Fuzzy Logic Based Energy Management Strategy for Battery Life Extension in Solar Integrated Electrical Vehicles

Fuzzy Logic Based Energy Management Strategy for Battery Life Extension in Solar Integrated Electrical Vehicles

S. Kranthi Kumar, K. Satyavathi, M. Vinay, P. Ashok, G. Jagadesh, S. Vijayendra

Department of Electrical Engineering, Raghu Engineering College Dakamarri, Affiliated under JNTU-GV, Vizianagaram, India

Abstract – This research addresses the critical lim- itation of Lithium-ion batteries in Electric Vehicles (EVs): rapid degradation due to high-current transients during aggressive drive cycles. We propose a Hybrid Energy Storage System (HESS) that integrates a high- energy density battery with a high-power density super- capacitor, orchestrated by an intelligent Fuzzy-Logic- based Power Management Unit (PMU). To ensure sus- tainability, a solar Photovoltaic (PV) array is integrated into the vehicle shell, regulated by an Incremental Con- ductance Maximum Power Point Tracking (MPPT) algo- rithm. A High-Precision Fuzzy Logic Controller (FLC) with a 35-rule base is developed to decouple the steady- state and transient power demands. Simulation results in MATLAB/Simulink demonstrate a 65% reduction in battery peak current, maintaining a stable 1.25C dis- charge rate even during 7C peak demand phases. This results in a 40% decrease in battery thermal loading and an estimated 2.5x increase in cycle life, providing a robust, high-density solution for next-generation solar- integrated EV powertrains.

Index TermsBLDC Motor, HESS, Super-capacitor, Fuzzy Logic Control, MPPT, Solar PV, Battery Life Ex- tension, Power Management Unit (PMU), EEE Final Year Project.

1. INTRODUCTION

   The global paradigm shift toward zero-emission transportation has positioned Electric Vehicles (EVs) as the cornerstone of the modern automotive indus- try. However, the commercial viability and consumer acceptance of EVs are heavily dictated by the perfor- mance, cost, and lifespan of the Energy Storage Sys- tem (ESS). Lithium-ion (Li-ion) batteries are currently the industry standard due to their high energy den- sity. Despite this, they face significant physiological challenges when subjected to the highly dynamic load profiles characteristic of urban driving.

   1. Problem Statement

      During rapid acceleration or regenerative braking, Li-ion batteries are subjected to high-magnitude cur- rent pulses. These transients lead to:

      1. Ohmic Heating: Proportional to I2R, causing localized hotspots within the cells.
      2. SEI Layer Growth: Rapid ions movement facil- itates the growth of the Solid Electrolyte Inter- phase layer, increasing internal resistance.
      3. Voltage Sag: High discharge rates cause signif- icant terminal voltage drops, reducing overall drive efficiency.
   2. Proposed Solution: Solar-HESS

   This research proposes a Solar-Integrated Hybrid Energy Storage System (HESS). By augmenting the battery with a super-capacitor (SC), we can effectively shave the peaks of the current demand. The super- capacitor handles the high-frequency power compo- nents (transients), while the battery provides the low- frequency energy component (cruising). A vehicle- integrated PV system provides auxiliary energy, re- ducing the depth of discharge (DoD) of the primary battery.

2. LITERATURE SURVEY
   1. HESS Topologies and Control

      Khaligh and Li (2010) provided a comprehensive taxonomy of HESS configurations.

      • Passive HESS: Direct parallel connection. Simple but lacks energy flow control.
      • Semi-Active HESS: One source is decoupled via a DC-DC converter. Offers partial control.
      • Active HESS: Both sources (or the transient source) are fully regulated via bidirectional con- verters.

        Our research adopts the Active Configuration for the super-capacitor, utilizing a bidirectional buck-boost converter to ensure the SC can absorb regenerative energy and provide boost current independently of the batterys state.

   2. Renewable Integration in EVs

   Solar integration presents a unique challenge due to partial shading and fluctuating irradiance. Singh and Mishra (2025) highlighted that traditional Perturb and Observe (P&O) algorithms often fail under fast- changing cloud cover. This paper utilizes the Incre- mental Conductance (IC) method, which offers supe- rior tracking speed and stability for vehicle-mounted PV systems.

3. MAThEmATIcAl ModElINg ANd ANAlYsIs

   A. Solar PV and IC-MPPT Modeling

   The solar cell is modeled using the equivalent single-diode circuit. The terminal current Ipv is:

   I = I I exp q(V + IRs) 1 V + IRs (1)

   B. The 35-Rule Inference Matrix

   The rule base (Table I) is designed to prioritize SC usage during transients. When acceleration is high (PB), the SC duty cycle is maximized to shield the

   battery.

   e /e	NB	NM	NS	Z	PS	PM	PB
   NB	NB	NB	NB	NB	NM	NS	Z
   NM	NB	NB	NB	NM	NS	Z	PS
   Z	NB	NM	NS	Z	PS	PM	PB
   PM	NS	Z	PS	PM	PB	PB	PB
   PB	Z	PS	PM	PB	PB	PB	PB

    

   ph 0 nKT Rp Table I: Comprehensive Fuzzy Rule Base for PMU The Incremental Conductance algorithm operates on the

   principle that the derivative of power with re- spect to

   voltage is zero at the MPP:

   By comparing the instantaneous conductance (I/V ) with the incremental conductance (dI/dV ), the con- troller adjusts the duty cycle to maintain maximum power output.

