life due to load transient peak current [8].UCs connected to dynamic response and traditional ESS meet transient load current demands.Battery and UC meet all load and Bulletin of Electr Eng & Inf ISSN: 2302-9285  Implementation of a P&E management system for a dual-source EV powered by … (Mohan P. Thakre)

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P exchange requirements managed by a multi-input innovative inverter [8], [9].Driving requires long-term E and P. The battery supplies constant profile energy, while the UC supplies dynamic peak load profile.This ESS is cheaper and stronger than battery-based ones [10].Hybrid energy storage system (HESS) power simulation (PSIM) uses closed-loop energy monitoring.Open-loop inverter control modes create powersharing and a hardware prototype [11], [12].Battery average current decreases standard case to 27%.The active power exchange between the propulsion system and the ESS extends the electric car's driving range [13], [14].
A hybrid remaining useful life (RUL) prediction model employing long short-term memory (LSTM) and Elman neural networks could monitor long-term battery degradation with higher cycle numbers and depict short-term capacity recovery at certain cycles [15].The author examined lithium-ion battery thermal safety issues, including thermal behaviour, thermal runaway modelling, and battery pack security management rules, taking into account the heat manufacturing system and thermal battery characteristics [16]- [18].Thermal runaway avoidance required heat, electrical, and mechanical model additives in numerous engineering techniques for material refining [19]- [21].HESS optimization sizing the RUL.Peak current needs during transient load demands of the propulsion system degrade system quality and safety [22].The author proposed a storage system with efficient E management using the SC and hybridization in E system [23]- [25].Battery and UC power the electric car.Hybridization improves battery life.UC shares battery dynamic current stress, reducing current pulses a hybridization improves fuel economy 25-50% [22]- [25].Section 2 provides an overview of the various types of EV and how they can be used in the context of contemporary control systems in the form of plug-in EV.In section 3, we model the various energy sources that go into an EV and compare them in terms of their energy density, power density, lifetime, cost, and weight.Before putting into action, the power-sharing capabilities of sources discovered in MATLAB and the PSIM environment, section 4 discusses the simulation study of the HESS.The study's findings are presented in section 5.

COMPARATIVE PERFORMANCE OF ENERGY SOURCES
EVs run on batteries.However, electric drive vehicle batteries possess limited life cycles, power density, charge-discharge rate, and charge time [26].Batteries' discharge rate reduces energy delivery.Battery impedance raises temperature [27].P storage system batteries are crucial.Transient loading challenges battery safety.Battery life is shortened by frequent charging and discharging without cell voltage balancing [26]- [29].
The ESS's battery-UC topology improved battery life, minimising the issues.High P density in the UC ensures proper energy and thermal management, enhancing EV efficiency [30].Ragone plots compare energy source P and E density.This graphic shows that batteries have a higher E density than P density.
UC energy density is significantly lower than P density.UC's lifespan exceeds the batteries.UCs also offer better low-temperature properties than batteries.Ragone plot in Figure 1.Thus, battery-UC operating yields better outcomes.Battery and UC sources define all propulsion system driving cycle requirements.From the Ragone chart, UC has a high P density (6,800 W/kg) but a low E density.However, batteries have high E density (100 to 265 Wh/kg) and low P density.Thus, the combination outperforms each alone.

MODELING OF SOURCES AND BATTERY
During the operation of an EV, the amount of energy that is stored in the storage system and the rate at which energy is extracted are the important factors that need to be considered in order to characterise the  ISSN: 2302-9285 Bulletin of Electr Eng & Inf, Vol. 12, No. 6, December 2023: 3190-3201 3192 energy management system.This is done in order to meet all of the specific energy demands and specific P demands that are imposed by driving cycles.In this section, the operation and modelling of a P source (UC), energy source (battery), and multisource inverter are broken down in detail, key terminology from the ESS is discussed.

