In the context of global green transformation, the shift toward new energy and clean energy sources has accelerated worldwide. As a key enabler, lithium-ion battery packs are increasingly deployed in electric vehicles, marine vessels, and defense equipment due to their high voltage, large capacity, low self-discharge, and enhanced safety. However, when multiple cells are combined in series-parallel configurations to meet power demands, inconsistencies among individual cells emerge as a critical challenge. These inconsistencies worsen with cycling, severely impacting the pack’s usable capacity, reliability, and service life. To address this, I have designed an active balancing management system based on a bidirectional flyback transformer. This battery management system (BMS) aims to mitigate cell imbalances, maximize actual available capacity, and extend operational lifespan. The core innovation lies in a hierarchical “module–branch–pack” balancing control strategy, implemented via dedicated hardware and software. In this article, I will detail the system architecture, circuit design, control algorithms, and experimental validation, emphasizing the role of the battery management system in ensuring consistent performance.
The battery management system (BMS) is fundamental for monitoring and regulating lithium-ion battery packs. It typically includes functions such as voltage and temperature sensing, current measurement, state-of-charge (SOC) estimation, and balancing control. In my design, the BMS comprises a Battery Cluster Unit (BCU) and Cell Supervisory Circuits (CSCs), interconnected through a communication network. The BCU acts as the master controller, handling overall pack management, while CSCs monitor individual cells and execute balancing commands. This decentralized approach enhances scalability and fault tolerance. The target application is an underwater vehicle requiring a power battery pack with specifications: DC 110 V operating voltage, 520 Ah rated capacity, 60 kWh energy, and weight under 675 kg. Using lithium iron phosphate (LiFePO₄) cells rated at 3.2 V and 60 Ah each, the pack configuration is derived from series-parallel calculations. The total series count is determined by $$N_s = \frac{V_{\text{pack}}}{V_{\text{cell}}} = \frac{110}{3.1} \approx 35.48$$, and parallel count by $$N_p = \frac{C_{\text{pack}}}{C_{\text{cell}}} = \frac{520}{60} \approx 8.67$$. To balance safety and reliability, a “cell–unit–module–branch–pack” topology is adopted: 3 cells in parallel and 6 in series form a module, 6 modules in series constitute a branch, and 3 branches in parallel compose the full pack. This structure allows continued operation even if a branch fails, underscoring the BMS’s role in managing redundancy.
The hardware design of the battery management system is split into management and control boards. The management board, centered on an LPC2378 microcontroller, includes current sensing circuits, communication interfaces, data storage, and switch control. For current measurement, Hall-effect sensors are used due to the high currents (up to 150 A per branch). The output voltage $$V_{\text{out}}$$ from the sensor is linearly proportional to the current $$I_{\text{branch}}$$, given by $$V_{\text{out}} = k_I \cdot I_{\text{branch}} + V_{\text{offset}}$$, where $$k_I$$ is the sensitivity (e.g., 40 mV/A) and $$V_{\text{offset}}$$ is the zero-current output. This signal is conditioned and fed to the microcontroller’s ADC for digital conversion. The control board, also using LPC2378, interfaces with LTC6804 battery monitoring chips and LTC3300 bidirectional balancers. Each LTC6804 monitors up to 6 series-connected cells, with voltage detection range of 0–5 V and accuracy better than 5 mV. The cell voltage $$V_{\text{cell}}$$ is measured through a resistive divider, where $$V_{\text{measured}} = V_{\text{cell}} \cdot \frac{R_2}{R_1 + R_2}$$. The LTC6804 includes a multiplexer and ADC to digitize each channel, transmitting data via SPI to the microcontroller. Temperature is sensed using NTC thermistors placed on module surfaces, with resistance $$R_T$$ related to temperature $$T$$ by the Steinhart-Hart equation: $$\frac{1}{T} = A + B \ln(R_T) + C (\ln(R_T))^3$$. These parameters are critical for the BMS to prevent thermal runaway.