   B. Active HESS Architecture

   The HESS consists of a Li-ion battery pack and a Maxwell super-capacitor module. The SC voltage Vsc and current Isc are governed by the bidirectional converter dynamics:

   disc

   The State of Charge (SOC) of the super-capacitor is a critical control input, defined as:

   C. BLDC Motor State-Space Representation

   To achieve high-precision control, the BLDC motor is modeled in the state-space domain. The phase voltage equations are:

   The mechanical motion is coupled via the electromag- netic torque Te:

   1. SIMULATION AND DETAILED RESULTS

      The proposed system was implemented in MAT- LAB R2023b. A UDDS (Urban Dynamometer Driving Schedule) profile was utilized to test the system under realistic conditions.

      1. Transient Response Analysis

         At t= 0.2s, a sudden load of 10Nm was applied.

         The FLC immediately detected the high e and com- manded the super-capacitor converter to discharge at maximum capacity.

4. PROposEd FUzzY LogIc PMU DEsIgN

The Power Management Unit (PMU) is the brain of the HESS. Traditional PID controllers are insuffi- cient for HESS due to the high non-linearity of the battery-SC interface and the unpredictable nature of road loads.

A. Fuzzification and Membership Functions

Two primary inputs are used:

1. 1. Speed Error (e): The difference between refer- ence and actual RPM.
   2. Change in Error (e): Representing the acceler- ation demand.

      Seven linguistic variables (NB, NM, NS, Z, PS, PM, PB) are assigned to each input using triangular member- ship functions for high computational efficiency on embedded EV controllers.

      Figure 1: BLDC Motor Speed response. The rise time is 0.3s with a negligible overshoot of 1.2% due to Fuzzy damping.

2. Battery Life Extension Metrics

The battery current was monitored throughout the cycle. In a battery-only system, the peak current reached 140A. With the Solar-HESS PMU, the peak battery current was restricted to 25A.

Quantitative results show:

• Peak Current Reduction: 65%.
• Thermal Loading Reduction: 40% decrease in internal temperature rise.
• Cycle Life Estimation: Based on the Wh- throughput model, the battery life is projected to increase from 1,500 to 3,750 cycles.

1. L. Zhang et al., A review of supercapacitor mod- eling, estimation,

   and applications, Renew. Sus- tain. Energy Rev., 2018.

2. Singh and Mishra, A solar PV-based compact EV charging solution,

   IEEE J. Photovolt., 2025.

3. Podder et al., Control strategies of different hy- brid energy storage

   systems, IEEE Access, 2021.

4. J. Wang et al., Review of bidirectional DC-DC converter topologies for

HESS, 2022 IEEE Inter- national Conference on Mechatronics.

Figure 2: Battery Electrical Parameters. The cur- rent waveform is flattened by the super-capacitors peak- shaving action.

1. DISCUSSION ON SOLAR INTEGRATION

   The Solar PV system provided an average of 350W during the simulation. While this is insufficient for full propulsion, it serves two critical roles: 1. Parasitic Load Compensation: Powers the vehicle electronics and FLC controller. 2. Resting Charge: During vehicle idle periods, the solar energy per- forms a trickle charge, maintaining the battery SOC and reducing the need for grid-based charging.

2. CONCLUSION AND FUTURE WORK

This research demonstrates that a Fuzzy-Logic- based HESS is a viable and superior alternative to conventional single-source EV powertrains. By effec- tively decoupling high-frequency transients from the battery, the proposed PMU ensures a stable discharge environment, significantly extending the chemical life of the Li-ion cells. The inclusion of solar PV adds a layer of energy resilience.

A. Future Research Directions

Future work will involve the integration of Artifi- cial Neural Networks (ANN) for terrain-aware energy management. By using GPS data to predict upcoming inclines, the PMU can pre-charge the super-capacitor from the solar array, ensuring maximum power avail- ability for hill climbs.

REFERENCES

1. I. A. Sayed and Y. Mahmoud, Energy Manage- ment of Battery- Supercapacitor Hybrid Storage in Electric Vehicles With Solar Integration: A Review, IEEE Access, vol. 13, 2025.
2. Khaligh and Li, Battery, ultracapacitor, and hy- brid energy storage

   systems for electric vehicles, Journal of Energy Storage, 2010.

3. Glavin et al., Hybrid battery-supercapacitor stor- age system during

   regenerative braking, IEEE Trans. Ind. Electron., 2008.

4. B. Wang et al., Bidirectional three-level cascaded converter with deadbeat control for HESS, IEEE Trans. Transport. Electrific., 2019.