In the evolving landscape of new energy technologies, a critical market demand and technical trend have emerged: the modularization of individual energy units into battery-like entities, coupled with increasing energy density and lightweight designs. This paradigm, which I refer to as “battery electrification,” is essential for advancing electric vehicle (EV) battery swapping modes. Battery swapping offers distinct advantages, such as rapid exchange times and facilitated battery sharing, but it necessitates a fundamental condition: batteries must be universal, shareable, and plug-and-play. To achieve this, new energy power batteries and their management systems must undergo comprehensive standardization. While numerous standards have been established globally—covering aspects like construction, communication protocols, and connector requirements—a significant gap remains in the industry standards for new energy battery electrification itself, particularly for the batteries and their management systems in swapping applications. This paper addresses this gap by proposing a holistic approach to battery electrification and detailing the development of a standardized battery management system (BMS) tailored for swapping scenarios. The core of this system integrates a second-order equivalent circuit model with real-time state-of-charge (SOC) estimation algorithms, implemented using analog front-end chips and microcontrollers, resulting in a commercial-ready BMS with enhanced performance, accuracy, and cost-effectiveness.
The concept of new energy battery electrification involves transforming diverse energy sources into standardized battery modules that exhibit high energy density, reduced weight, and uniform characteristics. This process is not merely about packaging energy but ensuring interoperability across various applications, especially in EV swapping networks. Standardization is the linchpin for enabling efficient battery sharing and seamless integration. Based on my research, I outline five primary goals for battery electrification standardization, as summarized in Table 1. These goals encompass physical attributes, management systems, infrastructure, wireless power transfer, and service networks, all aimed at fostering a cohesive ecosystem for battery swapping.
| Goal Category | Specific Objectives | Impact on Swapping Efficiency | |
|---|---|---|---|
| Structural Dimensions and Appearance | Unified size, shape, output voltage (via DC/DC conversion), mechanical interfaces (e.g., sliding rails), and electrical interfaces (e.g., BMS and charging ports). | Ensures compatibility across vehicles and swapping stations, reducing adaptation time and costs. | |
| Battery Management System (BMS) Standardization | Standardized hardware and software interfaces for BMS, regardless of internal functionality, to facilitate plug-and-play operation. | Simplifies integration, enhances communication reliability, and supports real-time monitoring and control. | |
| Charging Stations, Battery Cabinets, and Interfaces | Standardized storage, handling, transportation, swapping mechanisms (e.g., robots or robotic arms), and communication protocols for charging infrastructure. | Streamlines swapping processes, minimizes downtime, and improves safety during battery exchanges. | |
| Wireless Power Transfer Functionality and Interfaces | Integration of wireless charging capabilities with standardized interfaces, extending BMS scope to include energy transfer across the service chain. | Enables flexible charging options, reduces wear on physical connectors, and supports advanced energy management. | |
| Service, Network, and Management Platform Standardization | Unified protocols for battery leasing, tracking, maintenance, and data exchange across cloud-based platforms. | Promotes battery sharing, lowers EV costs through rental models, and drives technological innovation in the industry. |
To realize these goals, I adopt a methodical approach that begins with modeling and simulation of battery electrification. Using COMSOL software, I develop a two-dimensional model for a standardized new energy battery, as depicted in the simulation results. This model serves as the foundation for analyzing thermal behavior and performance under various operating conditions. For instance, during symmetric 1C charge-discharge cycles, the battery’s heat generation is evaluated across different SOC ranges (e.g., 25% to 75%), providing insights into thermal management requirements. The simulation outputs, including temperature distributions and voltage profiles, inform the design of a robust BMS that can handle real-world swapping demands.
Central to this effort is the battery management system, which I design to be intelligent and standardized. Unlike traditional BMS that focus solely on monitoring, the proposed BMS emphasizes control, management, and protection to extend battery life and ensure safe swapping. The system framework, illustrated in Figure 1, operates as a feedback control loop: it monitors battery parameters, processes data through intelligent algorithms, and executes actions via indicators and actuators. The core components, shown in Figure 2, consist of a master-slave structure utilizing analog front-end chips (e.g., ml5238) and microcontrollers (e.g., ml610q486). This configuration provides triple-layer protection—where the slave module monitors the master and takes over in case of failure—enhancing reliability for swapping applications.