The balancing circuitry is pivotal in the battery management system. I employed LTC3300 chips, which are high-efficiency bidirectional multi-cell balancers, paired with flyback transformers to transfer energy between cells. Each LTC3300 handles 6 series cells, enabling synchronous flyback balancing with currents up to 10 A and efficiency reaching 92%. The operation relies on switching transformers that provide isolation and bidirectional power flow. During balancing, energy is transferred from a higher-voltage cell to a lower-voltage one via transformer action. The equivalent circuit can be modeled with a transformer turns ratio $$n:1$$, where primary inductance $$L_p$$ and secondary inductance $$L_s$$ satisfy $$L_s = n^2 L_p$$. The energy transfer per switching cycle is $$E = \frac{1}{2} L_p I_p^2$$, with $$I_p$$ being the peak primary current. The average balancing current $$I_{\text{bal}}$$ is approximated by $$I_{\text{bal}} = \frac{E \cdot f_{\text{sw}}}{V_{\text{cell}}}$$, where $$f_{\text{sw}}$$ is the switching frequency (set to 100 kHz). For a target of 5 A, transformer parameters are selected accordingly, with isolation voltage exceeding DC 1500 V. The BMS orchestrates these balancers based on cell voltage disparities, ensuring efficient charge redistribution.
Software design for the battery management system encompasses both CSC control software and BCU management software. The CSC software, running on control boards, performs real-time acquisition of cell voltages and temperatures, and executes balancing commands. It operates in a loop: read LTC6804 registers, compute voltage differences, and activate LTC3300 balancers if thresholds are exceeded. The BCU software, on the management board, aggregates data from all CSCs, estimates SOC, predicts remaining life, controls charge/discharge switches, and communicates via CAN bus with an upper-level monitor. Algorithms for SOC estimation use ampere-hour integration combined with open-circuit voltage (OCV) correction: $$\text{SOC}(t) = \text{SOC}_0 – \frac{1}{C_{\text{nom}}} \int_0^t \eta I(\tau) d\tau$$, where $$\eta$$ is Coulombic efficiency and $$C_{\text{nom}}$$ is nominal capacity. The BMS also implements fault diagnosis, such as detecting overvoltage ($$V_{\text{cell}} > 3.65 \text{ V}$$), undervoltage ($$V_{\text{cell}} < 2.75 \text{ V}$$), and overtemperature ($$T > 60^\circ \text{C}$$). Communication protocols ensure robust data exchange, with CRC checks for integrity. The software architecture is modular, allowing updates for different pack configurations, highlighting the adaptability of the BMS.
Balancing control in this battery management system follows a three-layer strategy: module, branch, and pack-level balancing. At the module level, each CSC calculates the average voltage of its 6 cells: $$\bar{V}_{\text{module}} = \frac{1}{6} \sum_{i=1}^6 V_i$$. If any cell’s deviation exceeds a threshold $$\Delta V_{\text{th1}} = 40 \text{ mV}$$, the LTC3300 initiates balancing to transfer charge to or from that cell. The decision logic uses: $$\text{Balance if } |V_i – \bar{V}_{\text{module}}| > \Delta V_{\text{th1}}$$. At the branch level, the BCU computes the average module voltage per branch: $$\bar{V}_{\text{branch}} = \frac{1}{6} \sum_{j=1}^6 \bar{V}_{\text{module},j}$$. Modules with voltages differing by more than $$\Delta V_{\text{th2}} = 100 \text{ mV}$$ from $$\bar{V}_{\text{branch}}$$ are balanced via inter-module energy transfer using the flyback transformers. This is governed by: $$\text{Balance if } |\bar{V}_{\text{module},j} – \bar{V}_{\text{branch}}| > \Delta V_{\text{th2}}$$. For pack-level balancing, when branch capacities diverge significantly, the BMS adjusts discharge rates: during low-power operation, branches with lower SOC are halted to allow others to catch up. The SOC difference $$\Delta \text{SOC}$$ is estimated from voltage and current integration, and balancing is triggered if $$\Delta \text{SOC} > 5\%$$. This hierarchical approach minimizes energy loss and maximizes balancing speed.
