Battery State of Charge Management for an Electric Vehicle Traction System

*Ahmed Sayed Abdelaal Abdelaziz*

### **Abstract**

This chapter introduces a battery state of charge (SOC) management technique designed for an electric vehicle traction system that incorporates an indirect fieldoriented induction motor drive. The primary goal of this technique is to restrict the change in battery SOC from exceeding a maximum limit, by compensating for the motor speed tracking performance. It employs a fuzzy-tuned model predictive controller (FMPC), where a fuzzy logic controller (FLC) adjusts the input weight in the objective function to ensure that the change in battery SOC does not exceed the maximum permitted value while regulating the motor speed. The various components of the EV traction system are thoroughly modeled, and simulations are conducted using MATLAB/Simulink 2018b. The simulation results, carried out using the New European Drive Cycle (NEDC), verify that the technique limits the change in SOC while controlling the motor speed. This approach offers the advantage of maintaining precise control over the battery bank SOC, which distinguishes it from conventional speed regulators.

**Keywords:** model predictive control, fuzzy logic control, fuzzy weight tuning, state of charge management, electric vehicle Modeling, field oriented control, induction motor

### **1. Introduction**

One of the primary concerns associated with electric vehicles (EVs) pertains to their limited operational range. Additionally, the shortage of charging infrastructure and the extended charging duration remain significant challenges. In addressing these challenges, this chapter aims to shed light on a range of battery energy management (BEM) strategies outlined in existing literature while also introducing an innovative technique that holds promise for the EV market.

Regarding EVs, the BEM strategies can be segregated into two categories [1]. The first category involves the development of rules before initiating the system. Those rules dictate the behavior of the system during operation. Strategies falling within the second category are distinguished by their cost function and require an optimization technique to achieve the system's objective. In the literature, the first category of strategies involves the use of a fuzzy logic controller (FLC) for managing multiple

power sources such as combustion engines, ultra-capacitors, and batteries [2]. An FLC allocates power demand among these sources to maximize each source's efficiency. In an alternative approach, an FLC is designed to consider battery SOC, input reference speed, and commanded vehicle acceleration to determine the battery's power output, albeit with a trade-off of sacrificing a certain degree of motor performance to achieve battery energy conservation [3]. In the works [4], an advanced energy management system was developed. This system oversees both the torque signal and the SOC of the battery, subsequently generating the electric throttle signal to control the motor's speed. Additionally, in Suhail et al.'s study [5], a neural FLC was introduced for efficient management of regenerative braking in a hybrid EV. This controller continuously monitors the engine's speed and power, while accurately calculating the necessary torque for the given situation [5]. When the delivered power surpasses the required amount, the regenerative braking system initiates the process of charging the battery bank using the surplus power generated by the engine [5].

Among the strategies in the second category, dynamic programming (DP) is the most frequently employed optimization technique due to its ability to settle on the optimal solution [6]. In order to reduce the computational complexity, alternative approaches, such as coupling convex programming with a model predictive controller (MPC), can achieve a sub-optimal solution. Furthermore, the equivalent consumption and minimization strategy (ECMS) also obtained a sub-optimal solution [6].

The MPC-based BEM techniques primarily focus on solving receding horizon algorithms, predicting velocity profiles, and generating SOC reference trajectories. An adaptive ECMS (A-ECMS), and a fuzzy adaptive ECMS (Fuzzy A-ECMS) were compared [6]. They improve upon the original ECMS by dynamically estimating the optimal equivalent factor online, in contrast to the static value set by the user in ECMS. They continuously evaluate the current battery SOC against the desired SOC and adjust the optimal equivalent factor accordingly to minimize errors. The Fuzzy A-ECMS technique showed more robustness to various driving conditions as compared to the A-ECMS technique. In Ref. [7], an FLC monitors changes in the battery's first and second derivative of SOC and generates an input weight *R* for the MPC cost function. When sudden high acceleration occurs, an increase in *R* prompts the MPC to restrict the EV's acceleration to a safe level, minimizing battery energy consumption. In Ref. [8] a synthesized velocity profile prediction method is utilized to obtain driving velocity profiles. DP was then used to calculate optimal battery SOC trajectory and constraints at various set points [9]. These set points are then integrated into an MPC, which controls the maximum battery power output to track the optimal battery SOC at each set point. In Ref. [10], the road gradient was used in conjunction with an MPC, to generate a velocity profile for the vehicle. The MPC accelerated the vehicle when traveling up the road slope and decelerated the vehicle when traveling down the road slope. This was done prior to the occurrence of the road slope. Consequently, the power requirement from the battery was reduced. Furthermore, Zhao et al. [11] combined the wavelet neural network with the MPC to generate the reference SOC trajectory over a prediction horizon. This technique utilized particle swarm optimization to aid the wavelet neural network in generating the global SOC trajectory, which was used as a reference in the MPC. Furthermore, Chen et al. [12] adapted a long short-term memory velocity predictor. It gauged the vehicle's speed and power demand of the vehicle. Subsequently, an MPC strategically allocates load power between an ultra-capacitor and a battery through a DC-DC converter. This was carefully structured to guarantee that they operated at their highest efficiency and to minimize the overall power dissipation.