Sources modelling
The internal resistance of source-the physical resistance of the electrolyte electrodes offers opposition to the flow of current, known as the internal resistance of the source, as shown in Figure 2. Operating temperature and the state of the charge depends upon the internal resistance of the energy source.The efficiency of a source can be improved by maintaining less internal resistance.Expression of Kirchhoff's voltage law to the equivalent circuit of source given by as (1): Where _ is terminal voltage,   is internal resistance, and   is open circuit voltage (E.M.F).

Figure 2. Internal resistance of sources
The ratio of discharge energy to the total mass of the battery is denoted as the specific energy of the battery as in (2).Actual energy extracted from the source depends upon discharge current and thus specific energy.
In (3) indicates the ratio of discharge P and mass of the battery is known as a specific P of the battery.
This parameter also serves as a P level obtained from the battery.Battery capacity depends upon charges accumulated on the negative/positive electrode, which is measured in Coulombs (C) and expressed in terms of Ampere hour (Ah) (1 Ah=3,600 C).Energy capacity depends upon discharging current, presented in (4) with '  .' Where  / is charging/discharging current,  is battery capacity in Ah,  is multiplication factor of C, and  is C rate.For example, a battery with 16 Ah for 10 hr discharge at a current of 8 Amp, is expressed as 0.5 C10.As C-rate increases, C decreases.Charge battery not used for a long period, the battery discharges by losing some amount of charges.Discharging of a cell depends upon the temperature of cells, the slow decomposition of electrodes, and the release of gas.This is expressed in percentage/24 hr shown in (5): Where   is coefficient of self-discharge for 24 hr, and   is nominal energy capacity in Wh.
In (6) shows the ratio of Ah discharge to complete recharge:

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Useful to compare various types of energy sources.Battery energy efficiency is defined as the ratio of energy delivered from a particular state of charge (SOC) to energy consumed to maintain the same SOC.Higher battery efficiency is obtained by limiting rapid P discharges.

Battery modelling
For a discharge time   , the battery discharge energy   , shown in (7) which is a function of open-circuit voltage (E.M.F.)   , with internal resistance   and constant discharge current   .
Battery charging time was selected as   =   with the same magnitude of discharging current   and battery discharge energy   shown in (8): Battery efficiency is expressed as the ratio of discharging energy to the charging energy shown in (9): In 1897 W. Peakert formulated empirical relation between capacity (Q), discharging current (), and time (t) shown in (10): Where  is discharge current,   is time taken to reach the satisfactory voltage,  is curve fitting constant (n is 1 small currents, n is 2 large currents).
The relation can be derived as in (11): From (11) we can form discharge current in (12): In (13) indicates, battery SoC corresponds to real-time battery capacity (remaining capacity): In (14) indicates, state of discharge (SoD) is: Battery capacity can be expressed in another way called Depth of discharge as in (15): By deriving () obtained as in (16): The more practical equation to calculate discharge time obtained from manufacturers is given as in (17): Where C is nominal capacity, ℎ is hour rating,  is discharge current, and battery capacity in (ℎ)  ., practically SoC obtained by (18): In hybrid source energy management systems UC modeling is very important to optimize the size of the storage system.

Maximum discharging power
Multiplication of maximum discharging current and minimum allowable voltage gives the value of maximum discharging P. Maximum discharging P due to current expressed as in (19): Maximum discharging P as given in (20): Where   is the minimum allowable voltage.

Maximum charging power
In (21) indicates maximum charging P which depends upon maximum charging voltage and current.Charging rates always maintained less than the charge accumulation rate.

𝑉 𝐵𝑎𝑡𝑀𝑎𝑥 = maximum charging voltage
Using cell balancing overcharging of the cell is prevented.By limiting maximum charging P, the life of the cell will be extended.