Equilibrium strategies in the BMS focus on voltage-based and SOC-based balancing. Voltage balancing is straightforward but effective during charge/discharge phases. The control law selects a dynamic equilibrium point $$V_{\text{eq}}$$, which can be the average cell voltage or the median. During charging, cells with higher voltages are discharged to $$V_{\text{eq}}$$; during discharging, cells with lower voltages are charged. The energy transferred $$\Delta E$$ between two cells with voltage difference $$\Delta V$$ is $$\Delta E = C_{\text{cell}} \cdot \Delta V \cdot V_{\text{eq}}$$, where $$C_{\text{cell}}$$ is cell capacity. SOC balancing is more accurate but requires precise estimation. I use a combined method where SOC is derived from OCV-SOC curves, approximated by a polynomial: $$\text{SOC} = a_0 + a_1 V_{\text{oc}} + a_2 V_{\text{oc}}^2 + a_3 V_{\text{oc}}^3$$. The BMS then balances to equalize SOC values. The balancing point can be adaptive: for instance, if a cell’s SOC deviates by more than 10% from the mean, it becomes the target. The efficiency of balancing $$\eta_{\text{bal}}$$ is defined as $$\eta_{\text{bal}} = \frac{\text{Energy transferred to low cell}}{\text{Energy taken from high cell}} \times 100\%$$, typically above 90% in this design. Tables below summarize key parameters and balancing thresholds used in the BMS.
| Parameter | Symbol | Value | Unit |
|---|---|---|---|
| Cell nominal voltage | $$V_{\text{cell}}$$ | 3.2 | V |
| Cell nominal capacity | $$C_{\text{cell}}$$ | 60 | Ah |
| Pack voltage | $$V_{\text{pack}}$$ | 110 | V |
| Pack capacity | $$C_{\text{pack}}$$ | 520 | Ah |
| Module configuration | — | 3P6S | — |
| Branch configuration | — | 6 modules in series | — |
| Pack configuration | — | 3 branches in parallel | — |
| Voltage detection accuracy | — | <5 | mV |
| Balancing current max | $$I_{\text{bal,max}}$$ | 5 | A |
| Balancing efficiency | $$\eta_{\text{bal}}$$ | 92 | % |
| Module balance threshold | $$\Delta V_{\text{th1}}$$ | 40 | mV |
| Branch balance threshold | $$\Delta V_{\text{th2}}$$ | 100 | mV |
| SOC balance threshold | $$\Delta \text{SOC}_{\text{th}}$$ | 5 | % |
To validate the battery management system, I conducted extensive testing on a prototype pack. The first experiment assessed the balancing strategy by intentionally unbalancing one cell module to 90% SOC while others were fully charged. The pack underwent charge-discharge cycles at 0.25C rate (130 A), with balancing activated when voltage difference exceeded 40 mV. Over nine cycles, the SOC of the unbalanced module converged to the pack average, demonstrating the BMS’s effectiveness. The convergence can be modeled as an exponential decay: $$\Delta \text{SOC}(t) = \Delta \text{SOC}_0 \cdot e^{-t/\tau}$$, where $$\tau$$ is the time constant dependent on balancing current and cell capacity. Data from these cycles are summarized in the table below, showing increased charging capacity and stabilized cutoff voltages.
| Cycle Number | Charging Capacity (Ah) | Minimum Cell Cutoff Voltage (V) | Notes |
|---|---|---|---|
| 1 | 568.0 | 3.41 | Initial imbalance |
| 2 | 573.5 | 3.42 | Balancing active |
| 3 | 578.0 | 3.45 | Improvement seen |
| 4 | 582.0 | 3.48 | Voltage rising |
| 5 | 585.0 | 3.52 | Near convergence |
| 6 | 587.0 | 3.56 | Balanced state |
| 8 | 588.5 | 3.60 | Stable |
| 9 | 588.8 | 3.61 | Fully balanced |
A second experiment compared packs with and without the active balancing BMS over five charge-discharge cycles. The pack without balancing showed deteriorating consistency: during charging, the minimum cell cutoff voltage decreased cycle-by-cycle, and during discharging, the maximum cell cutoff voltage increased. This reduced usable capacity. In contrast, the BMS-equipped pack maintained stable cutoff voltages near theoretical limits, enhancing capacity. The discharge capacity $$C_{\text{discharge}}$$ for each cycle is given by $$C_{\text{discharge}} = \int I_{\text{discharge}} dt$$, measured until any cell hits voltage limits. The results are tabulated below, highlighting the BMS’s impact.