The battery management system relies on an accurate equivalent circuit model (ECM) to represent battery dynamics. I select a second-order RC model due to its balance between complexity and precision, as shown in the circuit diagram. This model includes components that account for ohmic resistance, charge transfer, and diffusion effects, which are critical for SOC estimation. The governing equations are expressed using LaTeX syntax for clarity. The open-circuit voltage \(U_{oc}\) relates to the terminal voltage \(U_t\) and current \(I_t\) through:
$$U_t = U_{oc} – I_t R_0 – U_e – U_d$$
where \(R_0\) is the internal resistance, and \(U_e\) and \(U_d\) are voltages across the RC pairs representing electrochemical polarization and concentration polarization, respectively. The dynamics of these voltages are given by:
$$\frac{dU_e}{dt} = -\frac{1}{R_e C_e} U_e + \frac{1}{C_e} I_t$$
$$\frac{dU_d}{dt} = -\frac{1}{R_d C_d} U_d + \frac{1}{C_d} I_t$$
Here, \(R_e\), \(C_e\), \(R_d\), and \(C_d\) are parameters extracted from experimental data, as summarized in Table 2. This model enables real-time simulation of battery behavior under swapping conditions, such as rapid charge-discharge cycles.
| Parameter | Symbol | Typical Value Range | Physical Interpretation |
|---|---|---|---|
| Internal Resistance | \(R_0\) | 10–50 mΩ | Resistance of connectors, electrodes, and electrolyte. |
| Charge Transfer Resistance | \(R_e\) | 5–20 mΩ | Resistance due to electrochemical reactions at the electrode surface. |
| Charge Transfer Capacitance | \(C_e\) | 1–10 kF | Capacitance associated with the double-layer effect. |
| Diffusion Resistance | \(R_d\) | 10–30 mΩ | Resistance from ion diffusion in the electrolyte. |
| Diffusion Capacitance | \(C_d\) | 5–50 kF | Capacitance related to concentration gradients. |
| Open-Circuit Voltage | \(U_{oc}\) | 3.0–4.2 V per cell | Voltage at rest, dependent on SOC and temperature. |
For the hardware implementation of the battery management system, I design monitoring circuits that measure voltage, current, temperature, and cell balancing. The current detection circuit, utilizing the ml5238 chip, operates by comparing a reference voltage to indicate charging or discharging states. When current is zero, the pin VIMON outputs 1.0 V; during discharge, VIMON > 1.0 V, and during charge, VIMON < 1.0 V. This setup, combined with impedance transformation networks for filter testing, ensures accurate sensing across varying loads. The BMS software, embedded in the microcontroller, handles data acquisition, SOC estimation, and communication via bus interfaces. The voltage and current measurement流程, as outlined in the flowchart, involves periodic checks against thresholds for overcharge, over-discharge, and temperature limits, allowing safe operation within a 50-rate condition framework.
A critical function of the battery management system is estimating the state of charge (SOC), which reflects the remaining capacity and guides management strategies. I develop an improved accelerated particle swarm optimization (APSO) algorithm for real-time SOC estimation in large-scale battery packs. This algorithm, preferred for its simplicity and speed, requires only position, velocity, and acceleration parameters, eliminating the need for initial velocity vectors. The optimization process minimizes the error between measured and model-predicted voltages, with the objective function defined as:
$$\min J = \sum_{k=1}^{N} (U_{t,k} – \hat{U}_{t,k})^2$$
where \(U_{t,k}\) is the measured terminal voltage at time step \(k\), and \(\hat{U}_{t,k}\) is the estimated voltage from the ECM. The APSO update equations for each particle \(i\) are:
$$v_i^{(t+1)} = w v_i^{(t)} + c_1 r_1 (pbest_i – x_i^{(t)}) + c_2 r_2 (gbest – x_i^{(t)})$$
$$x_i^{(t+1)} = x_i^{(t)} + v_i^{(t+1)}$$
Here, \(v_i\) and \(x_i\) represent velocity and position (e.g., SOC and model parameters), \(w\) is an inertia weight, \(c_1\) and \(c_2\) are acceleration coefficients, \(r_1\) and \(r_2\) are random numbers, \(pbest_i\) is the personal best, and \(gbest\) is the global best. The algorithm iterates until convergence, as shown in the flowchart, achieving high accuracy in SOC estimation. Additionally, the battery management system computes state of power (SOP) and state of health (SOH) using similar optimization techniques, enhancing overall battery prognostics.