*Battery State of Charge Management for an Electric Vehicle Traction System DOI: http://dx.doi.org/10.5772/intechopen.113221*

Inspired by the techniques discussed in Refs. [6, 7], this chapter introduces a novel SOC tracking method capable of restricting the maximum change in SOC at the cost of speed-tracking performance degradation. The chapter's scope focuses on designing and testing this technique through simulation and excludes the method for obtaining the SOC reference trajectory. The test results suggest that the proposed SOC tracking method successfully regulates the SOC degradation, and maintains it at the desired SOC reference. The testing was performed on the New European Drive Cycle (NEDC), and the average of the magnitude of the deviation from the SOC reference was found to be 0.00095 for the proposed SOC tracking technique compared to the 0.0037 obtained by the A-ECMS and the 0.0019 obtained by the Fuzzy A-ECMS techniques [6].

This chapter comprises five sections, with the introduction as the first section. Section 2 introduces the SOC tracking technique. Section 3 describes the EV traction system components and controllers. Section 4 presents the simulation methodology and results, and finally, Section 5 concludes the chapter.

### **2. Description of the state of charge tracking technique**

Among the BEM strategies explored in Ref. [7], the fuzzy-tuned model predictive controller (FMPC) technique stands out as having broader potential applications within the EV traction system. It offers the possibility of fine-tuning the approach to achieve a similar outcome to the Fuzzy A-ECMS strategy detailed in Ref. [6], particularly in terms of SOC tracking. However, before delving into these adjustments, it is essential to grasp the fundamental workings of the FMPC technique and gain a comprehensive understanding of the overall system.

### **2.1 Fuzzy-tuned model predictive controller**

**Figure 1** illustrates a flowchart detailing the FMPC BEM technique, as discussed in Ref. [7]. The primary aim of this approach is to mitigate variations in the speed regulating current signal, denoted as *i* ∗ *sq*, to address abrupt accelerations. These rapid speed increases are reflected in sudden surges in battery bank current, leading to a rapid decline in battery bank SOC over a short period. This not only reduces the battery's runtime but also contributes to a shorter battery lifespan [7].

To counteract these issues, the technique monitors the battery bank current and estimates the SOC. The rate of change of SOC, denoted by the first derivative of SOC, is obtained by taking the difference between the current sample of SOC and the preceding SOC sample. Furthermore, the second derivative in SOC is obtained by taking the difference between the current and preceding sample for the change in SOC. Those variables are processed by the FLC and *GMPC* gain. The final result is the parameter *R* that impacts the MPC objective function. This parameter is used to penalize variations in *i* ∗ *sq*. Additionally, the technique incorporates motor speed and drive cycle information into the MPC block. The chosen drive cycles are the NEDC drive cycle, representing a smooth driving behavior, and the US06 drive cycle, representing an aggressive driving behavior [7]. These drive cycles provide a comprehensive assessment of the FMPC BEM technique's ability to regulate speed across a range of driving habits. The MPC, equipped with the estimated model of the EV traction system, solves the cost function, which has been adjusted with the input

**Figure 1.** *Flowchart for the FMPC BEM technique [7].*

weight *R*, using a receding horizon algorithm. The resulting *i* <sup>∗</sup> *sq* signal effectively regulates motor speed while suppressing abrupt acceleration patterns. This design assists in preventing abrupt surges in battery current during acceleration and enables a smoother transition to the steady-state value of the battery bank's discharge current.

### **2.2 Proposed modification for the state of charge tracking**

For the SOC tracking to be effective, it is crucial for the input weight *R* to adapt based on the error between the battery bank SOC and the reference SOC. **Figure 2** presents the flowchart outlining the SOC tracking approach. This method utilizes an MPC for speed regulation, while an FLC assesses the difference between the SOC reference trajectory and the actual battery SOC. Subsequently, it generates an input weight *R* that constrains the MPC's speed regulating signal, *i* ∗ *sq*. This approach possesses the capability to tightly constrain the motor tracking performance to an extensive degree by closely adhering to the SOC reference trajectory. The effectiveness of this scheme was evaluated through simulation, with testing conducted using the NEDC drive cycle. Subsequent sections will detail the design of the EV traction system components and the Simulink model employed in this study.

*Battery State of Charge Management for an Electric Vehicle Traction System DOI: http://dx.doi.org/10.5772/intechopen.113221*

**Figure 2.** *Flowchart for the proposed SOC tracking technique.*