RESULT AND DISCUSSION
Steps involve modeling and simulating any system, i.e., representing the system with mathematical relationships, finalizing variables of input and output, selecting the correct model, and interpreting and validating the system.To comprehend the UCs charging and discharge features and electrical behavior, a simulation is conducted as follows in the MATLAB and PSIM environment.To validate the UCs dynamic behavioral response, the simulation is conducted in MATLAB/Simulink software, where the battery is attached directly to the DC link, and the UC is connected to the DC link via a dc/dc converter.A simulation for DC/DC converter based on UC/battery design has been chosen to recognize the fundamental significance of hybridization.In this design, current sharing is observed with the gradually increased resistive load over time.Batteries (12 V (VB), 50 Ah) are chosen with a UC of 12 V (VU), 20 Farad.With time delays, three resistive loads are gradually added and connected for loading purposes, and control switches are equipped for low charge protection.The model shown in Figure 3 is achieved in MATLAB/Simulink from the Simscape tool.The total simulation time is selected as 1,000 s.
For different time steps the resistive load gradually enters the circuit.The UC (12 Farad) and the battery share the peak load during the dynamic load variation.The outcome shown that the discharge of the battery begins at 320 sec.with a tiny decrease in the voltage of the battery owing to the internal battery resistance; the current is 2 A. UC shares the dynamic shift in load demand, thus preserving the DC link voltage by restricting the current of the battery.In simulink model, it is seen that the UC provides the peak P (at t=300 s) that limit the battery currently in the event of dynamic peak P need of the load as shown in Figure 4.
The PSIM simulation comprises an ac load such as induction motor (IM) fed inverter as shown in Figure 5, in which the suggested novel inverter is connected to an AC link.The source of the battery 24 V is linked to the inverter-1, while the UC (300 Farad, 24 volts) is attached to the inverter-2.The results demonstrate the P-sharing behavior of the battery and UC during a dynamic change in load.
Using a simulation of the PSIM script file, a space vector analogy is used.Control signals are acquired using the script control block.Current and torque feedback is obtained from the sensors.As an optimal battery and UC, the DC sources are chosen for simulation.A multi-source hybrid inverter is designed with insulated gate bipolar transistors (IGBTs) based on semiconductors.A 3-ph IM characterizes the load.Since the IM itself is the balanced RL load, the current sensor senses from one of the phases; taco generator is also used to sense the shaft speed.The selected period of simulation is 2 sec.This simulation demonstrates active and reactive (P-Q) load control.In view of the inductive load (RL), the motor's P factor lags.The outcomes denote the P&Q requirements of the load.a. Positive P is the power injected into the load from the DC bus.b.In Var, Q with positive magnitude is Q being injected into the load.c.In any combination where negative magnitudes inject P and Q from the load into the DC bus, bidirectional P flow is possible.In this mode, battery drives the load and an UC is not used.Battery connected to the inverter, which converts DC to AC and drives the load alone.Battery current and voltage are measured with scope.In Figures 6(a) and (b) are shown inverter-1 output voltage and current respectively.In mode-1 of Figure 6, the motor is supplied by a 24 V battery via an inverter.To get these findings, we run a 1 s model of inverter mode.As such an outcome of evidence suggesting that the P is in the positive direction of the graph, P and Q are supplied into the motor as indicated in Figures 7(a

CONCLUSION
Attributable to these simulation results, a feasible hybrid (multisource) inverter topology for HESS has been proposed.One key benefit of this topology is that no intermediate stages are required between the P grid/motor and the energy storage units.The efficiency of EV can be increased through the use of a novel multisource connection that better fulfils the load's P demand.Smooth current sharing and reduced average currents are additional benefits of using a multisource inverter.On the other hand, an ESS can increase its efficiency by cutting down on the size and cost of the converter by having the battery directly drive the load (a motor/power system) without performing a boost operation.THE SVPWM-based control strategy allows for P&E transfer between multiple sources during the dynamic load demands of the driving cycle, which increases load power stability.

Figure 1 .
Figure 1.Ragone chart Implementation of a P&E management system for a dual-source EV powered by … (Mohan P.Thakre)

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