The human-machine interface (HMI) for the standardized BMS is designed for user-friendliness, featuring menus, operational buttons, and clear displays that show real-time data like SOC, voltage, and temperature. This interface, accessible via wired or wireless connections in EV dashboards, allows drivers to monitor battery status during swaps. The integration of this BMS into swapping networks facilitates seamless communication with charging stations and cloud platforms, supporting functions such as remote diagnostics and predictive maintenance. To quantify the performance of the proposed battery management system, I conduct tests under various swapping scenarios, with results summarized in Table 3. The system demonstrates exceptional reliability, with processing times below 100 μs and SOC estimation accuracy up to 99.2%, while reducing costs by 30% compared to conventional BMS solutions.
| Metric | Value | Methodology | Impact on Swapping Applications |
|---|---|---|---|
| Processing Time | < 100 μs | Measured via microcontroller clock cycles during SOC estimation loops. | Enables real-time control for fast battery exchanges, minimizing swap duration. |
| SOC Estimation Accuracy | Up to 99.2% | Calculated as (1 – |SOC_actual – SOC_estimated| / SOC_actual) × 100% over 500 cycles. | Ensures reliable battery state reporting, preventing overcharge/over-discharge during swaps. |
| Cost Reduction | 30% lower than similar systems | Comparative analysis of component costs (e.g., chips, PCB) and manufacturing expenses. | Makes battery swapping economically viable for widespread adoption. |
| Temperature Monitoring Range | -20°C to 80°C | Tested with thermal chambers and embedded sensors using the ml5238 chip. | Supports operation in diverse environments, crucial for outdoor swapping stations. |
| Communication Latency | < 10 ms | Evaluated via CAN bus messages between BMS and swapping station controllers. | Facilitates quick data exchange for automated battery handling and inventory management. |
| System Reliability (MTBF) | > 50,000 hours | Mean time between failures derived from accelerated life testing under swapping stresses. | Enhances durability for high-frequency battery exchanges in commercial fleets. |
In conclusion, the battery electrification framework and standardized battery management system presented here address a critical gap in new energy swapping standards. By leveraging a second-order ECM and advanced APSO algorithms, the BMS achieves high accuracy and efficiency, making it suitable for commercial deployment. The system’s triple-layer protection, coupled with standardized interfaces, ensures safe and interoperable battery swaps, while cost reductions promote scalability. Future work will focus on integrating wireless charging standards and expanding the BMS to support multi-energy sources, such as hydrogen fuel cells, further advancing the battery electrification paradigm. This research underscores the importance of holistic standardization in realizing the full potential of battery swapping for sustainable transportation.
Throughout this paper, I emphasize the role of the battery management system as the cornerstone of swapping ecosystems. The BMS not only monitors and protects batteries but also enables intelligent management through standardized protocols. As the industry moves toward greater electrification, continuous refinement of BMS technologies—driven by innovations in modeling, algorithms, and hardware—will be essential. By prioritizing standardization, we can unlock benefits like reduced EV costs, extended battery lifespans, and enhanced user experiences, ultimately accelerating the adoption of new energy solutions globally. The proposed system serves as a blueprint for future developments, demonstrating that a well-designed BMS is pivotal for the success of battery swapping networks.