| Cycle | With BMS: Discharge Capacity (Ah) | Without BMS: Discharge Capacity (Ah) | With BMS: Max Cell Discharge Voltage (V) | Without BMS: Max Cell Discharge Voltage (V) |
|---|---|---|---|---|
| 1 | 530.20 | 525.00 | 2.55 | 2.62 |
| 2 | 530.10 | 524.30 | 2.57 | 2.62 |
| 3 | 529.80 | 524.40 | 2.56 | 2.65 |
| 4 | 529.80 | 524.00 | 2.55 | 2.68 |
| 5 | 529.60 | 524.10 | 2.55 | 2.73 |
The improvements are quantifiable. The capacity utilization ratio $$\gamma$$ is defined as $$\gamma = \frac{C_{\text{actual}}}{C_{\text{nominal}}} \times 100\%$$. For the BMS pack, $$\gamma$$ averaged 101.8% based on 530 Ah discharge versus 520 Ah nominal, whereas the non-BMS pack averaged 100.8%, indicating better utilization. Moreover, the voltage spread $$\sigma_V$$ across cells, calculated as $$\sigma_V = \sqrt{\frac{1}{N} \sum_{i=1}^N (V_i – \bar{V})^2}$$, decreased from over 50 mV to under 10 mV with balancing. This directly prolongs cycle life, as per empirical models like the Arrhenius equation for degradation: $$L = L_0 \cdot e^{-k \cdot \sigma_V}$$, where $$L$$ is lifetime and $$k$$ a constant. The BMS thus not only boosts capacity but also extends service life.
In designing the battery management system, I also considered efficiency metrics. The overall BMS power consumption $$P_{\text{BMS}}$$ includes losses from sensing, microcontroller operation, and balancing circuits. It can be expressed as $$P_{\text{BMS}} = P_{\text{MCU}} + P_{\text{sense}} + P_{\text{bal}}$$, where $$P_{\text{bal}} = (1 – \eta_{\text{bal}}) \cdot P_{\text{transfer}}$$. For typical operation, $$P_{\text{BMS}}$$ is below 5 W, negligible compared to pack power of kilowatts. The balancing speed is another figure of merit: time to balance a given SOC difference $$\Delta \text{SOC}$$ is $$t_{\text{bal}} = \frac{C_{\text{cell}} \cdot \Delta \text{SOC}}{I_{\text{bal}}}$$. With $$I_{\text{bal}} = 5 \text{ A}$$ and $$C_{\text{cell}} = 60 \text{ Ah}$$, balancing 10% SOC takes about 1.2 hours, suitable for overnight charging. The BMS software includes adaptive thresholds to optimize this: if voltage differences are small, balancing is delayed to reduce switching losses. This intelligence is key to a practical battery management system.
Future enhancements for this battery management system could integrate machine learning for predictive balancing. By analyzing historical voltage and temperature data, the BMS could forecast imbalances and preemptively initiate balancing, reducing stress on cells. Additionally, wireless communication between modules could simplify wiring in large packs. However, the current design already meets rigorous requirements for marine and vehicular applications. The hierarchical balancing, backed by robust hardware, ensures that the pack operates near its theoretical maximum, providing reliable power for critical missions.
In conclusion, the active balancing management system I designed effectively addresses inconsistencies in lithium-ion battery packs. Through a combination of bidirectional flyback transformers, LTC6804/LTC3300 chips, and layered control software, the BMS achieves high-efficiency energy transfer between cells. Experimental results confirm significant improvements in voltage uniformity, usable capacity, and cycle life. This battery management system is scalable and adaptable, making it suitable for electric vehicles, renewable energy storage, and defense equipment. As the world transitions to greener energy, advanced BMS technologies like this will play a pivotal role in maximizing battery performance and sustainability.